This paper proposes a neuro-fuzzy system called Multi Attribute QoS scoring (MAQS) for dynamic resource allocation in collaborative cloud computing. MAQS uses a 3-layer neural network trained on 5 quality of service attributes - distance, reputation, task completion time, completion ratio, and load - to provide a QoS score for each resource. Resources are then allocated based on this score. The algorithm collects data periodically from nodes and calculates QoS scores for incoming tasks to select the highest scoring node for task allocation. The paper argues this approach considers multiple attributes and heterogeneity of resources better than previous single-attribute methods.
35 content distribution with dynamic migration of services for minimum cost u...INFOGAIN PUBLICATION
Content Delivery Networks are the key for today’s internet content delivery. Users are knowingly or unknowingly accessing the CDN via internet. No matter how much the data retrieved by the user it may contain the CDN hand behind every character of text and every pixel of image. CDN came into existence to solve the delay problem. The moment when a user requests for a web page and the response delivered to the corresponding users web browser facing a huge delay. The main goal of this paper is content distribution of web services to multiple data centers placed in different geographical locations and providing security. A content distribution service is a major part of popular Internet applications. In proposed system hybrid clouds are used i.e., both private cloud as well as public cloud. One data center is allocated to each region. Providing security to the data is always an important issue because of the critical nature of the cloud and very large amount of complicated data it carries. To provide security cipher text policy algorithm is used. Authentication technique is used to verify the user authentication. If the user is authorized to access services then and only he receives configuration key to use.
DYNAMIC TASK SCHEDULING BASED ON BURST TIME REQUIREMENT FOR CLOUD ENVIRONMENTIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
Dynamic Resource Provisioning with Authentication in Distributed DatabaseEditor IJCATR
Data center have the largest consumption amounts of energy in sharing the power. The public cloud workloads of different
priorities and performance requirements of various applications [4]. Cloud data center have capable of sensing an opportunity to present
different programs. In my proposed construction and the name of the security level of imperturbable privacy leakage rarely distributed
cloud system to deal with the persistent characteristics there is a substantial increases and information that can be used to augment the
profit, retrenchment overhead or both. Data Mining Analysis of data from different perspectives and summarizing it into useful
information is a process. Three empirical algorithms have been proposed assignments estimate the ratios are dissected theoretically and
compared using real Internet latency data recital of testing methods
35 content distribution with dynamic migration of services for minimum cost u...INFOGAIN PUBLICATION
Content Delivery Networks are the key for today’s internet content delivery. Users are knowingly or unknowingly accessing the CDN via internet. No matter how much the data retrieved by the user it may contain the CDN hand behind every character of text and every pixel of image. CDN came into existence to solve the delay problem. The moment when a user requests for a web page and the response delivered to the corresponding users web browser facing a huge delay. The main goal of this paper is content distribution of web services to multiple data centers placed in different geographical locations and providing security. A content distribution service is a major part of popular Internet applications. In proposed system hybrid clouds are used i.e., both private cloud as well as public cloud. One data center is allocated to each region. Providing security to the data is always an important issue because of the critical nature of the cloud and very large amount of complicated data it carries. To provide security cipher text policy algorithm is used. Authentication technique is used to verify the user authentication. If the user is authorized to access services then and only he receives configuration key to use.
DYNAMIC TASK SCHEDULING BASED ON BURST TIME REQUIREMENT FOR CLOUD ENVIRONMENTIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
Dynamic Resource Provisioning with Authentication in Distributed DatabaseEditor IJCATR
Data center have the largest consumption amounts of energy in sharing the power. The public cloud workloads of different
priorities and performance requirements of various applications [4]. Cloud data center have capable of sensing an opportunity to present
different programs. In my proposed construction and the name of the security level of imperturbable privacy leakage rarely distributed
cloud system to deal with the persistent characteristics there is a substantial increases and information that can be used to augment the
profit, retrenchment overhead or both. Data Mining Analysis of data from different perspectives and summarizing it into useful
information is a process. Three empirical algorithms have been proposed assignments estimate the ratios are dissected theoretically and
compared using real Internet latency data recital of testing methods
NEW SECURE CONCURRECY MANEGMENT APPROACH FOR DISTRIBUTED AND CONCURRENT ACCES...ijiert bestjournal
Handover the critical data to the cloud provider sh ould have the guarantee of security and availabilit y for data at rest,in motion,and in use. Many alternatives sys tems exist for storage services,but the data confi dentiality in the database as a service paradigm are still immature. We propose a novel architecture that integrates clo ud database services paradigm with data confidentiality and exe cuting concurrent operations on encrypted data. Thi s is the method supporting geographically distributed client s to connect directly and access to an encrypted cl oud database,and to execute concurrent and independent operation s by using modifying the database structure. The proposed architecture has also the more advanta ge of removing intermediate proxies that limit the flexibility,availability,and expandability properties that are inbuilt in cloud-based systems. The efficacy of th e proposed architecture is evaluated by theoretical analyses a nd extensive experimental results with the help of prototype implementation related to the TPC-C standard benchm ark for various categories of clients and network l atencies. We propose a multi-keyword ranked search method for the encrypted cloud data databases,which simultan eously fulfill the needs of privacy requirements. The prop osed scheme could return not only the exact matchin g files,but also the files including the terms latent semantica lly associated to the query keyword.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
MCCVA: A NEW APPROACH USING SVM AND KMEANS FOR LOAD BALANCING ON CLOUDijccsa
Nowadays, the demand of using resources, using services via the intranet system or on the Internet is rapidly growing. The respective problem coming is how to use these resources effectively in terms of time and quality. Therefore, the network QoS and its economy are people concerns, cloud computing was born in an inevitable trend. However, managing resources and scheduling tasks in virtualized data centres on the cloud are challenging tasks. Currently, there are a lot of Load Balancing algorithms applied in clouds and proposed by many authors, scholars, and experts. These existing methods are more about natural and heuristic, but the application of AI, or modern datamining technologies, in load balancing is not too popular due to the different characteristics of cloud. In this paper, we propose an algorithm to reduce the processing time (makespan) on cloud computing, helping the load balancing work more efficiency. Here, we use the SVM algorithm to classify the coming Requests, K - Mean to cluster the VMs in cloud, then the LB will allocate the requests into the VMs in the most reasonable way. In this way, request with the least processing time will be allocated to the VMs with the lowest usage. We name this new proposal as MCCVA - Makespan Classification & Clustering VM Algorithm. We have experimented and evaluated this algorithm in CloudSim, a cloud simulation environment, we obtained better results than some other wellknown algorithms. With this MCCVA, we can see the big potential of AI and datamining in Load Balancing, we can further develop LB with AI to achieve better and better results of QoS.
An Efficient Cloud Scheduling Algorithm for the Conservation of Energy throug...IJECEIAES
Method of broadcasting is the well known operation that is used for providing support to different computing protocols in cloud computing. Attaining energy efficiency is one of the prominent challenges, that is quite significant in the scheduling process that is used in cloud computing as, there are fixed limits that have to be met by the system. In this research paper, we are particularly focusing on the cloud server maintenance and scheduling process and to do so, we are using the interactive broadcasting energy efficient computing technique along with the cloud computing server. Additionally, the remote host machines used for cloud services are dissipating more power and with that they are consuming more and more energy. The effect of the power consumption is one of the main factors for determining the cost of the computing resources. With the idea of using the avoidance technology for assigning the data center resources that dynamically depend on the application demands and supports the cloud computing with the optimization of the servers in use.
THRESHOLD BASED VM PLACEMENT TECHNIQUE FOR LOAD BALANCED RESOURCE PROVISIONIN...IJCNCJournal
The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.
Efficient Resource Sharing In Cloud Using Neural NetworkIJERA Editor
In cloud computing, collaborative cloud computing(CCC) is the emerging technology where globally-dispersed cloud resource belonging to different organization are collectively used in a cooperative manner to provide services. In previous research, Harmony enables a node to locate its desired resources and also find the reputation of the located resources, so that a client can choose resource providers not only by resource availability but also by the provider’s reputation of providing the resource. In proposed system to reform resource utilization based on optimal time period to allocate resources to the neural network training and to load factor calculation the dynamic priority scheduling technique is used to assign the priority to the cloud users according to their load. The dynamic priority scheduling algorithm strikes the right balance between performance and power efficiency.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree.
Fuzzy Based Algorithm for Cloud Resource Management and Task Schedulingijtsrd
This paper presents a Fuzzy Logic based approach to manage VM status and VM configuration within the cloud environment. The aim of the approach is to serve task requests efficiently with minimal use of resource and power. The proposed technique uses Fuzzy based approach to calculate the VM's status and VMs configurations, depending upon the cloud resource availability and job resource requirements, and then a fuzzy logic based controller is used to control the status and configurations of the VM to serve the intended purpose afterwards. Controlling in this way reduces the active physical resources and the clouds power requirements. The proposed controller is tested for different load conditions against the standard controlling algorithm to validate the concept. The test results obtained show that in terms of QoS, resource management, and power savings, the proposed fuzzy logic controller based technique outperforms standard techniques. Rashmi Singh Lodhi | Dr. R. K. Pateriya "Fuzzy Based Algorithm for Cloud Resource Management and Task Scheduling" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26604.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26604/fuzzy-based-algorithm-for-cloud-resource-management-and-task-scheduling/rashmi-singh-lodhi
Hybrid Based Resource Provisioning in CloudEditor IJCATR
The data centres and energy consumption characteristics of the various machines are often noted with different capacities.
The public cloud workloads of different priorities and performance requirements of various applications when analysed we had noted
some invariant reports about cloud. The Cloud data centres become capable of sensing an opportunity to present a different program.
In out proposed work, we are using a hybrid method for resource provisioning in data centres. This method is used to allocate the
resources at the working conditions and also for the energy stored in the power consumptions. Proposed method is used to allocate the
process behind the cloud storage.
A SURVEY ON RESOURCE ALLOCATION IN CLOUD COMPUTINGijccsa
Cloud computing is an on-demand service resource which includes applications to data centers on a
pay-per-use basis. In order to allocate these resources properly and satisfy users’ demands, an efficient
and flexible resource allocation mechanism is needed. Due to increasing user demand, the resource
allocating process has become more challenging and difficult. One of the main focuses of research
scholars is how to develop optimal solutions for this process. In this paper, a literature review on proposed
dynamic resource allocation techniques is introduced.
A SURVEY ON RESOURCE ALLOCATION IN CLOUD COMPUTINGijccsa
Cloud computing is an on-demand service resource which includes applications to data centers on a
pay-per-use basis. In order to allocate these resources properly and satisfy users’ demands, an efficient
and flexible resource allocation mechanism is needed. Due to increasing user demand, the resource
allocating process has become more challenging and difficult. One of the main focuses of research
scholars is how to develop optimal solutions for this process. In this paper, a literature review on proposed
dynamic resource allocation techniques is introduced.
A Survey on Resource Allocation in Cloud Computingneirew J
Cloud computing is an on-demand service resource which includes applications to data centers on a
pay-per-use basis. In order to allocate these resources properly and satisfy users’ demands, an efficient
and flexible resource allocation mechanism is needed. Due to increasing user demand, the resource
allocating process has become more challenging and difficult. One of the main focuses of research
scholars is how to develop optimal solutions for this process. In this paper, a literature review on proposed
dynamic resource allocation techniques is introduced.
An Efficient Queuing Model for Resource Sharing in Cloud Computingtheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
NEW SECURE CONCURRECY MANEGMENT APPROACH FOR DISTRIBUTED AND CONCURRENT ACCES...ijiert bestjournal
Handover the critical data to the cloud provider sh ould have the guarantee of security and availabilit y for data at rest,in motion,and in use. Many alternatives sys tems exist for storage services,but the data confi dentiality in the database as a service paradigm are still immature. We propose a novel architecture that integrates clo ud database services paradigm with data confidentiality and exe cuting concurrent operations on encrypted data. Thi s is the method supporting geographically distributed client s to connect directly and access to an encrypted cl oud database,and to execute concurrent and independent operation s by using modifying the database structure. The proposed architecture has also the more advanta ge of removing intermediate proxies that limit the flexibility,availability,and expandability properties that are inbuilt in cloud-based systems. The efficacy of th e proposed architecture is evaluated by theoretical analyses a nd extensive experimental results with the help of prototype implementation related to the TPC-C standard benchm ark for various categories of clients and network l atencies. We propose a multi-keyword ranked search method for the encrypted cloud data databases,which simultan eously fulfill the needs of privacy requirements. The prop osed scheme could return not only the exact matchin g files,but also the files including the terms latent semantica lly associated to the query keyword.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
MCCVA: A NEW APPROACH USING SVM AND KMEANS FOR LOAD BALANCING ON CLOUDijccsa
Nowadays, the demand of using resources, using services via the intranet system or on the Internet is rapidly growing. The respective problem coming is how to use these resources effectively in terms of time and quality. Therefore, the network QoS and its economy are people concerns, cloud computing was born in an inevitable trend. However, managing resources and scheduling tasks in virtualized data centres on the cloud are challenging tasks. Currently, there are a lot of Load Balancing algorithms applied in clouds and proposed by many authors, scholars, and experts. These existing methods are more about natural and heuristic, but the application of AI, or modern datamining technologies, in load balancing is not too popular due to the different characteristics of cloud. In this paper, we propose an algorithm to reduce the processing time (makespan) on cloud computing, helping the load balancing work more efficiency. Here, we use the SVM algorithm to classify the coming Requests, K - Mean to cluster the VMs in cloud, then the LB will allocate the requests into the VMs in the most reasonable way. In this way, request with the least processing time will be allocated to the VMs with the lowest usage. We name this new proposal as MCCVA - Makespan Classification & Clustering VM Algorithm. We have experimented and evaluated this algorithm in CloudSim, a cloud simulation environment, we obtained better results than some other wellknown algorithms. With this MCCVA, we can see the big potential of AI and datamining in Load Balancing, we can further develop LB with AI to achieve better and better results of QoS.
An Efficient Cloud Scheduling Algorithm for the Conservation of Energy throug...IJECEIAES
Method of broadcasting is the well known operation that is used for providing support to different computing protocols in cloud computing. Attaining energy efficiency is one of the prominent challenges, that is quite significant in the scheduling process that is used in cloud computing as, there are fixed limits that have to be met by the system. In this research paper, we are particularly focusing on the cloud server maintenance and scheduling process and to do so, we are using the interactive broadcasting energy efficient computing technique along with the cloud computing server. Additionally, the remote host machines used for cloud services are dissipating more power and with that they are consuming more and more energy. The effect of the power consumption is one of the main factors for determining the cost of the computing resources. With the idea of using the avoidance technology for assigning the data center resources that dynamically depend on the application demands and supports the cloud computing with the optimization of the servers in use.
THRESHOLD BASED VM PLACEMENT TECHNIQUE FOR LOAD BALANCED RESOURCE PROVISIONIN...IJCNCJournal
The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.
Efficient Resource Sharing In Cloud Using Neural NetworkIJERA Editor
In cloud computing, collaborative cloud computing(CCC) is the emerging technology where globally-dispersed cloud resource belonging to different organization are collectively used in a cooperative manner to provide services. In previous research, Harmony enables a node to locate its desired resources and also find the reputation of the located resources, so that a client can choose resource providers not only by resource availability but also by the provider’s reputation of providing the resource. In proposed system to reform resource utilization based on optimal time period to allocate resources to the neural network training and to load factor calculation the dynamic priority scheduling technique is used to assign the priority to the cloud users according to their load. The dynamic priority scheduling algorithm strikes the right balance between performance and power efficiency.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree.
Fuzzy Based Algorithm for Cloud Resource Management and Task Schedulingijtsrd
This paper presents a Fuzzy Logic based approach to manage VM status and VM configuration within the cloud environment. The aim of the approach is to serve task requests efficiently with minimal use of resource and power. The proposed technique uses Fuzzy based approach to calculate the VM's status and VMs configurations, depending upon the cloud resource availability and job resource requirements, and then a fuzzy logic based controller is used to control the status and configurations of the VM to serve the intended purpose afterwards. Controlling in this way reduces the active physical resources and the clouds power requirements. The proposed controller is tested for different load conditions against the standard controlling algorithm to validate the concept. The test results obtained show that in terms of QoS, resource management, and power savings, the proposed fuzzy logic controller based technique outperforms standard techniques. Rashmi Singh Lodhi | Dr. R. K. Pateriya "Fuzzy Based Algorithm for Cloud Resource Management and Task Scheduling" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26604.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26604/fuzzy-based-algorithm-for-cloud-resource-management-and-task-scheduling/rashmi-singh-lodhi
Hybrid Based Resource Provisioning in CloudEditor IJCATR
The data centres and energy consumption characteristics of the various machines are often noted with different capacities.
The public cloud workloads of different priorities and performance requirements of various applications when analysed we had noted
some invariant reports about cloud. The Cloud data centres become capable of sensing an opportunity to present a different program.
In out proposed work, we are using a hybrid method for resource provisioning in data centres. This method is used to allocate the
resources at the working conditions and also for the energy stored in the power consumptions. Proposed method is used to allocate the
process behind the cloud storage.
A SURVEY ON RESOURCE ALLOCATION IN CLOUD COMPUTINGijccsa
Cloud computing is an on-demand service resource which includes applications to data centers on a
pay-per-use basis. In order to allocate these resources properly and satisfy users’ demands, an efficient
and flexible resource allocation mechanism is needed. Due to increasing user demand, the resource
allocating process has become more challenging and difficult. One of the main focuses of research
scholars is how to develop optimal solutions for this process. In this paper, a literature review on proposed
dynamic resource allocation techniques is introduced.
A SURVEY ON RESOURCE ALLOCATION IN CLOUD COMPUTINGijccsa
Cloud computing is an on-demand service resource which includes applications to data centers on a
pay-per-use basis. In order to allocate these resources properly and satisfy users’ demands, an efficient
and flexible resource allocation mechanism is needed. Due to increasing user demand, the resource
allocating process has become more challenging and difficult. One of the main focuses of research
scholars is how to develop optimal solutions for this process. In this paper, a literature review on proposed
dynamic resource allocation techniques is introduced.
A Survey on Resource Allocation in Cloud Computingneirew J
Cloud computing is an on-demand service resource which includes applications to data centers on a
pay-per-use basis. In order to allocate these resources properly and satisfy users’ demands, an efficient
and flexible resource allocation mechanism is needed. Due to increasing user demand, the resource
allocating process has become more challenging and difficult. One of the main focuses of research
scholars is how to develop optimal solutions for this process. In this paper, a literature review on proposed
dynamic resource allocation techniques is introduced.
An Efficient Queuing Model for Resource Sharing in Cloud Computingtheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Performance and Cost Evaluation of an Adaptive Encryption Architecture for Cl...Editor IJLRES
The cloud database as a service is a novel paradigm that can support several Internet-based applications, but its adoption requires the solution of information confidentiality problems. We propose a novel architecture for adaptive encryption of public cloud databases that offers an interesting alternative to the tradeoff between the required data confidentiality level and the flexibility of the cloud database structures at design time. We demonstrate the feasibility and performance of the proposed solution through a software prototype. Moreover, we propose an original cost model that is oriented to the evaluation of cloud database services in plain and encrypted instances and that takes into account the variability of cloud prices and tenant workloads during a medium-term period.
On the Optimal Allocation of VirtualResources in Cloud Compu.docxhopeaustin33688
On the Optimal Allocation of Virtual
Resources in Cloud Computing Networks
Chrysa Papagianni, Aris Leivadeas, Symeon Papavassiliou,
Vasilis Maglaris, Cristina Cervelló-Pastor, and �Alvaro Monje
Abstract—Cloud computing builds upon advances on virtualization and distributed computing to support cost-efficient usage of
computing resources, emphasizing on resource scalability and on demand services. Moving away from traditional data-center oriented
models, distributed clouds extend over a loosely coupled federated substrate, offering enhanced communication and computational
services to target end-users with quality of service (QoS) requirements, as dictated by the future Internet vision. Toward facilitating the
efficient realization of such networked computing environments, computing and networking resources need to be jointly treated and
optimized. This requires delivery of user-driven sets of virtual resources, dynamically allocated to actual substrate resources within
networked clouds, creating the need to revisit resource mapping algorithms and tailor them to a composite virtual resource mapping
problem. In this paper, toward providing a unified resource allocation framework for networked clouds, we first formulate the optimal
networked cloud mapping problem as a mixed integer programming (MIP) problem, indicating objectives related to cost efficiency of
the resource mapping procedure, while abiding by user requests for QoS-aware virtual resources. We subsequently propose a method
for the efficient mapping of resource requests onto a shared substrate interconnecting various islands of computing resources, and
adopt a heuristic methodology to address the problem. The efficiency of the proposed approach is illustrated in a simulation/emulation
environment, that allows for a flexible, structured, and comparative performance evaluation. We conclude by outlining a proof-of-
concept realization of our proposed schema, mounted over the European future Internet test-bed FEDERICA, a resource virtualization
platform augmented with network and computing facilities.
Index Terms—Federated infrastructures, resource allocation, resource mapping, virtualization, cloud computing, quality of service
Ç
1 INTRODUCTION
CLOUD computing promises reliable services deliveredthrough next generation data centers that are built on
compute and storage virtualization technologies. According
to Buyya et al., [1] “a cloud is a type of parallel and distributed
system consisting of a collection of interconnected and virtualized
computers that are dynamically provisioned and presented as one
or more unified computing resources based on service-level
agreements established through negotiation between the service
provider and the consumers” and accessible as a composable
service via web 2.0 technologies.
Therefore, with respect to cloud computing there exist
the “as a service” definitions, which include software as a
service (SaaS), infrastructure as a se.
ANALYSIS OF THE COMPARISON OF SELECTIVE CLOUD VENDORS SERVICESijccsa
Cloud computing refers to a location that allows us to preserve our precious data and use computing and
networking services on a pay-as-you-go basis without the need for a physical infrastructure. Cloud
computing now provides us with powerful data processing and storage, exceptional availability and
security, rapid accessibility and adaption, ensured flexibility and interoperability, and time and cost
efficiency. Cloud computing offers three platforms (IaaS, PaaS, and SaaS) with unique capabilities that
promise to make it easier for a customer, organization, or trade to establish any type of IT business. We
compared a variety of cloud service characteristics in this article, following the comparing, it's
straightforward to pick a specific cloud service from the possible options by comparison with three chosen
cloud providers such as Amazon, Microsoft Azure, and Digital Ocean. By using findings of this study to not
only identify similarities and contrasts across various aspects of cloud computing, as well as to suggest
some areas for further study.
Multi-objective load balancing in cloud infrastructure through fuzzy based de...IAESIJAI
Cloud computing became a popular technology which influence not only
product development but also made technology business easy. The services
like infrastructure, platform and software can reduce the complexity of
technology requirement for any ecosystem. As the users of cloud-based
services increases the complexity of back-end technologies also increased.
The heterogeneous requirement of users in terms for various configurations
creates different unbalancing issues related to load. Hence effective load
balancing in a cloud system with reference to time and space become crucial
as it adversely affect system performance. Since the user requirement and
expected performance is multi-objective use of decision-making tools like
fuzzy logic will yield good results as it uses human procedure knowledge in
decision making. The overall system performance can be further improved by
dynamic resource scheduling using optimization technique like genetic
algorithm.
Agent based Aggregation of Cloud Services- A Research Agendaidescitation
-Cloud computing has come to the forefront as it
overcomes some of the issues in computing such as storage
space and processing power. It enables ubiquitous accessing
and processing of information without the need of excessive
computing facilities. In this work, we plan to brief some of the
issues in aggregating the cloud services, discovering futuristic
cloud service requests, develop a repository of the same and
propose an agent based Quality of Service (QoS) provisioning
system for cloud clients.
A CLOUD BROKER APPROACH WITH QOS ATTENDANCE AND SOA FOR HYBRID CLOUD COMPUTIN...cscpconf
Cloud Computing is the industry whose demand has been growing continuously since its appearance as a solution that offers different types of computing resources as a service over the Internet. The number of cloud computing providers grows into a run, while the end user is currently in the position of having many pricing options, distinct features and performance for the same required service. This work is inserted in the cloud computing task scheduling research field to hybrid cloud environments with service-oriented architecture (SOA), dynamic allocation and control of services and QoS requirements attendance. Therefore, it is proposed the QBroker Architecture, representing a cloud broker with trading features that implement the intermediation services, defined by the NIST Cloud Computing Reference Model. An experimental design was created in order to demonstrate compliance to the QoS requirement of maximum task execution time, the differentiation of services and dynamic allocation of services. The experimental results obtained by simulation with CloudSim prove that QBroker has the necessary requirements to provide QoS improvement in hybrid cloud computing environments based on SOA.
A cloud broker approach with qos attendance and soa for hybrid cloud computin...csandit
Cloud Computing is the industry whose demand has been growing continuously since its
appearance as a solution that offers different types of computing resources as a service over the
Internet. The number of cloud computing providers grows into a run, while the end user is
currently in the position of having many pricing options, distinct features and performance for
the same required service. This work is inserted in the cloud computing task scheduling
research field to hybrid cloud environments with service-oriented architecture (SOA), dynamic
allocation and control of services and QoS requirements attendance. Therefore, it is proposed
the QBroker Architecture, representing a cloud broker with trading features that implement the
intermediation services, defined by the NIST Cloud Computing Reference Model. An
experimental design was created in order to demonstrate compliance to the QoS requirement of
maximum task execution time, the differentiation of services and dynamic allocation of services.
The experimental results obtained by simulation with CloudSim prove that QBroker has the
necessary requirements to provide QoS improvement in hybrid cloud computing environments
based on SOA.
Cloud computing is the fastest emerging technology and a novel buzzword in the field of IT domain that offer distinct services, applications and focuses on providing sustainable, reliable, scalable and virtualized resources to its consumer. The main aim of cloud computing is to enhance the use of distributed resources to achieve higher throughput and resource utilization in large-scale computation problems. Scheduling affects the efficiency of cloud and plays a significant role in cloud computing to create high performance environment. The Quality of Service (QoS) requirements of user application define the scheduling of resources. Numbers of researchers have tried to solve these scheduling problems using different QoS based scheduling techniques. In this paper, a detail analysis of resource scheduling methodology is presented, with different types of scheduling based on soft computing techniques, their comparisons, benefits and results are discussed. Major finding of this paper helps researchers to decide suitable approach for scheduling user’s applications considering their QoS requirements.
ANALYSIS OF ATTACK TECHNIQUES ON CLOUD BASED DATA DEDUPLICATION TECHNIQUESneirew J
ABSTRACT
Data in the cloud is increasing rapidly. This huge amount of data is stored in various data centers around the world. Data deduplication allows lossless compression by removing the duplicate data. So, these data centers are able to utilize the storage efficiently by removing the redundant data. Attacks in the cloud computing infrastructure are not new, but attacks based on the deduplication feature in the cloud computing is relatively new and has made its urge nowadays. Attacks on deduplication features in the cloud environment can happen in several ways and can give away sensitive information. Though, deduplication feature facilitates efficient storage usage and bandwidth utilization, there are some drawbacks of this feature. In this paper, data deduplication features are closely examined. The behavior of data deduplication depending on its various parameters are explained and analyzed in this paper.
SUCCESS-DRIVING BUSINESS MODEL CHARACTERISTICS OF IAAS AND PAAS PROVIDERSneirew J
ABSTRACT Market analyses show that some cloud providers are significantly more successful than others. The research on the success-driving business model characteristics of cloud providers and thus, the reasons for this performance discrepancy is, however, still limited. Whereas cloud business models have mostly been examined comprehensively, independently from the distinctly different cloud ecosystem roles, this paper takes a perspective shift from an overall towards a selective, role-specific and thereby ecosystemic perspective on cloud business models. The goal of this paper is specifically to identify the success-driving business model characteristics of the so far widely neglected cloud ecosystem’s core roles, IaaS and PaaS provider, by conducting an exploratory multiple-case study. 21 expert interviews with representatives from 17 cloud providers serve as central data collection instrument. The result is a catalogue of generic as well as cloud-specific, subdivided into role-overarching and role-specific, business model characteristics. This catalogue supports cloud providers in the initial design, comparison and revision of their business models. Researchers obtain a promising starting and reference point for future analysis of business models of various cloud ecosystem roles.
Strategic Business Challenges in Cloud Systemsneirew J
For the past few years, the evolution of cloud computing has been potentially becoming one of the major
advances in the history of computing. But is cloud computing the saviour of business? Does it signal the
demise of the corporate IT functionality entirely? However, if cloud computing has to achieve its potential,
there is a need to have a clear understanding of various issues involved, both from the perspectives of the
providers and the consumers related to the technology, management and business aspects. Objective of this
research is to explore the strategic business, management and technical challenges existing in cloud
systems. It is believed that adopting a methodology and suggesting a corresponding architectural
framework would serve as a potential comprehensive conceptual tool, which shows path for mitigating
challenges and hence effort are put in bringing in by mentioning a suitable methodology and its brief
description. It concludes that International Business Machine Common Cloud Management Platform is one
way to realize the combined features of various models such as Hub & Spoke Model as a quality of
Governance model; Gen-Spec Research Methodology design for semantic and quality research studies into
one in the form of Reference Architecture. However in order to realize the full potential of the CustomerRespond-Adapt-Sense-Provider
(conceptual) methodology for dealing with semantics, it is important to
consider Internet of Things Architecture Reference Model where in the resources are translated into
Services.
Laypeople's and Experts' Risk Perception of Cloud Computing Services neirew J
Cloud computing is revolutionising the way software services are procured and used by Government
organizations and SMEs. Quantitative risk assessment of Cloud services is complex and undermined by
specific security concerns regarding data confidentiality, integrity and availability. This study explores how
the gap between the quantitative risk assessment and the perception of the risk can produce a bias in the
decision-making process about Cloud computing adoption.
The risk perception of experts in Cloud computing (N=37) and laypeople (N=81) about ten Cloud
computing services was investigated using the psychometric paradigm. Results suggest that the risk
perception of Cloud services can be represented by two components, called “dread risk” and “unknown
risk”, which may explain up to 46% of the variance. Other factors influencing the risk perception were
“perceived benefits”, “trust in regulatory authorities” and “technology attitude”.
This study suggests some implications that could support Government and non-Government organizations
in their strategies for Cloud computing adoption.
Factors Influencing Risk Acceptance of Cloud Computing Services in the UK Gov...neirew J
Cloud Computing services are increasingly being made available by the UK Government through the
Government digital marketplace to reduce costs and improve IT efficiency; however, little is known about
factors influencing the decision making process to adopt cloud services within the UK Government. This
research aims to develop a theoretical framework to understand risk perception and risk acceptance of
cloud computing services.
Study’s subjects (N=24) were recruited from three UK Government organizations to attend a semi
structured interview. Transcribed texts were analyzed using the approach termed interpretive
phenomenological analysis. Results showed that the most important factors influencing risk acceptance of
cloud services are: perceived benefits and opportunities, organization’s risk culture and perceived risks.
We focused on perceived risks and perceived security concerns. Based on these results, we suggest a
number of implications for risk managers, policy makers and cloud service providers
A Cloud Security Approach for Data at Rest Using FPE neirew J
In a cloud scenario, biggest concern is around security of the data. “Both data in transit and at rest must
be secure” is a primary goal of any organization. Data in transit can be made secure using TLS level
security like SSL certificates. But data at rest is not quite secure, as database servers in public cloud
domain are more prone to vulnerabilities. Not all cloud providers give out of box encryption with their
offerings. Also implementing traditional encryption techniques will cause lot of changes in application as
well as at database level. This paper provides efficient approach to encrypt data using Format Preserving
Encryption technique. FPE focuses mainly on encrypting data without changing format so that it’s easy to
develop and migrate legacy application to cloud. It is capable of performing format preserving encryption
on numeric, string and the combination of both. This literature states various features and advantages of
same.
Error Isolation and Management in Agile Multi-Tenant Cloud Based Applications neirew J
Management of errors in multi-tenant cloud based applications remains a challenging problem. This
problem is compounded due to (i) multiple versions of application serving different clients, (ii) agile nature
in which the applications are released to the clients, and (iii) variations in specific usage patterns of each
client. We propose a framework for isolating and managing errors in such applications. The proposed
framework is evaluated with two different popular cloud based applications and empirical results are
presented.
Locality Sim : Cloud Simulator with Data Localityneirew J
Cloud Computing (CC) is a model for enabling on-demand access to a shared pool of configurable
computing resources. Testing and evaluating the performance of the cloud environment for allocating,
provisioning, scheduling, and data allocation policy have great attention to be achieved. Therefore, using
cloud simulator would save time and money, and provide a flexible environment to evaluate new research
work. Unfortunately, the current simulators (e.g., CloudSim, NetworkCloudSim, GreenCloud, etc..) deal
with the data as for size only without any consideration about the data allocation policy and locality. On
the other hand, the NetworkCloudSim simulator is considered one of the most common used simulators
because it includes different modules which support needed functions to a simulated cloud environment,
and it could be extended to include new extra modules. According to work in this paper, the
NetworkCloudSim simulator has been extended and modified to support data locality. The modified
simulator is called LocalitySim. The accuracy of the proposed LocalitySim simulator has been proved by
building a mathematical model. Also, the proposed simulator has been used to test the performance of the
three-tire data center as a case study with considering the data locality feature.
Benefits and Challenges of the Adoption of Cloud Computing in Businessneirew J
The loss of business and downturn of economics almost occur every day. Thus technology is needed in
every organization. Cloud computing has played a major role in solving the inefficiencies problem in
organizations and increase the growth of business thus help the organizations to stay competitive. It is
required to improve and automate the traditional ways of doing business. Cloud computing has been
considered as an innovative way to improve business. Overall, cloud computing enables the organizations
to manage their business efficiently. Unnecessary procedural, administrative, hardware and software costs
in organizations expenses are avoided using cloud computing. Although cloud computing can provide
advantages but it does not mean that there are no drawbacks. Security has become the major concern in
cloud and cloud attacks too. Business organizations need to be alert against the attacks to their cloud
storage. Benefits and drawbacks of cloud computing in business will be explored in this paper. Some
solutions also provided in this paper to overcome the drawbacks. The method has been used is secondary
research, that is collecting data from published journal papers and conference papers.
Intrusion Detection and Marking Transactions in a Cloud of Databases Environm...neirew J
The cloud computing is a paradigm for large scale distributed computing that includes several existing
technologies. A database management is a collection of programs that enables you to store, modify and
extract information from a database. Now, the database has moved to cloud computing, but it introduces at
the same time a set of threats that target a cloud of database system. The unification of transaction based
application in these environments present also a set of vulnerabilities and threats that target a cloud of
database environment. In this context, we propose an intrusion detection and marking transactions for a
cloud of database environment.
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...neirew J
Fast development of knowledge and communication has established a new computational style which is
known as cloud computing. One of the main issues considered by the cloud infrastructure providers, is to
minimize the costs and maximize the profitability. Energy management in the cloud data centers is very
important to achieve such goal. Energy consumption can be reduced either by releasing idle nodes or by
reducing the virtual machines migrations. To do the latter, one of the challenges is to select the placement
approach of the migrated virtual machines on the appropriate node. In this paper, an approach to reduce
the energy consumption in cloud data centers is proposed. This approach adapts harmony search
algorithm to migrate the virtual machines. It performs the placement by sorting the nodes and virtual
machines based on their priority in descending order. The priority is calculated based on the workload.
The proposed approach is simulated. The evaluation results show the reduction in the virtual machine
migrations, the increase of efficiency and the reduction of energy consumption.
Data Distribution Handling on Cloud for Deployment of Big Dataneirew J
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud
computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to
process massive amounts of data. Processing large datasets has become crucial in research and business
environments. The big challenges associated with processing large datasets is the vast infrastructure
required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be
provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can
be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer
combines individual output from each Vms to produce final result. we have proposed an algorithm to
reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst
Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall
data processing time in cloud.
Cloud Computing is an attractive research area for the last few years; and there have been a tremendous
grows in the number of educational institutions all over the world who have either adopted or are
considering migrating to cloud computing. However, there are many concerns and reservations about
adopting conventional or public cloud based solutions. A new paradigm of cloud based solution has been
proposed, namely, the private cloud based solutions, which becomes an attractive choice to educational
Institutions. This paper presents the adjustment and implementation of private-based cloud solution for
multi-campus educational institution, namely, Al-Balqa Applied University (BAU) in Jordan.
Implementation of the Open Source Virtualization Technologies in Cloud Computingneirew J
The “Virtualization and Cloud Computing” is a recent buzzword in the digital world. Behind this fancy
poetic phrase there lies a true picture of future computing for both in technical and social perspective.
Though the “Virtualization and Cloud Computing are recent but the idea of centralizing computation and
storage in distributed data centres maintained by any third party companies is not new but it came in way
back in 1990s along with distributed computing approaches like grid computing, Clustering and Network
load Balancing. Cloud computing provide IT as a service to the users on-demand basis. This service has
greater flexibility, availability, reliability and scalability with utility computing model. This new concept of
computing has an immense potential in it to be used in the field of e-governance and in the overall IT
development perspective in developing countries like Bangladesh.
A Broker-based Framework for Integrated SLA-Aware SaaS Provisioning neirew J
In the service landscape, the issues of service selection, negotiation of Service Level Agreements (SLA), and
SLA-compliance monitoring have typically been used in separate and disparate ways, which affect the
quality of the services that consumers obtain from their providers. In this work, we propose a broker-based
framework to deal with these concerns in an integrated mannerfor Software as a Service (SaaS)
provisioning. The SaaS Broker selects a suitable SaaS provider on behalf of the service consumer by using
a utility-driven selection algorithm that ranks the QoS offerings of potential SaaS providers. Then, it
negotiates the SLA terms with that provider based on the quality requirements of the service consumer. The
monitoring infrastructure observes SLA-compliance during service delivery by using measurements
obtained from third-party monitoring services. We also define a utility-based bargaining decision model
that allows the service consumer to express her sensitivity for each of the negotiated quality attributes and
to evaluate the SaaS provider offer in each round of negotiation. A use-case with few quality attributes and
their respective utility functions illustrates the approach.
Comparative Study of Various Platform as a Service Frameworks neirew J
Cloud computing is an emerging paradigm with three basic service models such as Software as a Service
(SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). This paper focuses on
different kinds of PaaS frameworks. PaaS model provides choice of cloud, developer framework and
application service. In this paper, detailed study of four open PaaS frameworks like AppScale, Cloud
Foundry, Cloudify, and OpenShift are explained with the architectural components. We also explained
more PaaS packages like Stratos, mOSAIC, BlueMix, Heroku, Amazon Elastic Beanstalk, Microsoft Azure,
Google App Engine and Stakato briefly. In this paper we present the comparative study of PaaS
frameworks.
A Proposed Model for Improving Performance and Reducing Costs of IT Through C...neirew J
Information technologies are affecting the big business enterprises of todays from data processing and
transactions to achieve the goals efficiently and effectively, affecting creates new business opportunities
and towards new competitive advantage, service must be enough to match the recent trends of IT such as
cloud computing. Cloud computing technology has provided all IT services. Therefore, cloud computing
offers an alternative to adaptable with technology model current , creating reducing cost (Fixed costs and
ongoing), the proliferation of high speed Internet connections through Rent, not acquisitions, cheaper
powerful computing technology and effective performance. The public and private clouds are characterized
by flexibility, operational efficiency that reduces costs improve performance. Also cloud computing
generates business creativity and innovation resulted from collaborative ideas of users; presents cloud
infrastructure and services; paving new markets; offering security in public and private clouds; and
providing environmental impact regarding utilizing green energy technology. In this paper, the main
concentrate the cloud computing.
Secure cloud transmission protocol (SCTP) was proposed to achieve strong authentication and secure
channel in cloud computing paradigm at preceding work. SCTP proposed with its own techniques to attain
a cloud security. SCTP was proposed to design multilevel authentication technique with multidimensional
password generations System to achieve strong authentication. SCTP was projected to develop multilevel
cryptography technique to attain secure channel. SCTP was proposed to blueprint usage profile based
intruder detection and prevention system to resist against intruder attacks. SCTP designed, developed and
analyzed using protocol engineering phases. Proposed SCTP and its techniques complete design has
presented using Petrinet production model. We present the designed SCTP petrinet models and its
analysis. We discussed the SCTP design and its performance to achieve strong authentication, secure
channel and intruder prevention. SCTP designed to use in any cloud applications. It can authorize,
authenticates, secure channel and prevent intruder during the cloud transaction. SCTP designed to protect
against different attack mentioned in literature. This paper depicts the SCTP performance analysis report
which compares with existing techniques that are proposed to achieve authentication, authorization,
security and intruder prevention.
Attribute Based Access Control (ABAC) for EHR in Fog Computing Environmentneirew J
Cisco recently proposed a new computing environment called fog computing to support latency-sensitive
and real time applications. It is a connection of billions of devices nearest to the network edge. This
computing will be appropriate for Electronic Medical Record (EMR) systems that are latency-sensitive in
nature. In this paper, we aim to achieve two goals: (1) Managing and sharing Electronic Health Records
(EHRs) between multiple fog nodes and cloud, (2) Focusing on security of EHR, which contains highly
confidential information. So, we will secure access into EHR on Fog computing without effecting the
performance of fog nodes. We will cater different users based on their attributes and thus providing
Attribute Based Access Control ABAC into the EHR in fog to prevent unauthorized access. We focus on
reducing the storing and processes in fog nodes to support low capabilities of storage and computing of fog
nodes and improve its performance.
A Baye's Theorem Based Node Selection for Load Balancing in Cloud Environmentneirew J
Cloud computing is a popular computing model as it renders service to large number of users request on
the fly and has lead to the proliferation of large number of cloud users. This has lead to the overloaded
nodes in the cloud environment along with the problem of load imbalance among the cloud servers and
thereby impacts the performance. Hence, in this paper a heuristic Baye's theorem approach is considered
along with clustering to identify the optimal node for load balancing. Experiments using the proposed
approach are carried out on cloudsim simulator and are compared with the existing approach. Results
demonstrates that task deployment performed using this approach has improved performance in terms of
utilization and throughput when compared to the existing approaches.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Neuro-Fuzzy System Based Dynamic Resource Allocation in Collaborative Cloud Computing Using Multi Attribute QOS
1. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 1, February 2016
DOI : 10.5121/ijccsa.2016.6102 9
NEURO-FUZZY SYSTEM BASED DYNAMIC
RESOURCE ALLOCATION IN COLLABORATIVE
CLOUD COMPUTING USING MULTI ATTRIBUTE
QOS
Anitha 1
and Anirban Basu2
1
Research Scholar, VTU, EPCET, Bangalore
2
Professor, Department of CSE, APS College of Engg, Bangalore
ABSTRACT
Cloud collaboration is an emerging technology which enables sharing of computer files using cloud
computing. Here the cloud resources are assembled and cloud services are provided using these resources.
Cloud collaboration technologies are allowing users to share documents. Resource allocation in the cloud
is challenging because resources offer different Quality of Service (QoS) and services running on these
resources are risky for user demands. We propose a solution for resource allocation based on multi
attribute QoS Scoring considering parameters such as distance to the resource from user site, reputation of
the resource, task completion time, task completion ratio, and load at the resource. The proposed algorithm
referred to as Multi Attribute QoS scoring (MAQS) uses Neuro Fuzzy system. We have also included a
speculative manager to handle fault tolerance. In this paper it is shown that the proposed algorithm
perform better than others including power trust reputation based algorithms and harmony method which
use single attribute to compute the reputation score of each resource allocated.
KEYWORDS
Collaborative Cloud, Neural Network, Fuzzy system, Multi attribute, reputation
1. INTRODUCTION
In the current IT world, Cloud computing is gaining momentum, and cloud providers provide
customer with the IT infrastructure in a virtual manner which can be accessed using the internet.
High amount of data is exchanged between the cloud customers all over variety of computing
resources in high bandwidth along with data storage at the secondary level. Due to this; there is a
huge requirement for scalable resources among various cloud customers. But the problem with
the present single cloud computing will not be able to manage the connecting and detecting
activities for an application while it is running. Hence there is a need for virtual lab environment
to be built for the researchers so that they can connect multiple cloud servers, thus Collaborative
Cloud Computing (CCC) is one such proposal which is led by the advance research.
In the CCC, the resource are collectively distributed in a cooperative manner so that the different
organization and also different desktop types are interconnected into a virtual organization using
the CCC, so that if the resources provided in a single cloud server is not sufficient then the cloud
provider will switch to a different cloud application.
2. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 1, February 2016
10
CCC comprises of a millions of cloud resources which are sourced from different parts in a
distributed format. Thus the environment will use resource management (resMgt) efficiently and
Quality of Service (QoS) may be provided by different node types.
The Amazon’s family of web services in cloud platforms [1], using an Infrastructure-as-a-
Service (IaaS) model provides abstractions that are general enough to support a wide range of
existing distributed computing platforms tailored to specific application scenarios. Amazon
allows purchaser to rent virtual machines (EC2), storage volumes (EBS), and storage objects (S3)
on-demand and expend only for resources they use. While pricing models vary for each resource,
purchasers typically pay a fixed rate for both their length of use and their aggregate network and
disk I/O bandwidth.
Many consider IaaS platforms a natural evolution of ongoing work on high-performance
scientific and grid computing [2], which focuses predominantly on supporting large-scale
execution of computationally-intensive scientific tasks. Due to the generality of IaaS platforms,
applications with other models of computation have also become increasingly popular. In
particular, Google’s family of services, tailor abstractions for sub-tasks that are useful for
efficiently storing and searching unstructured, and largely static, customer and web data.
Reputation management and the resource management are utilized for gaining the performance
with the QoS and for selection of resources in trust worthy manner. There are three way of
connecting the cloud server in a trustworthy manner with effective and efficiency such as:
identifying the trust worthy resource, choosing the right resources and using other system for
utilizing the resources fully [3].
To address the trust worthy allocation, reputation based algorithms were used which does not
focus on the QoS and resource heterogeneity was neglected by assigning each node one
reputation value for providing all of its resources. So we propose an algorithm based on multi
attribute QoS selection and allocation of resources based on different reputation values using the
combination of neural network and fuzzy logic in CCC.
2. RELATED WORK
In this paper [4] the cloud information extraction technique was supported by the Collaborative
Cloud Computing. Neural Network (NN) based system was used for information retrieval which
is located in the different system as these data are accessed directly. The mechanism such as the
Artificial Neural Network, the output values are used here to activate the input functions; no
additional effort is needed to get the information. In the paper, they propose a solution where the
neural network is combined with the learning system so that the single point of failure is
eliminated and most of the cloud computing issues are eliminated and hence making an effective
and efficient information extraction in a cloud computing collaborative environment.
In this paper[5] the security issues are addressed by the CTrust Framework, this is done by
connecting different VT (Virtualization technology) and then be able to access the resources such
as the network, storage and software’s. Cloud running applications root trusts are made using the
Secure Hypervisor framework. One of the major issues of the cloud computing is the cloud
security, even though the cloud computing is used in online auction or e-commerce commerce; it
can be used in many different fields. The NIST (National Institute of Standards and Technology)
says the main concern in the cloud computing is its security issues. Here the operating system and
hardware are coupled using the software abstraction.
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The older methods such as the reputation management and resource management method are not
effective in long run and there is a lack of support for the dynamic and large environment which
is needed for the CCC. The older method focus is on one of the QoS parameter which is either the
efficiency or the security. Here the difference between the reputation management and the
resource management is understood by a method called Harmony [6], which has helped in a
proposal of the solution with a CCC platform which is a combination of the reputation and
resource management.
In order to have large scale CCC, selecting trustworthy resources, choosing these resources and
utilizing them to the maximum extent, must be executed in order. Many techniques have been
proposed for resource and reputation management but these two issues have been discussed
separately. When we combine both resource management and reputation management in CCC, it
is creating high overhead [7]
There exist another method which used harmony; uses the cycloid structure [8] in which all the
nodes are connected to one QoS parameter It can have a maximum of n=d*2 d
nodes, where d is
dimension. Each cyclic node id consists of two indices: cyclic index and cubic index.
3. PROPOSED SOLUTION
The proposed solution in done by QoS scoring which is multi attributed .The resources are
allocated with QoS score based on the attributes such as distance, reputation, task completion
time along with completion ratio and load.
The neural network is trained on QoS scoring system and the resource with best score is allocated
for user task.
The QoS score is calculated as explained below:
Let m1, m2, m3 ….mN be the number of machines in coordinated cloud.
Let t1, t2… tr be the number of task arriving for execution in cloud. Each of these tasks have a
deadline time dt1, dt2… dtr.
Let the actual completion time of jobs be at1, at2… atr
The job of schedulers is to allocate the tasks to machine in such a way that
Task Completion (TC) = ¥ (ati-dti) == 0 with 0< i < r
With objective function of maximizing the TC such a way that: TC-r is close to zero.
4. THE MULTI ATTRIBUTE QOS SCORING (MAQS)
The proposed solution called MAQS algorithm involves three stages:
Stage 1: Training the neural Network
Stage 2: Data collection for availability
Stage 3: Scheduling
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Training
The multi-layer feed forward neural network is trained periodically with five attributes which are
distance, reputation score, task completion time, task completion ratio and load and provides
price as the QoS output.
The five attributes are explained in detail below:
1. Distance is an important factor that affect the response time. If the processing center is
located close the request generated site, the response time in carrying the job to site and
carrying the result back to requested site is lowered. So we have considered distance as
one of the parameter.
2. Reputation of the resource is based on the number of times the jobs are completed
without any errors. In our solution, once the machine is allocated job and reply arrives,
the user can rate whether reply is accurate. Based on this reputation is calculated. For
calculation of Reputation Score (RS) we employ weighted averaging scheme based on
equation 1
RS = α * RS + (1-α) RSold ------------ (1)
New RS is given as feed back by user in scale of 1 to 5 based on the accuracy of result
and the new RS score is calculated using the weighted averaging scheme. We use
weighted scheme because, reputation should consider the history and not instant value of
the current reputation alone.
3. Task completion time is an indication of how fast the machine can run for job execution.
The machine with fast CPU cycles must be preferred more than others, so we have used
task completion time as one of the parameter for calculating the QOS.
4. Task completion ratio is indicator of how good the machine executed the job without
downtime. Machine with low downtime must be preferred than others with high down
time for job execution. So we have considered this parameter.
5. Scheduling policy must also be able to fairly share the load. Although machine with
faster CPU, closely located with less down time are more preferred, if all load is
scheduled to these machine alone, it will have a negative effect. So a load threshold must
be there on the machine. This is ensured by including load as one parameter for
calculating the QOS.
To generate training data set we have used Fuzzy Decision system. Fuzzy membership function is
devised for all the 5 input parameters and 1 output parameter and rule set is defined to convert
input to output.
Once the fuzzy logic system is created, we randomly generate different values for the input
parameters and use the fuzzy system to get the QOS value for those input variables and the result
is written to training file. Neural Network will use this training file to train the neurons. So at core
we have created Neuro Fuzzy System.
Distance (D) is in range of 0 to 1000 km and the membership function for it is defined by splitting
the distance to three ranges Near (N), Middle (M), and Far (F) as in figure 1
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Fig 1: Range of variable Distance
Reputation (R) is in range of 0 to 10 and membership function for it is defined by splitting the
reputation to three ranges Bad (B),Medium(M), good(G) as shown in figure 2
Fig 2: Range of variable Reputaion
Task completion time (T) is converted in terms of ratio to maximum completion time expected in
system. It is in range of 0 to 1. The membership function for it is defined by splitting to three
ranges Less (L), More (M), High (H) as in figure 3.
Fig 3: Range of variable Task completion Time
Task completion ratio (TR) is in range of 0 to 1. The membership function for it is defined by
splitting to three ranges Less (L), Medium (M), High (H) as shown in figure 4.
Fig 4: Range of variable Task completion ratio
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Load (L) at resource is in range of 0 to 100. The membership function for it is split to three ranges
Less (L), Medium (M) and High (H) as in figure 5
Fig 5: Range of variable Load
The output variable QOS is in range of 0 to 10 and it is split to three membership function
Low(L) , Medium(M), High(H) as shown in figure 6
Fig 6: Output variable QoS
The rule set for fuzzy system is given below in table 1
Table 1 Fuzzy Rule set
D R T TR L QOS
N B L L L M
N B L L M M
N B L L H L
N B L M L M
N B L M M M
N B L M H L
N B L H L H
N B L H M L
N B L H H L
N M L L M M
N M L L H L
N M L M L M
N M L M M M
N M L M H L
N M L H L H
N M L H M L
N M L H H L
N M M L L M
N M M L M M
N M M L H L
N M M M L M
N M M M M M
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N M M M H L
M B M L M M
M B M L H L
M B M M L M
M B M M M M
M B M M H L
M B M H L H
M B M H M L
M B M H H L
M B H L L M
M B H L M M
M B H L H L
M B H M L M
M B H M M M
M B H M H L
M B H H L H
M B H H M L
M B H H H L
M M L L L M
M M L L M M
M M L L H L
M M L M L M
M M L M M M
M M L M H L
M M L H L H
M M L H M L
M M L H H L
M M M L L M
M M M L M M
M M M L H L
M M M M L M
M M M M M M
M M M M H L
H B L L L M
H B L L M M
H B L L H L
H B L M L M
H B L M M M
H B L M H L
H B L H L H
H B L H M L
H B L H H L
H B M L L M
H B M L M M
H M M L M M
H M M L H L
H M M M L M
H M M M M M
H M M M H L
H M M H L H
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Data Collection
The data is collected frequently from all the nodes, which are separated over the world in this
phase. Nodes which are participated in collaborative cloud (CC) have dissect installed and this
dissect collects the census and reports to central manager about this. It maintains the periodic
heartbeat with the nodes in collaborative cloud to know the availability.
Scheduling
User tasks are allocated with resources based on the QOS.
Whenever user task arrives we calculate the QOS score for the node by providing the data
collection from node to neural network. The best QOS score node is selected and the task is
allocated.
The scheduling algorithm flow is given below
Input: the job to schedule
Output: the machine to allocate
For i=1 to all resources
Load (i) //get load at resource
End
For i=1 to all resources
Taskcompltime (i) // get average task completion time
End
For i=1 to all resources
Taskcomplratio (i) // get task complete ratio
End
For i=1 to all resources
rep (i) // get reputation score
End
For i=1 to all resources
QOS(i)=get_neural_score(distance to resource, rep(i), taskcompltime(i),
taskcomplratio(i),load (i))
End
Sort resources on QOS in descending order;
Res = machine (QOS (1)); // the machine allocated
Return Res.
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5. MATHEMATICAL MODEL OF THE SYSTEM
We design a 3 layer feed forward neural network for QOS scoring as shown in figure 7
Number of input neurons = 5
Number of hidden neurons = 11
Number of output neurons = 1
Fig 7: Feed Forward Neural Network
Each hidden layer neuron can be modelled as in figure 8
Fig 8: Modelling of hidden layer
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With N being 5 in the above case.
For thresholding, we use sigmoid function as shown in Figure 9
Fig 9: Sigmoid function for threshold
Let Xi be the input from the i the neuron
Let Wij be the weight of link between input neuron i and the hidden neuron j.
The input to each hidden neuron j is given as in equation 2
Inj = ------------------ (2)
The output from each hidden neuron is as in equation 3
Outj = Sigmoid ( ) ----------- (3)
Let Mj be the weight of the link from the j th hidden neuron to the single output neuron.
The input to the hidden neuron is modeled as in equation 4
Hj = * ------ (4)
The output from the output neuron is modeled in equation 5
O =sigmoid ( * ) ---(5)
Assume that, we have Z number of input request arriving to cloud broker and there are M
machines.
Each machine is of varied processing capability and distributed at different distances from the
user site.
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For each scheduling decision we calculate the QOS for each of M machines as {Qos1, Qos2,
Qos3… QosM}
We choose the J machine such that
QosJ > ¥ Qos k with J<k<M, k≠J
Once the scheduling is complete, we calculate the average completion time(TC) of each task
as in equation 6
Avg TC = ------------------- -------------- (6)
Z
We calculate the Job Success Ratio (JSR) as in equation 7
JSR = No of jobs completed in expected time
----------------------------------------------- --------- (7)
Z
6. IMPLEMENTATION
The proposed modular architecture is as in figure 10 explained below:
Fig 10: Proposed Architecture
Coordinated Cloud Manager: The Module helps in registering of the Coordinated Cloud which is
distributed geographically. This module also acquires the heartbeats of the Coordinated Cloud.
Reputation Manager: This Modules helps in collecting census report from the Coordinated Cloud
and it calculates the scoring.
Resource Selection: The resources based on the good reputation score are selected in this module
for task and also select QOS for each task.
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Price Assisted Resource Control: There may be an overload at some nodes because the node’s
resource with good score is always selected. This module helps in selecting the next less
overloaded resource based on price.
Speculative Manager: This module will take care of speculative replication to provide fault
tolerance.
7. PERFORMANCE ANALYSIS
We used Google cluster dataset for modelling the tasks and to test the performance of the
proposed solution. A Google cluster is a set of machines, packed into racks, and connected by a
high-bandwidth cluster network. A cell is a set of machines, typically all in a single cluster that
shares a common cluster-management system that allocates work to machines. Work arrives at a
cell in the form of jobs. A job is comprised of one or more tasks, each of which is accompanied
by a set of resource requirements used for scheduling (packing) the tasks onto machines. Each
task represents a Linux program, possibly consisting of multiple processes, to be run on a single
machine. Tasks and jobs are scheduled onto machines according to the lifecycle described below:
Resource requirements and usage data for tasks are derived from information provided by the
cell's management system and the individual machines in the cell. A single usage trace typically
describes several days of the workload on one of these compute cells. A trace is made up of
several datasets. A dataset contains a single table, indexed by a primary key that typically
includes a timestamp. Each dataset is packaged as a set of one or more files, each provided in a
compressed CSV format.
We generated the tasks for cloudsim from the task table of Google cluster data.
The task resource usage table contains the following fields as in table 1
Table 1: The task resource usage table
Field No Parameter Field
No
Parameter
1 start and
end time
6 assigned memory
2 job ID 7 page cache
memory usage
3 task index 8 cycles per
instruction (CPI)
4 machine ID 9 memory accesses
per instruction
(MAI)
5 memory
usage
10 sampling rate
Using these fields, we create the task list for testing the performance of our solution.
The average success rate and total waiting time is shown in figure 11 and figure 12 respectively.
From this we can observe that success rate is high and total waiting time is reduced as compared
to existing system.
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Fig 11: success rate graph
Fig 12: waiting time graph
The node utilization for the varied number of request rate is measured and from this we see that
utility is high in case of proposed MAQS as in figure 13
Fig 13: Node utilization Graph
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8. CONCLUSION AND ENHANCEMENTS
The previous implementation focused on resource allocation considering one reputation value
ignoring the heterogeneity of resources. Hence the proposed solution for resource allocation is
based on multiple attributes to compute the reputation score focusing on QoS parameter. This
mechanism has better success ratio and completion ratio of the task. In future, we plan to deduce
a mathematical formula to calculate the optimal time period to train the feed forward neural
network to improve on QoS parameters considering more than one parameter.
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