Cloud computing is a technology that was developed a decade ago to provide uninterrupted, scalable services to users and organizations. Cloud computing has also become an attractive feature for mobile users due to the limited features of mobile devices. The combination of cloud technologies with mobile technologies resulted in a new area of computing called mobile cloud computing. This combined technology is used to augment the resources existing in Smart devices. In recent times, Fog computing, Edge computing, and Clone Cloud computing techniques have become the latest trends after mobile cloud computing, which have all been developed to address the limitations in cloud computing. This paper reviews these recent technologies in detail and provides a comparative study of them. It also addresses the differences in these technologies and how each of them is effective for organizations and developers.
A review on orchestration distributed systems for IoT smart services in fog c...IJECEIAES
This paper provides a review of orchestration distributed systems for IoT smart services in fog computing. The cloud infrastructure alone cannot handle the flow of information with the abundance of data, devices and interactions. Thus, fog computing becomes a new paradigm to overcome the problem. One of the first challenges was to build the orchestration systems to activate the clouds and to execute tasks throughout the whole system that has to be considered to the situation in the large scale of geographical distance, heterogeneity and low latency to support the limitation of cloud computing. Some problems exist for orchestration distributed in fog computing are to fulfil with high reliability and low-delay requirements in the IoT applications system and to form a larger computer network like a fog network, at different geographic sites. This paper reviewed approximately 68 articles on orchestration distributed system for fog computing. The result shows the orchestration distribute system and some of the evaluation criteria for fog computing that have been compared in terms of Borg, Kubernetes, Swarm, Mesos, Aurora, heterogeneity, QoS management, scalability, mobility, federation, and interoperability. The significance of this study is to support the researcher in developing orchestration distributed systems for IoT smart services in fog computing focus on IR4.0 national agenda.
Advancement in computing facilities marks back from 1960’s with introduction of mainframes. Each of the computing has one or the other issues, so keeping this in mind cloud computing was introduced. Cloud computing has its roots in older technologies such as hardware virtualization, distributed computing, internet technologies, and autonomic computing. Cloud computing can be described with two models, one is service model and second is deployment model. While providing several services, cloud management’s primary role is resource provisioning. While there are several such benefits of cloud computing, there are challenges in adopting public clouds because of dependency on infrastructure that is shared by many enterprises. In this paper, we present core knowledge of cloud computing, highlighting its key concepts, deployment models, service models, benefits as well as security issues related to cloud data. The aim of this paper is to provide a better understanding of the cloud computing and to identify important research directions in this field
In this study, we propose situations where cloud is suitable and fog is more compatible, also define some services according to the cloud and fog architecture. We also provide a comparison of task scheduling algorithms of cloud computing and determine that fog is a light weight network so which is the best suitable algorithm for fog architecture on the basis of some attributes. The implementations of fog computing are challenging in today’s computational era; we define some reasons in which fog computing implementation is difficult.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Emerging cloud computing paradigm vision, research challenges and development...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Security and Privacy Issues of Fog Computing: A SurveyHarshitParkar6677
Abstract. Fog computing is a promising computing paradigm that ex-
tends cloud computing to the edge of networks. Similar to cloud comput-
ing but with distinct characteristics, fog computing faces new security
and privacy challenges besides those inherited from cloud computing. In
this paper, we have surveyed these challenges and corresponding solu-
tions in a brief manner.
A review on orchestration distributed systems for IoT smart services in fog c...IJECEIAES
This paper provides a review of orchestration distributed systems for IoT smart services in fog computing. The cloud infrastructure alone cannot handle the flow of information with the abundance of data, devices and interactions. Thus, fog computing becomes a new paradigm to overcome the problem. One of the first challenges was to build the orchestration systems to activate the clouds and to execute tasks throughout the whole system that has to be considered to the situation in the large scale of geographical distance, heterogeneity and low latency to support the limitation of cloud computing. Some problems exist for orchestration distributed in fog computing are to fulfil with high reliability and low-delay requirements in the IoT applications system and to form a larger computer network like a fog network, at different geographic sites. This paper reviewed approximately 68 articles on orchestration distributed system for fog computing. The result shows the orchestration distribute system and some of the evaluation criteria for fog computing that have been compared in terms of Borg, Kubernetes, Swarm, Mesos, Aurora, heterogeneity, QoS management, scalability, mobility, federation, and interoperability. The significance of this study is to support the researcher in developing orchestration distributed systems for IoT smart services in fog computing focus on IR4.0 national agenda.
Advancement in computing facilities marks back from 1960’s with introduction of mainframes. Each of the computing has one or the other issues, so keeping this in mind cloud computing was introduced. Cloud computing has its roots in older technologies such as hardware virtualization, distributed computing, internet technologies, and autonomic computing. Cloud computing can be described with two models, one is service model and second is deployment model. While providing several services, cloud management’s primary role is resource provisioning. While there are several such benefits of cloud computing, there are challenges in adopting public clouds because of dependency on infrastructure that is shared by many enterprises. In this paper, we present core knowledge of cloud computing, highlighting its key concepts, deployment models, service models, benefits as well as security issues related to cloud data. The aim of this paper is to provide a better understanding of the cloud computing and to identify important research directions in this field
In this study, we propose situations where cloud is suitable and fog is more compatible, also define some services according to the cloud and fog architecture. We also provide a comparison of task scheduling algorithms of cloud computing and determine that fog is a light weight network so which is the best suitable algorithm for fog architecture on the basis of some attributes. The implementations of fog computing are challenging in today’s computational era; we define some reasons in which fog computing implementation is difficult.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Emerging cloud computing paradigm vision, research challenges and development...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Security and Privacy Issues of Fog Computing: A SurveyHarshitParkar6677
Abstract. Fog computing is a promising computing paradigm that ex-
tends cloud computing to the edge of networks. Similar to cloud comput-
ing but with distinct characteristics, fog computing faces new security
and privacy challenges besides those inherited from cloud computing. In
this paper, we have surveyed these challenges and corresponding solu-
tions in a brief manner.
Implementing K-Out-Of-N Computing For Fault Tolerant Processing In Mobile and...IJERA Editor
Despite the advances in hardware for hand-held mobile devices, resource-intensive applications (e.g., video and imagestorage and processing or map-reduce type) still remain off bounds since they require large computation and storage capabilities.Recent research has attempted to address these issues by employing remote servers, such as clouds and peer mobile devices.For mobile devices deployed in dynamic networks (i.e., with frequent topology changes because of node failure/unavailability andmobility as in a mobile cloud), however, challenges of reliability and energy efficiency remain largely unaddressed. To the best of ourknowledge, we are the first to address these challenges in an integrated manner for both data storage and processing in mobilecloud, an approach we call k-out-of-n computing. In our solution, mobile devices successfully retrieve or process data, in the mostenergy-efficient way, as long as k out of n remote servers are accessible. Through a real system implementation we prove the feasibilityof our approach. Extensive simulations demonstrate the fault tolerance and energy efficiency performance of our framework in largerscale networks.
According to a new Gartner report1, “Around 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2022, Gartner predicts this
figure will reach 75%”. In addition to hosting new 5G era services, the other major network operator driver for edge compute and edge clouds is deploying virtualized network infrastructure, replacing many dedicated hardware-based elements with virtual network functions (VNFs) running on general purpose edge compute. Even portions of access networks are being virtualized, and many of these functions need to be deployed close to end users. The combination of these infrastructure and applications drivers is a major reason that so much of 5G era network transformation resolves around edge cloud distribution.
A Comparative Study: Taxonomy of High Performance Computing (HPC) IJECEIAES
The computer technologies have rapidly developed in both software and hardware field. The complexity of software is increasing as per the market demand because the manual systems are going to become automation as well as the cost of hardware is decreasing. High Performance Computing (HPC) is very demanding technology and an attractive area of computing due to huge data processing in many applications of computing. The paper focus upon different applications of HPC and the types of HPC such as Cluster Computing, Grid Computing and Cloud Computing. It also studies, different classifications and applications of above types of HPC. All these types of HPC are demanding area of computer science. This paper also done comparative study of grid, cloud and cluster computing based on benefits, drawbacks, key areas of research, characterstics, issues and challenges.
A Cloud-based Online Access on Smart Energy Metering in the PhilippinesIJAEMSJORNAL
This qualitative study was conducted to identify how smart energy metering could utilize the energy consumption remotely through cloud-computing and the utilization of the data management, and data visualization of a cloud-computing. Participants are working directly in smart energy metering and cloud-computing. Results revealed that most participants believed that smart energy metering can be controlled by cloud-computing and smart energy metering offers a robust and reliable modem enabling efficient communication. Moreover, some participants believed that existing application can deliver accurate data analysis, management, operations, and customer engagement. Few participants were direct users of cloud-computing but in some utilizations particularly in high-rise buildings, malls, and manufacturing plants, they were using cloud-computing to monitor their power usages and the harmonics power supplies in their facilities. Participants believed that application can edit, collect, deliver data through the utility-grade of meter data management system and most of cloud-based has utility on data visualization from web-based energy portal and reporting tools for consumers and utilities. Some participants said thatthe system is usually compatible in the SAP system. More so, most participants believed, that the scalability, central data storage, cost-efficiency, real-time response, and securities are advantages of the system and the location of data, inefficient cloud security policy, mixing of data, term of agreement, compatibility, application programming interfaces, redundant data management and disaster recoveries are the challenges of the system.
Fog Computing: Issues, Challenges and Future Directions IJECEIAES
In Cloud Computing, all the processing of the data collected by the node is done in the central server. This involves a lot of time as data has to be transferred from the node to central server before the processing of data can be done in the server. Also it is not practical to stream terabytes of data from the node to the cloud and back. To overcome these disadvantages, an extension of cloud computing, known as fog computing, is introduced. In this, the processing of data is done completely in the node if the data does not require higher computing power and is done partially if the data requires high computing power, after which the data is transferred to the central server for the remaining computations. This greatly reduces the time involved in the process and is more efficient as the central server is not overloaded. Fog is quite useful in geographically dispersed areas where connectivity can be irregular. The ideal use case requires intelligence near the edge where ultralow latency is critical, and is promised by fog computing. The concepts of cloud computing and fog computing will be explored and their features will be contrasted to understand which is more efficient and better suited for realtime application.
Fog Computing – Enhancing the Maximum Energy Consumption of Data Servers.dbpublications
Fog Computing and IoT systems make use of end-user premises devices as local servers. Here, we are identifying the scenarios for which running applications from NDCs are more energy-efficient than running the same applications from MDC. With the complete survey and analysis of various energy consumption factors such as different flow-variants and time-variants with respect to the Network Equipment we found two energy consumption use cases and respective results. Parameters such as current Load, Pmax, Cmax, Incremental Energy etc evolved with respect to system structure and various data related parameters leading to the conclusion that the NDC utilizes relatively reduced factor of energy comparative to the MDC. The study reveals that NDC as a part of Fog makeweights the MDCs to accompany respective applications, especially in the scenarios where IoT based applications are used where end users are the source data providers and can maximize the server utilization.
A New Improved Storage Model of Wireless Devices using the CloudIJCNC
This paper focuses on the development of new storage model by using cloud computing for
mobile devises. The concept of cloud computing has been applied to mobile devices for improving the
existing model (battery time and data saving) of mobile devices. In the recent eras, different types of
cloud computing techniques have been used for improving the efficiency of mobile devices. The paper
has combined the calibration and current launch amount characteristics with the trial results for drop in
battery voltage. A mathematical equation has been derived for mote operation scenario. Through this
equation, the power provide by the power supply as well as the average time of battery can be measured.
Presented By Ashok.J 3 rd BCA - AVVM Sri Pushpam College, Poondi , Tanjor
Slide 2: GRID COMPUTING Conceptual View Of Grid Computing ?
What Is Grid Computing?: What Is Grid Computing? Grid computing is the collection of computer resources from multiple locations to reach a common goal. GRID COMPUTING
Slide 4: How Grid Computing Works? GRID COMPUTING
Slide 5: Types Of Grid Data Grid Collaboration Grid Network Grid Utility Grid GRID COMPUTING Computational Grid
Slide 6: Grid topologies
Slide 7: Intra grids A Typical intra grid topology exist within S ingle Organization, providing a basic set of grid Services
Slide 8: Extra grids An Extra grid, Typically involves more than one security provider , and the level Management complexity increases
Slide 9: Inter Grids An inter grid requires the dynamic integration of applications, resources and service with patterns, Customers access via WAN/ Internet
Slide 10: A Simple Grid GRID COMPUTING
Slide 11: Complex Inter grid GRID COMPUTING
Slide 12: Grid Scheduled An application is one or more jobs that are scheduled to run a Grid GRID COMPUTING
Slide 13: Advantages : Can solve larger, more complex problems in a shorter time Easier to collaborate with other organizations Make better use of existing hardware GRID COMPUTING
Slide 14: Disa dvantages : Grid software and standards are still evolving Learning curve to get started Non-interactive job submission GRID COMPUTING
Slide 15: BENEFITS OF GRID COMPUTING GRID COMPUTING Exploiting underutilized resources Parallel CPU capacity Virtual organizations for collaboration and virtual resources Access to additional resources Resource balancing Reliability Management
Presented By Ashok.J ashokmannai0005@gmail.com
Swiftly increasing demand of computational
calculations in the process of business, transferring of files
under certain protocols and data centers force to develop an
emerging technology cater to the services for computational
need, highly manageable and secure storage. To fulfill these
technological desires cloud computing is the best answer by
introducing various sorts of service platforms in high
computational environment. Cloud computing is the most
recent paradigm promising to turn around the vision of
“computing utilities” into reality. The term “cloud
computing” is relatively new, there is no universal agreement
on this definition. In this paper, we go through with different
area of expertise of research and novelty in cloud computing
domain and its usefulness in the genre of management. Even
though the cloud computing provides many distinguished
features, it still has certain sorts of short comings amidst with
comparatively high cost for both private and public clouds. It
is the way of congregating amasses of information and
resources stored in personal computers and other gadgets
and further putting them on the public cloud for serving
users. Resource management in a cloud environment is a
hard problem, due to the scale of modern data centers, their
interdependencies along with the range of objectives of the
different actors in a cloud ecosystem. Cloud computing is
turning to be one of the most explosively expanding
technologies in the computing industry in this era. It
authorizes the users to transfer their data and computation to
remote location with minimal impact on system performance.
With the evolution of virtualization technology, cloud
computing has been emerged to be distributed systematically
or strategically on full basis. The idea of cloud computing has
not only restored the field of distributed systems but also
fundamentally changed how business utilizes computing
today. Resource management in cloud computing is in fact a
typical problem which is due to the scale of modern data
centers, the variety of resource types and their inter
dependencies, unpredictability of load along with the range of
objectives of the different actors in a cloud ecosystem.
Sustainability and fog computing applications, advantages and challengesAbdulMajidFarooqi
Designing a sustainable society is a key concern of the United Nations' 2030 Sustainable Development Goals. Sustainable fog computing is the most prominent solution for most problems occurring in cloud data centers, such as latency, security, carbon footprint, electricity consumption and so on. It is an extended design of cloud computing that supports horizontal computing paradigm providing cloud-like services at the edge of user premises. After emerging IoT fog computing has become the first choice of time sensitive applications due to its residing closer to the devices and sensors. In this paper we have introduced fog computing and differentiated it from cloud, furthermore, we have discussed how we can achieve sustainability through fog in several applications areas. Also, we have presented some existing challenges of fog paradigm. Moreover, we have reviewed some existing work about fog computing.
This presentation has been presented in the 3rd International Conference on Computing and Communication Technologies (ICCCT’19), Chennai, India
For the full paper please visit: https://ieeexplore.ieee.org/document/8824983
Grid computing or network computing is developed to make the available electric power in the similar way
as it is available for the grid. For that we just plug in the power and whoever needs power, may use it. In
grid computing if a system needs more power than available it can share the computing with other
machines connected in a grid. In this way we can use the power of a super computer without a huge cost
and the CPU cycles that were wasted previously can also be utilized. For performing grid computation in
joined computers through the internet, the software must be installed which supports grid computation on
each computer inside the VO. The software handles information queries, storage management, processing
scheduling, authentication and data encryption to ensure information security.
CONTAINERIZED SERVICES ORCHESTRATION FOR EDGE COMPUTING IN SOFTWARE-DEFINED W...IJCNCJournal
As SD-WAN disrupts legacy WAN technologies and becomes the preferred WAN technology adopted by corporations, and Kubernetes becomes the de-facto container orchestration tool, the opportunities for deploying edge-computing containerized applications running over SD-WAN are vast. Service orchestration in SD-WAN has not been provided with enough attention, resulting in the lack of research focused on service discovery in these scenarios. In this article, an in-house service discovery solution that works alongside Kubernetes’ master node for allowing improved traffic handling and better user experience when running micro-services is developed. The service discovery solution was conceived following a design science research approach. Our research includes the implementation of a proof-ofconcept SD-WAN topology alongside a Kubernetes cluster that allows us to deploy custom services and delimit the necessary characteristics of our in-house solution. Also, the implementation's performance is tested based on the required times for updating the discovery solution according to service updates. Finally, some conclusions and modifications are pointed out based on the results, while also discussing possible enhancements.
In this paper we are study-ing about cloud computing, their types, need to use cloud computing. We also study the architecture of the mobile cloud computing. So we included new techniques for backup and restoring data from mobile to cloud. Here we proposed to apply some compres-sion technique while backup and restore data from Smartphone to cloud and cloud to the Smartphone.
Implementing K-Out-Of-N Computing For Fault Tolerant Processing In Mobile and...IJERA Editor
Despite the advances in hardware for hand-held mobile devices, resource-intensive applications (e.g., video and imagestorage and processing or map-reduce type) still remain off bounds since they require large computation and storage capabilities.Recent research has attempted to address these issues by employing remote servers, such as clouds and peer mobile devices.For mobile devices deployed in dynamic networks (i.e., with frequent topology changes because of node failure/unavailability andmobility as in a mobile cloud), however, challenges of reliability and energy efficiency remain largely unaddressed. To the best of ourknowledge, we are the first to address these challenges in an integrated manner for both data storage and processing in mobilecloud, an approach we call k-out-of-n computing. In our solution, mobile devices successfully retrieve or process data, in the mostenergy-efficient way, as long as k out of n remote servers are accessible. Through a real system implementation we prove the feasibilityof our approach. Extensive simulations demonstrate the fault tolerance and energy efficiency performance of our framework in largerscale networks.
According to a new Gartner report1, “Around 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2022, Gartner predicts this
figure will reach 75%”. In addition to hosting new 5G era services, the other major network operator driver for edge compute and edge clouds is deploying virtualized network infrastructure, replacing many dedicated hardware-based elements with virtual network functions (VNFs) running on general purpose edge compute. Even portions of access networks are being virtualized, and many of these functions need to be deployed close to end users. The combination of these infrastructure and applications drivers is a major reason that so much of 5G era network transformation resolves around edge cloud distribution.
A Comparative Study: Taxonomy of High Performance Computing (HPC) IJECEIAES
The computer technologies have rapidly developed in both software and hardware field. The complexity of software is increasing as per the market demand because the manual systems are going to become automation as well as the cost of hardware is decreasing. High Performance Computing (HPC) is very demanding technology and an attractive area of computing due to huge data processing in many applications of computing. The paper focus upon different applications of HPC and the types of HPC such as Cluster Computing, Grid Computing and Cloud Computing. It also studies, different classifications and applications of above types of HPC. All these types of HPC are demanding area of computer science. This paper also done comparative study of grid, cloud and cluster computing based on benefits, drawbacks, key areas of research, characterstics, issues and challenges.
A Cloud-based Online Access on Smart Energy Metering in the PhilippinesIJAEMSJORNAL
This qualitative study was conducted to identify how smart energy metering could utilize the energy consumption remotely through cloud-computing and the utilization of the data management, and data visualization of a cloud-computing. Participants are working directly in smart energy metering and cloud-computing. Results revealed that most participants believed that smart energy metering can be controlled by cloud-computing and smart energy metering offers a robust and reliable modem enabling efficient communication. Moreover, some participants believed that existing application can deliver accurate data analysis, management, operations, and customer engagement. Few participants were direct users of cloud-computing but in some utilizations particularly in high-rise buildings, malls, and manufacturing plants, they were using cloud-computing to monitor their power usages and the harmonics power supplies in their facilities. Participants believed that application can edit, collect, deliver data through the utility-grade of meter data management system and most of cloud-based has utility on data visualization from web-based energy portal and reporting tools for consumers and utilities. Some participants said thatthe system is usually compatible in the SAP system. More so, most participants believed, that the scalability, central data storage, cost-efficiency, real-time response, and securities are advantages of the system and the location of data, inefficient cloud security policy, mixing of data, term of agreement, compatibility, application programming interfaces, redundant data management and disaster recoveries are the challenges of the system.
Fog Computing: Issues, Challenges and Future Directions IJECEIAES
In Cloud Computing, all the processing of the data collected by the node is done in the central server. This involves a lot of time as data has to be transferred from the node to central server before the processing of data can be done in the server. Also it is not practical to stream terabytes of data from the node to the cloud and back. To overcome these disadvantages, an extension of cloud computing, known as fog computing, is introduced. In this, the processing of data is done completely in the node if the data does not require higher computing power and is done partially if the data requires high computing power, after which the data is transferred to the central server for the remaining computations. This greatly reduces the time involved in the process and is more efficient as the central server is not overloaded. Fog is quite useful in geographically dispersed areas where connectivity can be irregular. The ideal use case requires intelligence near the edge where ultralow latency is critical, and is promised by fog computing. The concepts of cloud computing and fog computing will be explored and their features will be contrasted to understand which is more efficient and better suited for realtime application.
Fog Computing – Enhancing the Maximum Energy Consumption of Data Servers.dbpublications
Fog Computing and IoT systems make use of end-user premises devices as local servers. Here, we are identifying the scenarios for which running applications from NDCs are more energy-efficient than running the same applications from MDC. With the complete survey and analysis of various energy consumption factors such as different flow-variants and time-variants with respect to the Network Equipment we found two energy consumption use cases and respective results. Parameters such as current Load, Pmax, Cmax, Incremental Energy etc evolved with respect to system structure and various data related parameters leading to the conclusion that the NDC utilizes relatively reduced factor of energy comparative to the MDC. The study reveals that NDC as a part of Fog makeweights the MDCs to accompany respective applications, especially in the scenarios where IoT based applications are used where end users are the source data providers and can maximize the server utilization.
A New Improved Storage Model of Wireless Devices using the CloudIJCNC
This paper focuses on the development of new storage model by using cloud computing for
mobile devises. The concept of cloud computing has been applied to mobile devices for improving the
existing model (battery time and data saving) of mobile devices. In the recent eras, different types of
cloud computing techniques have been used for improving the efficiency of mobile devices. The paper
has combined the calibration and current launch amount characteristics with the trial results for drop in
battery voltage. A mathematical equation has been derived for mote operation scenario. Through this
equation, the power provide by the power supply as well as the average time of battery can be measured.
Presented By Ashok.J 3 rd BCA - AVVM Sri Pushpam College, Poondi , Tanjor
Slide 2: GRID COMPUTING Conceptual View Of Grid Computing ?
What Is Grid Computing?: What Is Grid Computing? Grid computing is the collection of computer resources from multiple locations to reach a common goal. GRID COMPUTING
Slide 4: How Grid Computing Works? GRID COMPUTING
Slide 5: Types Of Grid Data Grid Collaboration Grid Network Grid Utility Grid GRID COMPUTING Computational Grid
Slide 6: Grid topologies
Slide 7: Intra grids A Typical intra grid topology exist within S ingle Organization, providing a basic set of grid Services
Slide 8: Extra grids An Extra grid, Typically involves more than one security provider , and the level Management complexity increases
Slide 9: Inter Grids An inter grid requires the dynamic integration of applications, resources and service with patterns, Customers access via WAN/ Internet
Slide 10: A Simple Grid GRID COMPUTING
Slide 11: Complex Inter grid GRID COMPUTING
Slide 12: Grid Scheduled An application is one or more jobs that are scheduled to run a Grid GRID COMPUTING
Slide 13: Advantages : Can solve larger, more complex problems in a shorter time Easier to collaborate with other organizations Make better use of existing hardware GRID COMPUTING
Slide 14: Disa dvantages : Grid software and standards are still evolving Learning curve to get started Non-interactive job submission GRID COMPUTING
Slide 15: BENEFITS OF GRID COMPUTING GRID COMPUTING Exploiting underutilized resources Parallel CPU capacity Virtual organizations for collaboration and virtual resources Access to additional resources Resource balancing Reliability Management
Presented By Ashok.J ashokmannai0005@gmail.com
Swiftly increasing demand of computational
calculations in the process of business, transferring of files
under certain protocols and data centers force to develop an
emerging technology cater to the services for computational
need, highly manageable and secure storage. To fulfill these
technological desires cloud computing is the best answer by
introducing various sorts of service platforms in high
computational environment. Cloud computing is the most
recent paradigm promising to turn around the vision of
“computing utilities” into reality. The term “cloud
computing” is relatively new, there is no universal agreement
on this definition. In this paper, we go through with different
area of expertise of research and novelty in cloud computing
domain and its usefulness in the genre of management. Even
though the cloud computing provides many distinguished
features, it still has certain sorts of short comings amidst with
comparatively high cost for both private and public clouds. It
is the way of congregating amasses of information and
resources stored in personal computers and other gadgets
and further putting them on the public cloud for serving
users. Resource management in a cloud environment is a
hard problem, due to the scale of modern data centers, their
interdependencies along with the range of objectives of the
different actors in a cloud ecosystem. Cloud computing is
turning to be one of the most explosively expanding
technologies in the computing industry in this era. It
authorizes the users to transfer their data and computation to
remote location with minimal impact on system performance.
With the evolution of virtualization technology, cloud
computing has been emerged to be distributed systematically
or strategically on full basis. The idea of cloud computing has
not only restored the field of distributed systems but also
fundamentally changed how business utilizes computing
today. Resource management in cloud computing is in fact a
typical problem which is due to the scale of modern data
centers, the variety of resource types and their inter
dependencies, unpredictability of load along with the range of
objectives of the different actors in a cloud ecosystem.
Sustainability and fog computing applications, advantages and challengesAbdulMajidFarooqi
Designing a sustainable society is a key concern of the United Nations' 2030 Sustainable Development Goals. Sustainable fog computing is the most prominent solution for most problems occurring in cloud data centers, such as latency, security, carbon footprint, electricity consumption and so on. It is an extended design of cloud computing that supports horizontal computing paradigm providing cloud-like services at the edge of user premises. After emerging IoT fog computing has become the first choice of time sensitive applications due to its residing closer to the devices and sensors. In this paper we have introduced fog computing and differentiated it from cloud, furthermore, we have discussed how we can achieve sustainability through fog in several applications areas. Also, we have presented some existing challenges of fog paradigm. Moreover, we have reviewed some existing work about fog computing.
This presentation has been presented in the 3rd International Conference on Computing and Communication Technologies (ICCCT’19), Chennai, India
For the full paper please visit: https://ieeexplore.ieee.org/document/8824983
Grid computing or network computing is developed to make the available electric power in the similar way
as it is available for the grid. For that we just plug in the power and whoever needs power, may use it. In
grid computing if a system needs more power than available it can share the computing with other
machines connected in a grid. In this way we can use the power of a super computer without a huge cost
and the CPU cycles that were wasted previously can also be utilized. For performing grid computation in
joined computers through the internet, the software must be installed which supports grid computation on
each computer inside the VO. The software handles information queries, storage management, processing
scheduling, authentication and data encryption to ensure information security.
CONTAINERIZED SERVICES ORCHESTRATION FOR EDGE COMPUTING IN SOFTWARE-DEFINED W...IJCNCJournal
As SD-WAN disrupts legacy WAN technologies and becomes the preferred WAN technology adopted by corporations, and Kubernetes becomes the de-facto container orchestration tool, the opportunities for deploying edge-computing containerized applications running over SD-WAN are vast. Service orchestration in SD-WAN has not been provided with enough attention, resulting in the lack of research focused on service discovery in these scenarios. In this article, an in-house service discovery solution that works alongside Kubernetes’ master node for allowing improved traffic handling and better user experience when running micro-services is developed. The service discovery solution was conceived following a design science research approach. Our research includes the implementation of a proof-ofconcept SD-WAN topology alongside a Kubernetes cluster that allows us to deploy custom services and delimit the necessary characteristics of our in-house solution. Also, the implementation's performance is tested based on the required times for updating the discovery solution according to service updates. Finally, some conclusions and modifications are pointed out based on the results, while also discussing possible enhancements.
In this paper we are study-ing about cloud computing, their types, need to use cloud computing. We also study the architecture of the mobile cloud computing. So we included new techniques for backup and restoring data from mobile to cloud. Here we proposed to apply some compres-sion technique while backup and restore data from Smartphone to cloud and cloud to the Smartphone.
Contemporary Energy Optimization for Mobile and Cloud Environmentijceronline
Cloud and mobile computing applications are increasing heavily in terms of usage. These two areas extending usability of systems. This review paper gives information about cloud and mobile applications in terms of resources they consume and the need of choosing variety of features for users from several locations and the evolutionary provisions for service provider and end users. Both the fields are combined to provide good functionality, efficiency and effectiveness with mobile phones. The enhancement by considering power consumption by means of resource constrained nature of devices, communication media and cost effectiveness. This paper discuss about the concepts related to power consumption, underlying protocols and the other performance issues
Cooperative hierarchical based edge-computing approach for resources allocati...IJECEIAES
Using mobile and Internet of Things (IoT) applications is becoming very popular and obtained researchers’ interest and commercial investment, in order to fulfill future vision and the requirements for smart cities. These applications have common demands such as fast response, distributed nature, and awareness of service location. However, these requirements’ nature cannot be satisfied by central systems services that reside in the clouds. Therefore, edge computing paradigm has emerged to satisfy such demands, by providing an extension for cloud resources at the network edge, and consequently, they become closer to end-user devices. In this paper, exploiting edge resources is studied; therefore, a cooperative-hierarchical approach for executing the pre-partitioned applications’ modules between edges resources is proposed, in order to reduce traffic between the network core and the cloud, where this proposed approach has a polynomial-time complexity. Furthermore, edge computing increases the efficiency of providing services, and improves end-user experience. To validate our proposed cooperative-hierarchical approach for modules placement between edge nodes’ resources, iFogSim toolkit is used. The obtained simulation results show that the proposed approach reduces network’s load and the total delay compared to a baseline approach for modules’ placement, moreover, it increases the network’s overall throughput.
Cloud computing security through symmetric cipher modelijcsit
Cloud computing can be defined as an application and services which runs on distributed network using
virtualized and it is accessed through internet protocols and networking. Cloud computing resources and
virtual and limitless and information’s of the physical systems on which software running are abstracted
from the user. Cloud Computing is a style of computing in which dynamically scalable and often virtualized
resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or
control over the technology infrastructure in the "cloud" that supports them. To satisfy the needs of the
users the concept is to incorporate technologies which have the common theme of reliance on the internet
Software and data are stored on the servers whereas cloud computing services are provided through
applications online which can be accessed from web browsers. Lack of security and access control is the
major drawback in the cloud computing as the users deal with sensitive data to public clouds .Multiple
virtual machine in cloud can access insecure information flows as service provider; therefore to implement
the cloud it is necessary to build security. Therefore the main aim of this paper is to provide cloud
computing security through symmetric cipher model. This article proposes symmetric cipher model in
order to implement cloud computing security so that data can accessed and stored securely.
ABSTRACT
In today’s world, the swift increase of utilizing mobile services and simultaneously discovering of the cloud computing services, made the Mobile Cloud Computing (MCC) selected as a wide spread technology among mobile users. Thus, the MCC incorporates the cloud computing with mobile services for achieving facilities in daily using mobile. The capability of mobile devices is limited of computation context, memory capacity, storage ability, and energy. Thus, relying on cloud computing can handle these troubles in the mobile surroundings. Cloud Computing gives computing easiness and capacity such provides availability of services from anyplace through the Internet without putting resources into new foundation, preparing, or application authorizing. Additionally, Cloud Computing is an approach to expand the limitations or increasing the abilities dynamically. The primary favourable position of Cloud Computing is that clients just use what they require and pay for what they truly utilize. Mobile cloud computing is a form for various services, where a mobile gadget is able to utilize the cloud for data saving, seeking, information mining, and multimedia preparing. Cloud computing innovation is also causes many new complications in side of safety and gets to direct when users store significant information with cloud servers. As the clients never again have physical ownership of the outsourced information, makes the information trustworthiness, security, and authenticity insurance in Cloud Computing is extremely difficult and conceivably troublesome undertaking. In MCC environments, it is hard to find a paper embracing most of the concepts and issues such as: architecture, computational offloading, challenges, security issues, authentications and so on. In this paper we discuss these concepts with presenting a review of the most recent papers in the domain of MCC.
PROCEDURE OF EFFECTIVE USE OF CLOUDLETS IN WIRELESS METROPOLITAN AREA NETWORK...IJCNCJournal
The article develops a method to ensure the efficient use of cloudlet resources by the mobile users. The article provides a solution to the problem of correct use of cloudlets located on the movement route of mobile users in Wireless Metropolitan Area Networks - WMAN environment. Conditions for downloading
necessary applications to the appropriate cloudlet using the possible values that determine the importance and coordinates of the cloudlets were studied. The article provides a model of the mobile user's route model in metropolitan environments and suggests a method for solving the problem.
Efficient architectural framework of cloud computing Souvik Pal
Cloud computing is that enables adaptive, favorable and on-demand network access to a collective pool of adjustable and configurable computing physical resources which networks, servers, bandwidth, storage that can be swiftly provisioned and released with negligible supervision endeavor or service provider interaction. From business prospective, the viable achievements of Cloud Computing and recent developments in Grid computing have brought the platform that has introduced virtualization technology into the era of high performance computing. However, clouds are Internet-based concept and try to disguise complexity overhead for end users. Cloud service providers (CSPs) use many structural designs combined with self-service capabilities and ready-to-use facilities for computing resources, which are enabled through network infrastructure especially the internet which is an important consideration. This paper provides an efficient architectural Framework for cloud computing that may lead to better performance and faster access.
Opportunistic job sharing for mobile cloud computingijccsa
Cloud Computing is the evolution of new business era which is covered with many of technologies.These
technology are taking advantage of economies of scale and multi tenancy which are used to decrees the
cost of information technology resources. Many of the organization are eager to reduce their computing
cost through the means of virtualization. This demand of reducing the computing cost and time has led to
the innovation of Cloud Computing. Itenhanced computing through improved deployment and
infrastructure costs and processing time. Mobile computing & its applications in smart phones enable a
new, rich user experience. Due to extreme usage of limited resources in smart phones it create problems
which are battery problems, memory space and CPU. To solve this problem, we propose a dynamic mobile
cloud computing architecture framework to use global resources instead of local resources. In this
proposed framework the usefulness of job sharing workload at runtime reduces the load at the local client
and the dynamic throughput time of the job through Wi-Fi Connectivity.
All the details of Fog Computing is discussed in this PPT, its better to get knowledge about this ppt,All the details of applications and examples are covered..
Secured Communication Model for Mobile Cloud Computingijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
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developments in cloud computing are fog and edge computing. It is necessary to make a comparison of these
techniques to understand the advantages of using edge, fog, and clone cloud in different systems.
Cloud-based applications use a central server to process data, which increases the communication
between user devices. Hence, there is a need for looking beyond the cloud at the edge of cloud networks. The
aim is to explore possibilities of performing computations at edge nodes. Edge Cloud augments traditional
data centers consisting of cloud models, with service nodes placed at the network edges[4][5]. The proximity
of edge nodes, allows data processing to and from remote clouds to be done at the edge. Computing on edge
nodes closer to end-users can be exploited as a platform for application providers to improve their services.
Similarly, clone clouds provide a distributed mechanism of application execution, in contrast to
edge cloud. It automatically transforms mobile applications to benefit from the cloud [6]. This application is
a flexible run-time execution partitioner, which transfers execution onto device clones running on the cloud.
A clone allows for the dynamic execution of various applications by alternating between the clone and the
device.
In the same way, fog computing gives the user the option of performing cloud operations at
locations closer to his or her point of interest. Fog computing is similar in many ways to cloud and edge
computing. The huge influx of real-time data, and the need for processing the same, gave rise to the
terminology of fog computing [7]. Fog nodes are heterogeneous devices, ranging from high-end servers,
access points, set-top boxes, edge routers, etc., to end devices, such as mobile phones, smart watches, and
sensors. It uses existing networks and routers in nearby locations to perform operations just like the cloud [8].
Fog computing also has a better response to the Internet of Things environment and is suitable for real-time
service requests.
The goal of this study is to understand the differences between these technologies by performing a
comparative study of each of these approaches. Some of the application areas where they are useful are also
discussed.The next section provides a survey on edge, fog, and the components of their builds that make
them feasible for implementation. This study includes a comparative study on edge computing and fog
computing, a discussion on the elastic execution mechanisms using clone cloud computing, and an inspection
of off-loading mechanisms used in clones and the cost-benefit analysis to off-load. Additionally, a summary
and comparative study on fog, edge, and clone cloud mechanisms is provided.
2. SURVEY ON EDGE COMPUTING AND FOG COMPUTING
2.1. Edge Computing Review
It is known that cloud computing and mobile computing are used together to harness mobile back-
ends to augment the resources for Smart devices. However, they suffer from lag, network resilience, and link
failures. Edge computing is a terminology that is used to augment traditional data centres with service nodes
at network edges. Mobile Edge Computing (MEC) [9] allows the use of cloud and IT services within close
proximity of mobile subscribers, thus reducing the end-to-end latency. It is based on a decentralized model
that interconnects a heterogeneous cloud and it is based on the following elements:
a. Proximity: The possibility to use nodes that are nearer rather than farther away.
b. Intelligence: Use of autonomous decision making to help in the miniaturization of systems.
c. Control: Management and coordination comes from edge machines that assign or delegate
computation [10].
Hence, based on these overall elements, edge computing delivers low-latency, bandwidth-efficiency,
and resilient end-user services. Using this service, users receive latency benefits from those who are away
from the data centres. Edge computing provides a traditional data centre with extended capabilities to deploy
applications at the edge networks[11]. The proximity of users and low latency are significant advantages in
times of network congestion. These features allow the mobile network operators, vendors, as well as
application service providers, to improve existing services using edge computing, enabling significant value
addition to the respective business models. Figure 1 shows edge centre architecture through LAN and WAN
networks connected to the cloud data centre.
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Figure 1. Edge centre architecture in LAN/WAN network
2.1.1. Model of an Edge System
Edge computing supports primitive virtualization instead of hypervisor-based hardware
virtualization. In the paper by Satyanarayanan [12], edge computing is discussed as a new paradigm in which
substantial computing and storage resources are done at micro data centres placed at the Internet’s edge in
close proximity to mobile devices or sensors. The WiCloud architecture by Hongxing [13] consists of a
layered architecture that includes a physical layer, virtual layer, and a function and service layer. The paper
by Chang et al. [14] gives a general model of an edge cloud that can work with all types of edge services and
also IoT platforms.
2.1.2. Applications Based on Edge Computing
The Follow Me Edge (FME) architecture is an edge service architecture proposed by
Dutta et al. [15], where the service continuously follows the user to the closest edge. Migration is done to
ensure that no data is lost. To realize the FME architecture, the edge operator needs to keep updated
information about resources and user locations. The SLA consists of an integrated architecture of edge
operators, a shared storage concept, and service migration, which are enforced in the FME architecture. A
framework for mobile edge computing to support diverse applications in a Smart city scenario, by reducing
core network traffic through Smart MEC, is the overall idea. Another open sensor platform developed on the
basis of edge computing is proposed in Waggle, a wireless program that uses sensors to measure air borne
pollutants. It is an OpenStack-based, edge platform that consists of a node controller that manages a sensor
data cache, reads simple sensor values, and manages the network stack and encryption.
The edge computing for the sensor platform addresses resilience, performance, isolation, and data
privacy. Nebula [10] is a dispersed cloud infrastructure that uses edge. It was developed by the University of
Minnesota to support distributed data-intensive computing and for efficient movement and availability of
large quantities of data to compute resources. It acts as a decentralized cloud, working at the edge of the
network and helping with the devices lacking resources. Femto Cloud, discussed in work by Ammar et. Al.
[16] are a collection of co-located devices used to provide a cloud service at the edge. The Femto Cloud
provides a dynamic, self-configuring, and multi-device mobile cloud out of a cluster of mobile devices. The
architecture is designed to enable multiple mobile devices to be configured into a coordinated cloud
computing service. From the study on edge computing and its applications, it is seen that migration of
resources has provided application-aware provisioning in edge-based applications.
2.2. Fog Computing Review
Fog computing was first introduced by Cisco Systems, Inc., for wireless data transfer between
distributeddevices in an IoT network paradigm. Fog computing, or fog, is an architecture that uses one or
more collaborative end-user clients or near-user edge devices to carry out storage, communication, control,
configuration, measurement, and management. Many research papers consider fog and edge computing
complementary to one another. Similar to edge computing, fog computing is carried out closer to the end
user’s networks. It is also a virtualized platform located between end users and cloud data centres hosted on
the Internet. It enables computing at the edge of the network[17]. Edge routers are advertised for processor
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speed, number of cores, and built-in network storage, and are used as fog servers. There is also a cellular base
station, and a Wi-Fi access point or femto cell router as a server. In fog computing, facilities or
infrastructures that can provide resources for services at the edge of the network are called “fog nodes.”
Figure 2 demonstrates a three-layer user/ fog/cloud network. Fog computing provides advantages in terms of
reduction in delay, power consumption, and reduces data traffic over the network.
Figure 2. Three-layer user/fog/cloud
2.2.1. Fog Computing Areas and Applications
Fog computing is used as open architectures for developing Smart living environments with
potentially thousands of vendors. The Open Fog Consortium [18] is a global system in collaboration with
ARM, Cisco, Dell, Intel, Microsoft, etc., to accelerate the adoption of fog computing and to build a common
reference architecture covering hardware and software platforms and highly sophisticated capabilities. The
fog network connects every component of the fog. Emerging techniques, such as software-defined
networking (SDN) and network function virtualization (NFV), are proposed to create flexible and easy to
maintain network environments.
The employment of SDN and NFV can ease the implementation and management, increase network
scalability, and reduce costs in many aspects of fog computing, such as resource allocation, VM migration,
traffic monitoring, application-aware control, and programmable interfaces[19]. Augmented reality and real-
time video analytics are one of the areas supported by fog computing, which can maximize throughput and
reduce latency in both processing and transmission for high stream video streaming processes. The Internet
of Things and Connected Smart devices have given way to fog computing principles, which has been used in
various domains, such as Vehicular networks, Body Area Networks (BAN), and the Smart Grid. Fog
computing allows for greater support and better response time to the Internet of things environment, it is
suitable for real-time service requests, and it shares resources efficiently [8].
2.3. Comparison between Fog and Edge Computing
Cloudlet is a resource-rich computer like “cloud in a box,” which is available for use by nearby
mobile devices. Bahl et. al. [20] built Cloudlet, ahead of fog computing, but now agree with the concept of
fog computing. In fog computing, infrastructures are provided as resources for services at the edge of the
network. They are called fog nodes, which are similar to cloudlets. They can be resource-poor devices, such
as set-top-boxes, access points, routers, switches, base stations, and end devices, or resource-rich machines
such as Cloudlet and IOx. This study reviews power consumption by cloud and fog resources.
Mathematically, it has been proven that by using fog resources, power consumption is minimum in different
applications. As fog computing is a relatively new concept in cloud computing, the presence of secure sand-
boxes for the implementation of fog applications brings about new challenges in terms of trust and privacy.
Fog are a type of mini-clouds in the network for increasing resource availability, thus by doing so, isolation
and sandboxing mechanisms must be in place to ensure bidirectional trust among cooperating parties.
Standardization mechanisms on the network should take place at the terminal and node end of fog networks.
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There is also a lack of central entity controlling the fog; hence, it is difficult to assert if a given device is
indeed hosting a component.
At the same time, there are many open-ended issues in edge computing where application
deployment strategies, edge node security, and failure recovery, are some of the issues that need further
research. As edge is in close proximity to end-users, it enables reduction of latency. It also provides radio
network capability, including access to network information and integration with operator network services.
These are some of the advantages of using edge computing. Table 1 presents a comparative study of fog and
edge cloud computing. In the next section a different perspective of distributed cloud computing, using
elastic execution mechanisms between devices and a cloud, is discussed. Further, a comparison on clone
cloud, fog, and edge computing is presented.
Table 1. Comparison between Fog and Edge Computing
Fog Computing Edge Computing
1. Deployed at the local premises of mobile users. Deployed as a traditional data centre with extended capabilities.
2.
Virtualized device with build-in data storage,
computing, and communication facility.
Uses an edge server similar to a traditional data centre server.
3. Can be adapted from existing system components. It is completely built as new system or a mini cloud data centre.
4.
Energy consumption of fog is less than cloud
services, but overhead is high compared to cloud.
Edge uses less resources than the cloud and initial overhead to
build is high compared to cloud
5. No central entity controlling the fog cloud.
Edge server using cloud technologies and virtualization used to
control edge components.
6.
May not be controlled by network operators, uses an
ad-hoc distribution.
Allows the mobile network operators to improve existing services
with edge.
3. CLONE CLOUD: ELASTIC EXECUTION MECHANISM IN THE CLOUD
The advantages of cloud computing are the services offered by cloud providers, i.e. Software-as-a-
Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS). To address the inherent
problem of resource constraint in mobile devices, the concept of off-loading data and computation to cloud
service providers is used. Examples, such as crowd sourcing, image processing, use of GPS, and Internet data
happen outside the device using mobile cloud computing. Augmented Smartphone applications, with clone
cloud, address challenges in ways to off-load execution to the cloud infrastructure. Clone cloud technology
was introduced by Chun [21] for off-loading execution from the Smartphone to a computational
infrastructure hosting in the cloud of Smartphone clones. The idea was proposed on the simple concept that
allows Smartphones to host its expensive and exotic applications. The novelty of the approach is that replicas
are loosely synchronized and virtualized on emulated devices.
The framework is a cloud-based, fine-grained, thread-level, application partitioner, which clones the
entire mobile platform during runtime execution into the cloud Virtual Machine and runs the mobile
application inside the Virtual Machine, without performing any change in the application code. This
approach also replicates the whole Smartphone image, with few or no modifications, into powerful VM
replicas, thus transforming a single machine computation into a distributed computation semi-automatically.
The aim of off-loading is also to minimize the communication and execution cost between the mobile device
and its surrogates. Figure 3 shows a replica of a clone cloud-based architecture with a thread to distribute the
process execution.
Figure 3. Clone cloud-based architecture
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3.1. Related Study on Clone Clouds
A study by Chang [22] performed a cost analysis by using different clone cloud resource allocation
strategies. The paper uses computer-intensive java applications for elastic execution mechanisms in the
cloud, and they are compared for remote processing speed, cost, and energy savings, along-side the
unmodified application. It also proved that comparisons of remote and local executions on netbooks and
laptops were reduced from 5% to 50%, as compared to fog and edge computing elastic executions, which
required reduced amounts of cloud services. Infrastructure development that is needed in fog and edge
computing is not required in clone clouds. Elastic executions offer more flexibility to developers and end-
users to choose cloud services at their preference. The advantages of using a clone cloud is that they late-bind
the decision to off-load executions to the cloud infrastructure, thus giving more autonomy to end-users.
Many frameworks are developed for off-loading computations on clone clouds, such as the one by
Bal [23], which provides a system to off-load mobile device applications. The application generates code
from the original application as a remote service, identical to the local one, where the remote versions are run
on a multi-core computer instance, and takes full advantage of parallelism. If remote resources are not
available (such as network connectivity), then the application can run on local resources entirely. The clone
cloud uses Virtual Machine migration to off-load part of their application workload to a resource-rich server
through either 3G or Wi-Fi.
The cost model analyses the cost of execution of the application on the device and the migration
cost. Virtual Migration reports provide an energy saving from 60% to 90% for different types of applications,
such as chess, games, and face recognition systems. There are also many other elastic platforms for code off-
load, such as a system level clone and delegated surrogates on the cloud. In all these mechanisms, the most
important requirement is the availability of network connectivity. The main idea of using a clone cloud is fast
execution and minimum cost of sending data to the cloud, thus significantly reducing the execution cost on
the device. There are methods to achieve this through analysis of constraints required to partition the
application and clone it in the cloud.
Before off-loading, a cost-benefit analysis should be done to evaluate the benefits of off-loading and
the potential gain, by evaluating the predicted cost of execution with user-specific requirements. The cost
analysis is determined by using a profiler to keep track of devices energy used, network characteristics, and
application characteristics. Based on the information from the profiler, the application decides whether to off-
load the application. Some applications do allow an optimizer to decide which methods are to be migrated so
that the cost of migration and execution is minimized [24]. Monitory cost is also one of the aspects to be
considered while migrating applications to the cloud. Clone cloud approaches have various ways of using
cloud resources to enhance the computing capabilities of mobile devices. It uses migration and re-integration
methods to split modules of application between cloud and clone. Therefore, the overall performance and
credibility of these augmentation approaches is highly dependent on the cloud-based resource characteristics.
Performance, availability, elasticity, vulnerability to security attacks, reliability, cost, and distance are major
characteristics to be considered by cloud service providers when augmenting[25]. Elastic execution strategies
use different approaches compared to fog and edge computing. The next section summarizes these different
cloud methodologies.
4. EVALUATION OF DIFFERENT CLOUD EXECUTION MODELS
From the studies presented, it is seen that all of these technology providers provide different types of
cloud services. The final benefits are for the end-user to use these services according to each user’s
requirements. Edge computing and fog computing are setup to limit the latency between cloud data centres
and end devices. All data centres are connected by Internet connections between users and cloud services,
which are long, thin, and susceptible to network failures. Internet access is provided by ISPs in different
regions. Urban areas have good access to cellular towers, because of the density of population and hence,
Internet connectivity is also available in these areas. However, there are reduced numbers of towers in rural
areas, therefore Internet access and access to cloud services is minimum. Thus, it can be argued that good
access to cellular towers gives good Internet connectivity, which in turn provides good access to cloud
providers. From a business perspective, the more data and computation obtained is of advantage, as data is
charged per minute to consumers. Hence, limited and adequate use of the Internet is beneficial to all. Clone
cloud-based, distributed execution strategies provide effective utilization of cloud resources from the
consumer’s perspective. Remote areas are further benefited by fog and edge computing due to limited
connectivity issues.
Thus, network proximity offers a number of advantages in terms of reduced number of hops, fast
responding cloud services, etc., in the cloud[26]. In urban areas, network proximity is good because of good
connectivity, while the counter is true in rural areas. Unlike the free surrogate resources, utilizing cloud
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infrastructure levies financial charges to the end-users. Mobile users pay for consumed infrastructure
resources according to the SLAs negotiated with cloud vendors. In certain scenarios, users prefer local
execution or application termination because of the monetary cost of cloud infrastructures. However, user
payment is of advantage to cloud vendors so that they can maintain their services and deliver reliable, robust,
and secure services to the mobile users[27]. Thus, it can be concluded that in cities and industrial hubs, clone
cloud-based distributed cloud execution is beneficial, while small towns and villages should take advantages
of edge and fog computing. Table 2 provides the comparative study of all of the different cloud execution
mechanisms.
Table 2. Comparison between Fog, Edge, and Clone Cloud Models
5. CONCLUSION
This paper provides an overview of the recent developments in the area of cloud computing and
mobile cloud computing. The paper focuses on the recent concepts of fog, edge, and clone-based
computational off-loading services. Fog computing and edge computing are the latest in the development of
cloud services, by bringing the cloud services closer to the end-user thus, reducing time of execution on
cloud servers. These technologies are compared with existing technologies of clone cloud-based
computational off-loading mechanisms. Clone clouds are categorized into different types of augmentation
strategies, thus limiting the use of cloud resources. Altogether, these findings indicate there are advantages in
using clone cloud, fog, and edge computing-based approaches. It is evident from the study that areas of
utilization of cloud resources have an impact on services offered. The recent approaches of fog and edge
computing are suitable for users with limited connectivity issues, and clone clouds are suitable for optimal
use of cloud services.
ACKNOWLEDGEMENTS
I would like to thank all the unknown reviewers for the comments and suggestions.
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![International Journal of Electrical and Computer Engineering (IJECE)
Vol. 8, No. 6, December 2018, pp. 4646~4653
ISSN: 2088-8708, DOI: 10.11591/ijece.v8i6.pp4646-4653 4646
Journal homepage: http://iaescore.com/journals/index.php/IJECE
A Comparison of Cloud Execution Mechanisms Fog, Edge, and
Clone Cloud Computing
T. Francis
Department of Computer Engineering, BITS Pilani University, India
Article Info ABSTRACT
Article history:
Received Mar 27, 2018
Revised Jun 7, 2018
Accepted Sep 9, 2018
Cloud computing is a technology that was developed a decade ago to provide
uninterrupted, scalable services to users and organizations. Cloud computing
has also become an attractive feature for mobile users due to the limited
features of mobile devices. The combination of cloud technologies with
mobile technologies resulted in a new area of computing called mobile cloud
computing. This combined technology is used to augment the resources
existing in Smart devices. In recent times, Fog computing, Edge computing,
and Clone Cloud computing techniques have become the latest trends after
mobile cloud computing, which have all been developed to address the
limitations in cloud computing. This paper reviews these recent technologies
in detail and provides a comparative study of them. It also addresses the
differences in these technologies and how each of them is effective for
organizations and developers.
Keyword:
Clone cloud
Edge computing
Elastic executions
Fog computing
IoT
Mobile back-ends
Mobile cloud computing
Mobile off-loading mechanisms
Virtualization
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
T. Francis,
Department of Electrical and Computer Engineering,
Department of Computer Engineering,
BITS Pilani University, India.
Email: tinajibu@gmail.com
1. INTRODUCTION
Smart devices such as, Smart phones, tablets, PCs, Net-books, etc., are trending in the market today.
These devices are compact and small, which make them handy for use. Being small, they have fewer
resources than other systems. Cloud computing was used to address the drawbacks in these devices, such as
lack of storage, computational power, and the limited battery life of Smart devices[1]. Mobile cloud
computing is an interdisciplinary combination of cloud and mobile computing. Resource constraints in
mobiles gave rise to the use of the cloud in mobiles, which was later known as Mobile Cloud computing or
MCC [2]. It is also considered as an extension to cloud computing, but at the same time, it provides services
to Smart devices. There are a number of limitations in using mobile cloud computing, as cloud data centers
are often located at remote locations from the place of request.
This makes it more susceptible to suffer from lag, network resilience, and link failures, thus, the
objective of using mobile cloud computing fails because of these issues. Also the volume and speed of data
in applications such as healthcare, manufacturing and traffic management is high hence big data analytics
tools such as mahout, sparks etc are used.They process multiple data sources, distributed memory tasks and
range of algorithms however it is still inclined to cloud data centres who provide and support their
services [3]. Hence there is need to get the cloud closer to users. Edge, Fog, and Clone clouds try to address
these issues and are differentiated from each other in terms of usage and build techniques. The most recent](https://image.slidesharecdn.com/v6227mar187jun1812432-20148-2-ededitari-201126064552/85/A-Comparison-of-Cloud-Execution-Mechanisms-Fog-Edge-and-Clone-Cloud-Computing-1-320.jpg)
![Int J Elec & Comp Eng ISSN: 2088-8708
A Comparison of Cloud Execution Mechanisms Fog, Edge, and … (T. Francis)
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developments in cloud computing are fog and edge computing. It is necessary to make a comparison of these
techniques to understand the advantages of using edge, fog, and clone cloud in different systems.
Cloud-based applications use a central server to process data, which increases the communication
between user devices. Hence, there is a need for looking beyond the cloud at the edge of cloud networks. The
aim is to explore possibilities of performing computations at edge nodes. Edge Cloud augments traditional
data centers consisting of cloud models, with service nodes placed at the network edges[4][5]. The proximity
of edge nodes, allows data processing to and from remote clouds to be done at the edge. Computing on edge
nodes closer to end-users can be exploited as a platform for application providers to improve their services.
Similarly, clone clouds provide a distributed mechanism of application execution, in contrast to
edge cloud. It automatically transforms mobile applications to benefit from the cloud [6]. This application is
a flexible run-time execution partitioner, which transfers execution onto device clones running on the cloud.
A clone allows for the dynamic execution of various applications by alternating between the clone and the
device.
In the same way, fog computing gives the user the option of performing cloud operations at
locations closer to his or her point of interest. Fog computing is similar in many ways to cloud and edge
computing. The huge influx of real-time data, and the need for processing the same, gave rise to the
terminology of fog computing [7]. Fog nodes are heterogeneous devices, ranging from high-end servers,
access points, set-top boxes, edge routers, etc., to end devices, such as mobile phones, smart watches, and
sensors. It uses existing networks and routers in nearby locations to perform operations just like the cloud [8].
Fog computing also has a better response to the Internet of Things environment and is suitable for real-time
service requests.
The goal of this study is to understand the differences between these technologies by performing a
comparative study of each of these approaches. Some of the application areas where they are useful are also
discussed.The next section provides a survey on edge, fog, and the components of their builds that make
them feasible for implementation. This study includes a comparative study on edge computing and fog
computing, a discussion on the elastic execution mechanisms using clone cloud computing, and an inspection
of off-loading mechanisms used in clones and the cost-benefit analysis to off-load. Additionally, a summary
and comparative study on fog, edge, and clone cloud mechanisms is provided.
2. SURVEY ON EDGE COMPUTING AND FOG COMPUTING
2.1. Edge Computing Review
It is known that cloud computing and mobile computing are used together to harness mobile back-
ends to augment the resources for Smart devices. However, they suffer from lag, network resilience, and link
failures. Edge computing is a terminology that is used to augment traditional data centres with service nodes
at network edges. Mobile Edge Computing (MEC) [9] allows the use of cloud and IT services within close
proximity of mobile subscribers, thus reducing the end-to-end latency. It is based on a decentralized model
that interconnects a heterogeneous cloud and it is based on the following elements:
a. Proximity: The possibility to use nodes that are nearer rather than farther away.
b. Intelligence: Use of autonomous decision making to help in the miniaturization of systems.
c. Control: Management and coordination comes from edge machines that assign or delegate
computation [10].
Hence, based on these overall elements, edge computing delivers low-latency, bandwidth-efficiency,
and resilient end-user services. Using this service, users receive latency benefits from those who are away
from the data centres. Edge computing provides a traditional data centre with extended capabilities to deploy
applications at the edge networks[11]. The proximity of users and low latency are significant advantages in
times of network congestion. These features allow the mobile network operators, vendors, as well as
application service providers, to improve existing services using edge computing, enabling significant value
addition to the respective business models. Figure 1 shows edge centre architecture through LAN and WAN
networks connected to the cloud data centre.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)