This document discusses context-aware mobile cloud computing and its challenges. It defines mobile cloud computing as integrating cloud computing technology with mobile devices to enhance their computational power, memory, storage, energy, and context awareness. It discusses two types of mobile cloud computing architectures - infrastructure-based systems where cloud hardware provides services to mobile users, and ad hoc systems where mobile devices form groups to share services. The document highlights the need for context-aware mobile cloud application development models that support computation offloading between smartphones and clouds. It discusses challenges like determining when offloading improves performance or saves energy given factors like available resources and bandwidth.
A methodology for model driven multiplatform mobile application developmentIAEME Publication
This document describes a methodology for developing mobile applications across multiple platforms using model-driven development. The methodology utilizes domain-specific modeling languages to define application logic, data structures, communication, and user interfaces in a platform-independent way. Model processors then generate executable code for different mobile platforms from these models. The generated code leverages platform-specific libraries and frameworks to optimize for energy efficiency on mobile devices. Some computation tasks are also offloaded to the cloud to further improve efficiency. The goal is to develop high-quality, energy-efficient mobile applications that can be maintained consistently across multiple platforms.
This document summarizes a research paper on mobile cloud computing. It begins with definitions of mobile cloud computing, discussing how it combines mobile computing and cloud computing. It then describes the general architecture of mobile cloud computing and some of its key advantages, such as extending battery life, improving data storage and processing power, and improving reliability. Several applications of mobile cloud computing are discussed, including mobile commerce, mobile learning, and mobile healthcare. Potential limitations around cloud service costs, mobile network costs, availability, and security are also outlined. The document concludes by discussing future research directions, such as overcoming low bandwidth issues through 4G networks and femtocells.
With a rapid growth of the mobile applications and development of cloud computing concept, mobile cloud
computing (MCC) has been introduced to be a potential technology for mobile services. MCC integrates the cloud
computing into the mobile environment and overcomes obstacles related to the performance, security etc discussed in
mobile computing. This paper gives an overview of the MCC including the definition, architecture, and applications. The
issues, existing solutions and approaches are presented.
Mobile cloud computing implications and challengesAlexander Decker
This document discusses mobile cloud computing, which integrates cloud computing with mobile environments. It describes how mobile devices and cloud computing can be combined to provide opportunities for mobile applications and services. The key aspects covered include:
1. An overview of cloud computing models including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS).
2. How mobile cloud computing works by moving data storage and processing to centralized cloud platforms accessed over the mobile internet.
3. The importance of mobile cloud applications for areas like mobile commerce, education, healthcare, and more.
QoS-Aware Middleware for Optimal Service Allocation in Mobile Cloud ComputingReza Rahimi
- The document discusses QoS-aware middleware for optimal service allocation in mobile cloud computing.
- It proposes a 2-tier cloud architecture consisting of local clouds and public clouds and develops algorithms to optimally allocate services for mobile users across these tiers.
- A location-time workflow model is used to represent mobile applications and QoS metrics like delay, power consumption and price are considered for optimal service allocation.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Mobile Cloud Computing MCC which consolidates versatile processing and distributed computing, has turned out to be one of the business trendy expressions and a noteworthy dialog string in the IT world since 2009. As MCC is still at the beginning period of improvement, it is important to get a handle on an exhaustive comprehension of the innovation so as to call attention to the course of future research. With the last point, this paper introduces a survey on the foundation and standard of MCC, attributes, ongoing examination work, and future research patterns. A concise record on the foundation of MCC from portable processing to distributed computing is given and after that pursued an exchange on attributes and ongoing exploration work. It at that point examinations the highlights and framework of versatile distributed computing. The remainder of the paper investigations the difficulties of versatile distributed computing, rundown of some examination ventures identified with this territory, and calls attention to promising future research bearings. Sumit | Ms. Kirti Bhatia | Ms. Shalini Bhadola ""Cloud Computing using Mobile Phone"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23145.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23145/cloud-computing-using-mobile-phone/sumit
IRJET- Achieving Load Balancing Between Privacy Protection Level and Power Co...IRJET Journal
This document discusses achieving a balance between privacy protection and power consumption for location-based services (LBS). It proposes a customized power consumption model to address this tradeoff. The model is based on six factors that influence power usage on smartphones running LBS apps: backlight, CPU, WiFi, memory, bandwidth, and GPS. The execution time of different privacy protection methods can adjust the power consumed by each factor. The model is tested on dummy-based privacy approaches to determine the best k-anonymity value while managing power consumption.
A methodology for model driven multiplatform mobile application developmentIAEME Publication
This document describes a methodology for developing mobile applications across multiple platforms using model-driven development. The methodology utilizes domain-specific modeling languages to define application logic, data structures, communication, and user interfaces in a platform-independent way. Model processors then generate executable code for different mobile platforms from these models. The generated code leverages platform-specific libraries and frameworks to optimize for energy efficiency on mobile devices. Some computation tasks are also offloaded to the cloud to further improve efficiency. The goal is to develop high-quality, energy-efficient mobile applications that can be maintained consistently across multiple platforms.
This document summarizes a research paper on mobile cloud computing. It begins with definitions of mobile cloud computing, discussing how it combines mobile computing and cloud computing. It then describes the general architecture of mobile cloud computing and some of its key advantages, such as extending battery life, improving data storage and processing power, and improving reliability. Several applications of mobile cloud computing are discussed, including mobile commerce, mobile learning, and mobile healthcare. Potential limitations around cloud service costs, mobile network costs, availability, and security are also outlined. The document concludes by discussing future research directions, such as overcoming low bandwidth issues through 4G networks and femtocells.
With a rapid growth of the mobile applications and development of cloud computing concept, mobile cloud
computing (MCC) has been introduced to be a potential technology for mobile services. MCC integrates the cloud
computing into the mobile environment and overcomes obstacles related to the performance, security etc discussed in
mobile computing. This paper gives an overview of the MCC including the definition, architecture, and applications. The
issues, existing solutions and approaches are presented.
Mobile cloud computing implications and challengesAlexander Decker
This document discusses mobile cloud computing, which integrates cloud computing with mobile environments. It describes how mobile devices and cloud computing can be combined to provide opportunities for mobile applications and services. The key aspects covered include:
1. An overview of cloud computing models including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS).
2. How mobile cloud computing works by moving data storage and processing to centralized cloud platforms accessed over the mobile internet.
3. The importance of mobile cloud applications for areas like mobile commerce, education, healthcare, and more.
QoS-Aware Middleware for Optimal Service Allocation in Mobile Cloud ComputingReza Rahimi
- The document discusses QoS-aware middleware for optimal service allocation in mobile cloud computing.
- It proposes a 2-tier cloud architecture consisting of local clouds and public clouds and develops algorithms to optimally allocate services for mobile users across these tiers.
- A location-time workflow model is used to represent mobile applications and QoS metrics like delay, power consumption and price are considered for optimal service allocation.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Mobile Cloud Computing MCC which consolidates versatile processing and distributed computing, has turned out to be one of the business trendy expressions and a noteworthy dialog string in the IT world since 2009. As MCC is still at the beginning period of improvement, it is important to get a handle on an exhaustive comprehension of the innovation so as to call attention to the course of future research. With the last point, this paper introduces a survey on the foundation and standard of MCC, attributes, ongoing examination work, and future research patterns. A concise record on the foundation of MCC from portable processing to distributed computing is given and after that pursued an exchange on attributes and ongoing exploration work. It at that point examinations the highlights and framework of versatile distributed computing. The remainder of the paper investigations the difficulties of versatile distributed computing, rundown of some examination ventures identified with this territory, and calls attention to promising future research bearings. Sumit | Ms. Kirti Bhatia | Ms. Shalini Bhadola ""Cloud Computing using Mobile Phone"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23145.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23145/cloud-computing-using-mobile-phone/sumit
IRJET- Achieving Load Balancing Between Privacy Protection Level and Power Co...IRJET Journal
This document discusses achieving a balance between privacy protection and power consumption for location-based services (LBS). It proposes a customized power consumption model to address this tradeoff. The model is based on six factors that influence power usage on smartphones running LBS apps: backlight, CPU, WiFi, memory, bandwidth, and GPS. The execution time of different privacy protection methods can adjust the power consumed by each factor. The model is tested on dummy-based privacy approaches to determine the best k-anonymity value while managing power consumption.
PROCEDURE OF EFFECTIVE USE OF CLOUDLETS IN WIRELESS METROPOLITAN AREA NETWORK...IJCNCJournal
The article develops a method to ensure the efficient use of cloudlet resources by the mobile users. The article provides a solution to the problem of correct use of cloudlets located on the movement route of mobile users in Wireless Metropolitan Area Networks - WMAN environment. Conditions for downloading
necessary applications to the appropriate cloudlet using the possible values that determine the importance and coordinates of the cloudlets were studied. The article provides a model of the mobile user's route model in metropolitan environments and suggests a method for solving the problem.
This document discusses end-to-end security in mobile cloud computing. It defines mobile cloud computing and explains its advantages over mobile devices alone. The document outlines challenges to end-to-end security in service-oriented architectures and mobile cloud computing. It proposes a security framework that uses taint analysis and aspect-oriented programming to monitor service executions and detect unauthorized external service invocations. A trust broker would maintain trust sessions and evaluate the trustworthiness of services to ensure end-to-end security.
Mobile cloud computing aims to augment the capabilities of mobile devices by moving data processing and storage to powerful centralized cloud platforms. This conserves local resources on mobile devices while extending storage capacity and enhancing data security. Key challenges include the limited capabilities of mobile devices, quality of communication given changing network conditions and disconnections, and how to effectively divide applications between mobile and cloud resources. Research is still needed to address task division, data delivery, quality of service standards, and providing suitable interactive services for mobile devices within this environment.
starts with an introduction to mobile cloud computing with a definition, architecture, and advantages/disadvantages. At the next sections, continues with the applications of MCC, detailed challenges in mobile environment and solutions. Lastly the document concludes the main issues about the mobile cloud computing with the conclusion part.
M2C2: A Mobility Management System For Mobile Cloud ComputingKaran Mitra
Mobile devices have become an integral part of our daily lives. Applications
running on these devices may avail storage and compute resources from
the cloud(s). Further, a mobile device may also connect to heterogeneous
access networks (HANs) such as WiFi and LTE to provide ubiquitous
network connectivity to mobile applications. These devices have limited
resources (compute, storage and battery) that may lead to service
disruptions. In this context, mobile cloud computing enables offloading
of computing and storage to the cloud. However, applications running
on mobile devices using clouds and HANs are prone to unpredictable
cloud workloads, network congestion and handoffs. To run these applications
efficiently the mobile device requires the best possible cloud and
network resources while roaming in HANs. This paper proposes, develops
and validates a novel system called M2C2 which supports mechanisms
for: i.) multihoming, ii.) cloud and network probing, and iii.) cloud
and network selection. We built a prototype system and performed extensive
experimentation to validate our proposed M2C2. Our results
analysis shows that the proposed system supports mobility efficiently
in mobile cloud computing.
Paper can be downloaded from: http://karanmitra.me/wp-content/uploads/2015/02/MitraetalLTUWCNC_Preprint2015.pdf
Self-tuning data centers aim to minimize human intervention through machine learning techniques. Current challenges include meeting service level agreements for performance and uptime while maximizing efficiency of resources and minimizing costs. A self-tuning architecture uses monitoring data to detect issues and make recommendations for scaling, migration, or tuning of resources without human input. This approach aims to optimize data centers so they can scale efficiently to support growing workloads and applications.
This document discusses mobile cloud computing (MCC). It defines MCC as infrastructure where data storage and processing occur outside the mobile device. MCC provides advantages to mobile devices with limited resources by offering cloud services elastically. The document outlines the MCC architecture and describes how mobile requests are processed in the cloud. It lists applications of MCC like mobile commerce, healthcare and gaming. Issues with MCC like bandwidth, availability and security are also covered. In conclusion, MCC combines advantages of mobile and cloud computing to provide opportunities for mobile business.
Mobile cloud computing combines mobile web and cloud computing to address limitations of the mobile web like limited storage, small screens, and unreliable browsers/connections. It takes data processing off mobile devices and into the cloud, creating a common platform across devices. While mobile cloud computing currently has under 1 billion subscribers, its potential is high given over 5 billion mobile subscribers globally, especially in Africa where it could provide widespread access to information and resources.
The document discusses mobile cloud computing trends and applications. It notes that mobile cloud computing involves storing data and processing outside mobile devices. This allows mobile devices to have richer capabilities. The document outlines several applications of mobile cloud computing including for enterprises, developers, healthcare, automotive, education, and consumer electronics. It also discusses challenges of mobile cloud computing like mobility constraints, bandwidth limitations, and security risks.
Mobile cloud computing (MCC) refers to an infrastructure where data storage and processing occur remotely on powerful centralized cloud servers, rather than locally on mobile devices. This alleviates issues like limited battery, storage, and bandwidth on mobile devices. MCC provides advantages like lower costs, greater scalability, reliability, and availability of data and applications stored in the cloud. Popular MCC applications include mobile commerce, healthcare, gaming and more. Key challenges include low bandwidth, service availability, and computation offloading in dynamic environments. Security issues involve protecting user privacy and securing data in the cloud.
This document summarizes mobile cloud computing. It defines mobile cloud computing as combining cloud computing, mobile computing, and wireless networks to provide rich computational resources to mobile users. It describes the advantages of mobile cloud computing in providing data storage, processing, and access from anywhere. It outlines the basic architecture and discusses hierarchical architectures using "cloudlets" to improve performance. It also discusses issues like limited mobile devices and connection quality, and proposes solutions like optimizing application distribution and developing network infrastructure around cloudlets.
Mobile Applications on an Elastic and Scalable 2-Tier Cloud ArchitectureReza Rahimi
The document proposes MAPCloud, a mobile application platform that uses a two-tier cloud architecture. The first tier is a public cloud for scalability. The second tier uses local clouds or cloudlets for lower latency. It formulates the resource allocation problem and presents CRAM, a heuristic algorithm that combines simulated annealing and greedy approaches to optimize allocation for quality of service factors like price, power usage, and delay. CRAM also uses R-trees to efficiently retrieve nearby cloud services.
Geochronos File Sharing Application Using CloudIJERA Editor
Accessing, running and sharing applications and data at present face many challenges. Cloud Computing and Social Networking technologies have the potential to simplify or eliminate many of these challenges. Social Networking technologies provide a means for easily sharing applications and data. Now a day’s people want to be connected 24x7 to the world around them. Networking and Communication have come together to make the world a small place to live in. People want to be in constant touch with their subordinates where ever they are and avail emergency services whenever needed. In this paper we present an on-line/on-demand interactive application service (Software as a Service). The service is built on a cloud computing basement that provisions virtualized application servers based on user demand. An open source social networking platform is leveraged to establish a portal front-end that enables applications and results to be easily shared between users. In the proposed system users can access the documents uploaded into the cloud by others and provide any data they have in hand to other users through the same cloud. This also allows the users to have an interactive session through the chat screens present in the cloud. The paper also highlights some major security issues existing in current cloud computing environment.
Mobile Cloud: Security Issues and Challenges discusses security concerns with mobile cloud computing. It outlines the evolution of cloud computing and features of mobile cloud computing. The document then discusses challenges such as bandwidth limitations and security issues including data ownership, privacy, and data security. Existing solutions and possible solutions to security issues are presented, along with a conclusion emphasizing the need for data security plans and addressing threats to attain more reliable and cost-effective mobile cloud computing.
This document discusses how mobile cloud computing can help businesses become more mobile. It covers topics like mobile-centric applications, social and contextual experiences, application stores, the Internet of everything, analytics, big data, and cloud computing. The document then summarizes the winners of various categories in mobile apps and technologies.
Mobile cloud computing (MCC) at its simplest, refers to an infrastructure where both the data storage and data processing happen outside of the mobile device.
Mobile cloud computing (MCC) at its simplest, refers to an infrastructure where both the data storage and data processing happen outside of the mobile device.
Mobile cloud computing (MCC) at its simplest, refers to an infrastructure where both the data storage and data processing happen outside of the mobile device.
This is a small and simple Presentation on the topic Mobile Cloud Computing Made for a Symposium. The content inside the slides are taken from Google and various research papers, this slide is purely for educational purpose and not meant for commercial publication.
A survey of fog computing concepts applications and issuesRezgar Mohammad
This document provides a survey of fog computing that discusses its key concepts, applications, and issues. It defines fog computing as a scenario that provides computation, storage, and networking services between end devices and cloud servers at the edge of the network. Representative applications of fog computing discussed include augmented reality, real-time video analytics, content delivery/caching, and mobile big data analytics. Potential issues covered include fog networking, quality of service concerns regarding connectivity, reliability, and capacity, and resource management challenges in dynamically provisioning and scheduling resources across fog nodes.
Gearing up of resource poor mobile devices using cloudamelpakkath
This document discusses mobile cloud computing and outlines a project on gearing resource-poor mobile devices with powerful clouds. It introduces three common mobile cloud architectures: centralized cloud, cloudlet, and ad hoc mobile cloud. It also discusses computation offloading and capability extending. The project modules are described as an energy-saving module using content-based image retrieval offloaded to the cloud, a computation offloading module, and a security and reliability module. It concludes that mobile cloud computing can change technology trends and daily life by allowing more intensive applications on mobile devices.
ABSTRACT
In today’s world, the swift increase of utilizing mobile services and simultaneously discovering of the cloud computing services, made the Mobile Cloud Computing (MCC) selected as a wide spread technology among mobile users. Thus, the MCC incorporates the cloud computing with mobile services for achieving facilities in daily using mobile. The capability of mobile devices is limited of computation context, memory capacity, storage ability, and energy. Thus, relying on cloud computing can handle these troubles in the mobile surroundings. Cloud Computing gives computing easiness and capacity such provides availability of services from anyplace through the Internet without putting resources into new foundation, preparing, or application authorizing. Additionally, Cloud Computing is an approach to expand the limitations or increasing the abilities dynamically. The primary favourable position of Cloud Computing is that clients just use what they require and pay for what they truly utilize. Mobile cloud computing is a form for various services, where a mobile gadget is able to utilize the cloud for data saving, seeking, information mining, and multimedia preparing. Cloud computing innovation is also causes many new complications in side of safety and gets to direct when users store significant information with cloud servers. As the clients never again have physical ownership of the outsourced information, makes the information trustworthiness, security, and authenticity insurance in Cloud Computing is extremely difficult and conceivably troublesome undertaking. In MCC environments, it is hard to find a paper embracing most of the concepts and issues such as: architecture, computational offloading, challenges, security issues, authentications and so on. In this paper we discuss these concepts with presenting a review of the most recent papers in the domain of MCC.
PROCEDURE OF EFFECTIVE USE OF CLOUDLETS IN WIRELESS METROPOLITAN AREA NETWORK...IJCNCJournal
The article develops a method to ensure the efficient use of cloudlet resources by the mobile users. The article provides a solution to the problem of correct use of cloudlets located on the movement route of mobile users in Wireless Metropolitan Area Networks - WMAN environment. Conditions for downloading
necessary applications to the appropriate cloudlet using the possible values that determine the importance and coordinates of the cloudlets were studied. The article provides a model of the mobile user's route model in metropolitan environments and suggests a method for solving the problem.
This document discusses end-to-end security in mobile cloud computing. It defines mobile cloud computing and explains its advantages over mobile devices alone. The document outlines challenges to end-to-end security in service-oriented architectures and mobile cloud computing. It proposes a security framework that uses taint analysis and aspect-oriented programming to monitor service executions and detect unauthorized external service invocations. A trust broker would maintain trust sessions and evaluate the trustworthiness of services to ensure end-to-end security.
Mobile cloud computing aims to augment the capabilities of mobile devices by moving data processing and storage to powerful centralized cloud platforms. This conserves local resources on mobile devices while extending storage capacity and enhancing data security. Key challenges include the limited capabilities of mobile devices, quality of communication given changing network conditions and disconnections, and how to effectively divide applications between mobile and cloud resources. Research is still needed to address task division, data delivery, quality of service standards, and providing suitable interactive services for mobile devices within this environment.
starts with an introduction to mobile cloud computing with a definition, architecture, and advantages/disadvantages. At the next sections, continues with the applications of MCC, detailed challenges in mobile environment and solutions. Lastly the document concludes the main issues about the mobile cloud computing with the conclusion part.
M2C2: A Mobility Management System For Mobile Cloud ComputingKaran Mitra
Mobile devices have become an integral part of our daily lives. Applications
running on these devices may avail storage and compute resources from
the cloud(s). Further, a mobile device may also connect to heterogeneous
access networks (HANs) such as WiFi and LTE to provide ubiquitous
network connectivity to mobile applications. These devices have limited
resources (compute, storage and battery) that may lead to service
disruptions. In this context, mobile cloud computing enables offloading
of computing and storage to the cloud. However, applications running
on mobile devices using clouds and HANs are prone to unpredictable
cloud workloads, network congestion and handoffs. To run these applications
efficiently the mobile device requires the best possible cloud and
network resources while roaming in HANs. This paper proposes, develops
and validates a novel system called M2C2 which supports mechanisms
for: i.) multihoming, ii.) cloud and network probing, and iii.) cloud
and network selection. We built a prototype system and performed extensive
experimentation to validate our proposed M2C2. Our results
analysis shows that the proposed system supports mobility efficiently
in mobile cloud computing.
Paper can be downloaded from: http://karanmitra.me/wp-content/uploads/2015/02/MitraetalLTUWCNC_Preprint2015.pdf
Self-tuning data centers aim to minimize human intervention through machine learning techniques. Current challenges include meeting service level agreements for performance and uptime while maximizing efficiency of resources and minimizing costs. A self-tuning architecture uses monitoring data to detect issues and make recommendations for scaling, migration, or tuning of resources without human input. This approach aims to optimize data centers so they can scale efficiently to support growing workloads and applications.
This document discusses mobile cloud computing (MCC). It defines MCC as infrastructure where data storage and processing occur outside the mobile device. MCC provides advantages to mobile devices with limited resources by offering cloud services elastically. The document outlines the MCC architecture and describes how mobile requests are processed in the cloud. It lists applications of MCC like mobile commerce, healthcare and gaming. Issues with MCC like bandwidth, availability and security are also covered. In conclusion, MCC combines advantages of mobile and cloud computing to provide opportunities for mobile business.
Mobile cloud computing combines mobile web and cloud computing to address limitations of the mobile web like limited storage, small screens, and unreliable browsers/connections. It takes data processing off mobile devices and into the cloud, creating a common platform across devices. While mobile cloud computing currently has under 1 billion subscribers, its potential is high given over 5 billion mobile subscribers globally, especially in Africa where it could provide widespread access to information and resources.
The document discusses mobile cloud computing trends and applications. It notes that mobile cloud computing involves storing data and processing outside mobile devices. This allows mobile devices to have richer capabilities. The document outlines several applications of mobile cloud computing including for enterprises, developers, healthcare, automotive, education, and consumer electronics. It also discusses challenges of mobile cloud computing like mobility constraints, bandwidth limitations, and security risks.
Mobile cloud computing (MCC) refers to an infrastructure where data storage and processing occur remotely on powerful centralized cloud servers, rather than locally on mobile devices. This alleviates issues like limited battery, storage, and bandwidth on mobile devices. MCC provides advantages like lower costs, greater scalability, reliability, and availability of data and applications stored in the cloud. Popular MCC applications include mobile commerce, healthcare, gaming and more. Key challenges include low bandwidth, service availability, and computation offloading in dynamic environments. Security issues involve protecting user privacy and securing data in the cloud.
This document summarizes mobile cloud computing. It defines mobile cloud computing as combining cloud computing, mobile computing, and wireless networks to provide rich computational resources to mobile users. It describes the advantages of mobile cloud computing in providing data storage, processing, and access from anywhere. It outlines the basic architecture and discusses hierarchical architectures using "cloudlets" to improve performance. It also discusses issues like limited mobile devices and connection quality, and proposes solutions like optimizing application distribution and developing network infrastructure around cloudlets.
Mobile Applications on an Elastic and Scalable 2-Tier Cloud ArchitectureReza Rahimi
The document proposes MAPCloud, a mobile application platform that uses a two-tier cloud architecture. The first tier is a public cloud for scalability. The second tier uses local clouds or cloudlets for lower latency. It formulates the resource allocation problem and presents CRAM, a heuristic algorithm that combines simulated annealing and greedy approaches to optimize allocation for quality of service factors like price, power usage, and delay. CRAM also uses R-trees to efficiently retrieve nearby cloud services.
Geochronos File Sharing Application Using CloudIJERA Editor
Accessing, running and sharing applications and data at present face many challenges. Cloud Computing and Social Networking technologies have the potential to simplify or eliminate many of these challenges. Social Networking technologies provide a means for easily sharing applications and data. Now a day’s people want to be connected 24x7 to the world around them. Networking and Communication have come together to make the world a small place to live in. People want to be in constant touch with their subordinates where ever they are and avail emergency services whenever needed. In this paper we present an on-line/on-demand interactive application service (Software as a Service). The service is built on a cloud computing basement that provisions virtualized application servers based on user demand. An open source social networking platform is leveraged to establish a portal front-end that enables applications and results to be easily shared between users. In the proposed system users can access the documents uploaded into the cloud by others and provide any data they have in hand to other users through the same cloud. This also allows the users to have an interactive session through the chat screens present in the cloud. The paper also highlights some major security issues existing in current cloud computing environment.
Mobile Cloud: Security Issues and Challenges discusses security concerns with mobile cloud computing. It outlines the evolution of cloud computing and features of mobile cloud computing. The document then discusses challenges such as bandwidth limitations and security issues including data ownership, privacy, and data security. Existing solutions and possible solutions to security issues are presented, along with a conclusion emphasizing the need for data security plans and addressing threats to attain more reliable and cost-effective mobile cloud computing.
This document discusses how mobile cloud computing can help businesses become more mobile. It covers topics like mobile-centric applications, social and contextual experiences, application stores, the Internet of everything, analytics, big data, and cloud computing. The document then summarizes the winners of various categories in mobile apps and technologies.
Mobile cloud computing (MCC) at its simplest, refers to an infrastructure where both the data storage and data processing happen outside of the mobile device.
Mobile cloud computing (MCC) at its simplest, refers to an infrastructure where both the data storage and data processing happen outside of the mobile device.
Mobile cloud computing (MCC) at its simplest, refers to an infrastructure where both the data storage and data processing happen outside of the mobile device.
This is a small and simple Presentation on the topic Mobile Cloud Computing Made for a Symposium. The content inside the slides are taken from Google and various research papers, this slide is purely for educational purpose and not meant for commercial publication.
A survey of fog computing concepts applications and issuesRezgar Mohammad
This document provides a survey of fog computing that discusses its key concepts, applications, and issues. It defines fog computing as a scenario that provides computation, storage, and networking services between end devices and cloud servers at the edge of the network. Representative applications of fog computing discussed include augmented reality, real-time video analytics, content delivery/caching, and mobile big data analytics. Potential issues covered include fog networking, quality of service concerns regarding connectivity, reliability, and capacity, and resource management challenges in dynamically provisioning and scheduling resources across fog nodes.
Gearing up of resource poor mobile devices using cloudamelpakkath
This document discusses mobile cloud computing and outlines a project on gearing resource-poor mobile devices with powerful clouds. It introduces three common mobile cloud architectures: centralized cloud, cloudlet, and ad hoc mobile cloud. It also discusses computation offloading and capability extending. The project modules are described as an energy-saving module using content-based image retrieval offloaded to the cloud, a computation offloading module, and a security and reliability module. It concludes that mobile cloud computing can change technology trends and daily life by allowing more intensive applications on mobile devices.
ABSTRACT
In today’s world, the swift increase of utilizing mobile services and simultaneously discovering of the cloud computing services, made the Mobile Cloud Computing (MCC) selected as a wide spread technology among mobile users. Thus, the MCC incorporates the cloud computing with mobile services for achieving facilities in daily using mobile. The capability of mobile devices is limited of computation context, memory capacity, storage ability, and energy. Thus, relying on cloud computing can handle these troubles in the mobile surroundings. Cloud Computing gives computing easiness and capacity such provides availability of services from anyplace through the Internet without putting resources into new foundation, preparing, or application authorizing. Additionally, Cloud Computing is an approach to expand the limitations or increasing the abilities dynamically. The primary favourable position of Cloud Computing is that clients just use what they require and pay for what they truly utilize. Mobile cloud computing is a form for various services, where a mobile gadget is able to utilize the cloud for data saving, seeking, information mining, and multimedia preparing. Cloud computing innovation is also causes many new complications in side of safety and gets to direct when users store significant information with cloud servers. As the clients never again have physical ownership of the outsourced information, makes the information trustworthiness, security, and authenticity insurance in Cloud Computing is extremely difficult and conceivably troublesome undertaking. In MCC environments, it is hard to find a paper embracing most of the concepts and issues such as: architecture, computational offloading, challenges, security issues, authentications and so on. In this paper we discuss these concepts with presenting a review of the most recent papers in the domain of MCC.
ENERGY EFFICIENT COMPUTING FOR SMART PHONES IN CLOUD ASSISTED ENVIRONMENTIJCNCJournal
In recent years, the employment of smart mobile phones has increased enormously and are concerned as an area of human life. Smartphones are capable to support immense range of complicated and intensive applications results shortened power capability and fewer performance. Mobile cloud computing is the newly rising paradigm integrates the features of cloud computing and mobile computing to beat the constraints of mobile devices. Mobile cloud computing employs computational offloading that migrates the computations from mobile devices to remote servers. In this paper, a novel model is proposed for dynamic task offloading to attain the energy optimization and better performance for mobile applications in the cloud environment. The paper proposed an optimum offloading algorithm by introducing new criteria such as benchmarking for offloading decision making. It also supports the concept of partitioning to divide the computing problem into various sub-problems. These sub-problems can be executed parallelly on mobile device and cloud. Performance evaluation results proved that the proposed model can reduce around 20% to 53% energy for low complexity problems and up to 98% for high complexity problems.
Cloud-based augmentation for mobile devices: Motivation, Taxonomy, and Open C...Saeid Abolfazli
Comprehensive Survey on Mobile Cloud Computing. The paper abstract is here:
Recently, Cloud-based Mobile Augmentation (CMA) approaches have gained remarkable ground from academia and industry. CMA is the state-of-the-art mobile augmentation model that employs resource-rich clouds to increase, enhance, and optimize computing capabilities of mobile devices aiming at execution of resource-intensive mobile applications. Augmented mobile devices envision to perform extensive computations and to store big data beyond their intrinsic capabilities with least footprint and vulnerability. Researchers utilize varied cloud-based computing resources (e.g., distant clouds and nearby mobile nodes) to meet various computing requirements of mobile users. However, employing cloud-based computing resources is not a straightforward panacea. Comprehending critical factors (e.g., current state of mobile client and remote resources) that impact on augmentation process and optimum selection of cloud-based resource types are some challenges that hinder CMA adaptability. This paper comprehensively surveys the mobile augmentation domain and presents taxonomy of CMA approaches. The objectives of this study is to highlight the effects of remote resources on the quality and reliability of augmentation processes and discuss the challenges and opportunities of employing varied cloud-based resources in augmenting mobile devices. We present augmentation definition, motivation, and taxonomy of augmentation types, including traditional and cloud-based. We critically analyze the state-of-the-art CMA approaches and classify them into four groups of distant fixed, proximate fixed, proximate mobile, and hybrid to present a taxonomy. Vital decision making and performance limitation factors that influence on the adoption of CMA approaches are introduced and an exemplary decision making flowchart for future CMA approaches are presented. Impacts of CMA approaches on mobile computing is discussed and open challenges are presented as the future research directions.
Optimizing Using the Offloading Technique and Dynamic Computation in the Mobi...BRNSSPublicationHubI
This document summarizes a research article about optimizing mobile cloud computing through computation offloading techniques. It discusses how offloading tasks from mobile devices to remote cloud servers can optimize system performance, minimize battery usage, and allow resource-intensive apps to run. The proposed approach uses a task scheduler to minimize energy consumption on both private and public clouds by evaluating whether to execute tasks locally or remotely. Experimental results show interfaces for uploading/downloading files and viewing resource availability for offloaded tasks. In conclusion, computation offloading overcomes the limited resources of mobile devices by leveraging cloud computing resources.
This document summarizes a research paper on developing a mobile application called MeghaOS that allows users to access and run desktop applications hosted on cloud infrastructure directly from their smartphones. The application was created to bridge the gap between limited smartphone capabilities and more powerful desktop applications. It implements an interface to Amazon Web Services that allows users to launch virtual machine instances, install and run web applications, and access them through their smartphone. The paper outlines the components of the MeghaOS application, proposed methodology, implementation details and screenshots. It also compares MeghaOS to other mobile cloud applications and file sync services.
A Review And Research Towards Mobile Cloud ComputingSuzanne Simmons
This document provides an overview of mobile cloud computing (MCC), including its advantages and challenges. MCC integrates cloud computing with mobile environments to provide mobile users access to rich computing resources and applications. Key advantages include extending battery life by offloading processing to cloud servers, improving data storage capacity and processing power by storing data in the cloud, and improving reliability through data backup in the cloud. However, challenges exist due to limitations of mobile devices like processing power, storage and battery life. Additionally, the quality of wireless communication introduces issues like variable bandwidth and delays. Dividing applications between mobile devices and cloud servers also requires optimization techniques to determine the most efficient distribution of processing tasks.
Abstract: Cloud computing is a latest trend and a hot topic in today global world. In which sources are provided to concern as local user on an on demand basically as usual it provides the path or means of internet. Mobile cloud computing is simply cloud computing throughout that at all smallest variety of devices could be involved as wireless equipment this paper concern multiple procedure and procedure for the mobile cloud computing . It developed every General mobile cloud computing solution and application specific solution. It also concern about the cloud computing in which mobile phones are used to browse the web, write e-mails, videos etc. Mobile phones are become the universal interface online services and cloud computing application general run local on mobile phones.
Contemporary Energy Optimization for Mobile and Cloud Environmentijceronline
Cloud and mobile computing applications are increasing heavily in terms of usage. These two areas extending usability of systems. This review paper gives information about cloud and mobile applications in terms of resources they consume and the need of choosing variety of features for users from several locations and the evolutionary provisions for service provider and end users. Both the fields are combined to provide good functionality, efficiency and effectiveness with mobile phones. The enhancement by considering power consumption by means of resource constrained nature of devices, communication media and cost effectiveness. This paper discuss about the concepts related to power consumption, underlying protocols and the other performance issues
This document summarizes techniques for improving the energy efficiency of mobile devices in cloud environments. It discusses hardware approaches like improving processors and batteries as well as software approaches including energy-aware operating systems, applications that reduce resource requirements, and resource-aware computing. It also describes approaches that involve offloading computation and data storage to cloud resources to conserve local resources on mobile devices. In conclusion, it emphasizes that improving battery life is important for mobile devices and discusses how mobile cloud computing can help minimize battery consumption through the use of cloud services.
Mobile Fog: A Programming Model for Large–Scale Applications on the Internet ...HarshitParkar6677
creating a new environment, namely the Internet of Things
(IoT), that enables a wide range of future Internet applications.
In this work, we present Mobile Fog, a high level
programming model for future Internet applications that are
geospatially distributed, large–scale, and latency–sensitive.
We analyze use cases for the programming model with camera
network and connected vehicle applications to show the
efficacy of Mobile Fog. We also evaluate application performance
through simulation.
MOBILE CLOUD COMPUTING –FUTURE OF NEXT GENERATION COMPUTINGijistjournal
Mobile device has become essential part of human life. Apart from call and receive functions, user can access many function in his/her mobile. A user wants everything on his/her mobile device for the ease of work. Some people use tablets instead of laptop or desktop. In this paper, insights into Mobile Cloud Computing (MCC) are presented. First overview of cloud computing system is discussed. Then after architecture of MCC is presented. Some applications based on MCC are also discussed and paper is concluded by exploring the problems and solutions of these in MCC.
IRJET- Resource Management in Mobile Cloud Computing: MSaaS & MPaaS with Femt...IRJET Journal
This document discusses resource management in mobile cloud computing using Mobile Software as a Service (MSaaS) and Mobile Platform as a Service (MPaaS) with femtocell and Wi-Fi networks. It proposes using femtocell and Wi-Fi private cloud networks to overcome mobile performance issues like limited battery life, storage, and bandwidth. MSaaS and MPaaS can further improve quality of service, pricing, and standard interfaces. The document suggests this approach can effectively manage resources and improve the performance of mobile cloud computing.
Secured Communication Model for Mobile Cloud Computingijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Opportunistic job sharing for mobile cloud computingijccsa
Cloud Computing is the evolution of new business era which is covered with many of technologies.These
technology are taking advantage of economies of scale and multi tenancy which are used to decrees the
cost of information technology resources. Many of the organization are eager to reduce their computing
cost through the means of virtualization. This demand of reducing the computing cost and time has led to
the innovation of Cloud Computing. Itenhanced computing through improved deployment and
infrastructure costs and processing time. Mobile computing & its applications in smart phones enable a
new, rich user experience. Due to extreme usage of limited resources in smart phones it create problems
which are battery problems, memory space and CPU. To solve this problem, we propose a dynamic mobile
cloud computing architecture framework to use global resources instead of local resources. In this
proposed framework the usefulness of job sharing workload at runtime reduces the load at the local client
and the dynamic throughput time of the job through Wi-Fi Connectivity.
A secure sharing control framework supporting elastic mobile cloud computing IJECEIAES
In elastic mobile cloud computing (EMCC), mobile devices migrate some computing tasks to the cloud for execution according to current needs and seamlessly and transparently use cloud resources to enhance their functions. First, based on the summary of existing EMCC schemes, a generic EMCC framework is abstracted; it is pointed out that the migration of sensitive modules in the EMCC program can bring security risks such as privacy leakage and information flow hijacking to EMCC; then, a generic framework of elastic mobile cloud computing that incorporates risk management is designed, which regards security risks as a cost of EMCC and ensures that the use of EMCC is. Finally, it is pointed out that the difficulty of risk management lies in risk quantification and sensitive module labeling. In this regard, risk quantification algorithms are designed, an automatic annotation tool for sensitive modules of Android programs is implemented, and the accuracy of the automatic annotation is demonstrated through experiments.
This document discusses mobile cloud computing (MCC), which combines mobile networks and cloud computing. MCC allows mobile users to utilize cloud computing services and resources through mobile devices without requiring powerful local hardware. The document outlines the key components of MCC architecture, including mobile users, mobile operators, internet service providers, and cloud service providers. It also discusses common MCC applications like cloud email, mobile commerce, cloud music, and mobile gaming. The document concludes with characteristics of MCC like flexibility, scalability, broad network access, location independence, and reliability.
This document provides a survey of mobile cloud computing. It discusses the definitions and models of cloud computing including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Mobile cloud computing is defined as using cloud infrastructure for data storage and processing instead of on the mobile device. The document reviews several research papers on mobile cloud computing and discusses challenges like network latency, limited bandwidth, security, and privacy. It proposes architectures and techniques to address these challenges and better utilize mobile cloud computing.
A Proposed Solution to Secure MCC Uprising Issue and Challenges in the Domain...IJERD Editor
The development of cloud computing and mobility,mobile cloud computing has emerged and
become a focus of research. By the means of on-demand self-service and extendibility, it can offer the
infrastructure, platform, and software services in a cloud to mobile users through the mobile network. Security
and privacy are the key issues for mobile cloud computing applications, and still face some enormous
challenges. In order to facilitate this emerging domain, we firstly in brief review the advantages and system
model of mobile cloud computing, and then pay attention to the security and privacy in the mobile cloud
computing. MCC provides a platform where mobile users make use of cloud services on mobile devices. The
use of MCC minimizes the performance, compatibility, and lack of resources issues in mobile computing
environment. By deeply analyzing the security and privacy issues from three aspects: mobile terminal, mobile
network and cloud, we give the current security and privacy approaches. The users of MCC are still below
expectations because of the associated risks in terms of security and privacy. These risks are playing important
role by preventing the organizations to adopt MCC environment. Significant amount of research is in progress in
order to reduce the security concerns but still a lot work has to be done to produce a security prone MCC
environment. This paper presents a comprehensive literature review of MCC and its security issues,challenges
and possible solutions for the security issues.
A methodology for model driven multiplatform mobile application developmentIAEME Publication
This document describes a methodology for developing mobile applications across multiple platforms using model-driven development. The methodology utilizes domain-specific modeling languages to define application logic, data structures, communication, and user interfaces in a platform-independent way. Model processors then generate executable code for different mobile platforms from these models. The generated code leverages platform-specific libraries and frameworks to optimize for energy efficiency on mobile devices. Some computation tasks are also offloaded to the cloud to further improve efficiency. The goal is to develop high-quality, energy-efficient mobile applications that can be maintained consistently across multiple platforms.
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
• On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
AI in customer support Use cases solutions development and implementation.pdfmahaffeycheryld
AI in customer support will integrate with emerging technologies such as augmented reality (AR) and virtual reality (VR) to enhance service delivery. AR-enabled smart glasses or VR environments will provide immersive support experiences, allowing customers to visualize solutions, receive step-by-step guidance, and interact with virtual support agents in real-time. These technologies will bridge the gap between physical and digital experiences, offering innovative ways to resolve issues, demonstrate products, and deliver personalized training and support.
https://www.leewayhertz.com/ai-in-customer-support/#How-does-AI-work-in-customer-support
Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
This presentation is about Food Delivery Systems and how they are developed using the Software Development Life Cycle (SDLC) and other methods. It explains the steps involved in creating a food delivery app, from planning and designing to testing and launching. The slide also covers different tools and technologies used to make these systems work efficiently.
This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
2. M AY/J U N E 201 5 I EEE CLO U D COM PU T I N G 43
system, multiple mobile devices form a group that
acts as a cloud and offers services to other mobile
devices.2
These cloud services can be virtual (group
based) or real, with requests sent to the real cloud
(see Figure 1). Given space limitations, we restrict
our discussion to infrastructure-based architectures.
Whereas the primary objective of cloud comput-
ing is to provide IT resources to businesses in a cost-
effective manner, mobile cloud computing focuses
on overcoming smartphone constraints and enhanc-
ing mobile users’ experience. Recent research has
identified three main benefits of mobile cloud tech-
nology: it enhances smartphone applications’ perfor-
mance by utilizing the computational power of the
resource-rich cloud, makes smartphone applications
energy efficient by reducing computational overhead
on the devices using computation offloading, and
enables smartphones to execute resource-intensive
applications that are unsupported in a resource-
constrained environment.1
Because the two technologies’ objectives dif-
fer, so do their challenges. For instance, in mobile
cloud computing, a mobile device’s limited energy is
an issue, whereas in cloud computing, the supply of
energy is unlimited. Similarly, mobility is an impor-
tant parameter in mobile cloud computing but less
important in cloud computing. Security, however, is
equally important in both technologies.3,4
Mobile cloud computing uses computation
offloading to migrate resource-intensive computation-
al tasks from a smartphone to the cloud. Computation
offloading is missing in traditional mobile application
development models, because smartphone applica-
tions are designed to execute on the smartphone only.
Therefore, mobile cloud computing requires special-
ized mobile cloud application development models
that support computation offloading and execution
of smartphone applications in two environments—
smartphone and cloud5
(see the “Mobile Cloud Ap-
plication Development Models” sidebar for further
discussion). Mobile cloud application models offload
computational tasks to the cloud through a process,
component (module), application, or smartphone
clone image6
that resides in the cloud and facilitates
executing the smartphone’s computational requests.
The application models need to be context aware be-
cause computation offloading isn’t always beneficial
and can cause performance degradation or energy
wastage. This article highlights context-awareness
aspects of mobile cloud application development
models, and presents various challenges to achieving
the required context awareness.
Context-Aware Application Models
There are two perspectives on context awareness
in mobile cloud application models: context-aware
Wi-Fi access point
Infrastructure-based cloud
BTS BTS
BTS BTS
Internet
Mobile device
Mobile device Cellular network
Ad hoc cloud
Mobile device
Mobile device
Mobile device
PC Server
Virtual cloud
Mobile device
Mobile device Mobile device
Mobile device
FIGURE 1. A mobile cloud architecture in which mobile devices offload computations to an infrastructure-based
cloud, virtual cloud, and ad hoc cloud via cellular or Wi-Fi communication link (BTS: base transceiver station).
3. 44 I EEE CLO U D COM PU T I N GW W W.COM PU T ER .O RG /CLO U D COM PU T I N G
MOBILE CLOUD
application partitioning and context-aware computa-
tion offloading.
Context-Aware Application Partitioning
Offloading an entire application to the cloud gen-
erally isn’t a good solution, because the applica-
tion might require smartphone hardware support,
such as GPS and sensors, that isn’t available in the
cloud. Moreover, offloading an application can re-
quire a high amount of communication that might
increase the offloading delay and energy consump-
tion. To overcome this issue, researchers recom-
mend offloading only resource-intensive parts of
an application to the cloud, which can enhance
performance, increase energy efficiency, or support
execution.
To enable this selective offloading, a smartphone
application is partitioned into components to be dis-
tributed between the smartphone and cloud for ex-
ecution. The partitioning can be static (predefined
during development) or dynamic (based on runtime
conditions).7
However, to gain the benefits of mobile
MOBILE CLOUD APPLICATION DEVELOPMENT
MODELS
ost mobile cloud applications are developed
for Android, Windows Mobile, iOS, and Black-
Berry platforms using technologies such as Hadoop,
HTML5, Internet Suspend/Resume, R-OSGi, Stack-
On-Demand (SOD), and Representational State-
Transfer (REST). The applications are either powered
by real cloud instances (Amazon Elastic Compute
Cloud, Microsoft Azure, or Google App Engine) or
virtual cloud service/virtual machine (VM) instances
powered by VMWare Workstation or Oracle VM
VirtualBox.
Currently, mobile cloud applications are tested
in a real environment because there’s no specialized
simulator for this technology. Among other areas,
mobile cloud computing research groups and active
projects are exploring mobile cloud service models,
for example,
• the Mobile Multimedia Cloud Computing Project
at RWTH Aachen University (http://dbis.rwth
-aachen.de/cms/projects/i5cloud);
• mobile cloud middleware and application migra-
tion, such as the Reuse and Migration of Legacy
Applications to Interoperable Cloud Services
(Remics) project at the University of Tartu (http://
mc.cs.ut.ee/mcsite/projects);
• mobile cloud-based context-aware applications
for smart cities, healthcare, and productivity, such
as the Mobile Cloud Computing Group at Univer-
sity College Cork, Ireland (www.ucc.ie/en/mccg/
projects); and
• mobile cloud networks, services, and architec-
tures, such as the MONICA project (http://cordis
.europa.eu/project/rcn/101690_en.html) at the
Community Research and Development Informa-
tion Service (CORDIS).
Mobile cloud applications include mathematical
tools, file indexing, image processing tools, games,
download tools, antivirus tools, rendering and stream-
ing, context-aware health monitoring, smart cities,
and big data analysis. Unfortunately, these applica-
tions are implemented as a proof of concept (for test-
ing purposes only). Applications currently available in
the market have limited support of cloud computing,
where the service is hosted solely in the cloud with
no support of code/application migration. Mobile
cloud applications are expected to be launched in the
market soon. Table A summarizes recent well-known
application models, which are discussed in detail
elsewhere.1–9
References
1. A.R. Khan et al., “A Survey of Mobile Cloud Com-
puting Application Models,“ IEEE Comm. Surveys
Tutorials, vol. 16, no. 1, 2014, pp. 393–413.
2. B.-G. Chun et al., “CloneCloud: Elastic Execution
between Mobile Device and Cloud,” Proc. Int’l
Conf. Computer Systems, 2011, pp. 301–314.
3. X. Zhang et al., “Towards an Elastic Application
Model for Augmenting Computing Capabilities of
Mobile Platforms,” Mobile Wireless Middleware, Op-
4. M AY/J U N E 201 5 I EEE CLO U D COM PU T I N G 45
cloud computing, applications are partitioned in a
context-aware fashion that considers user interaction
frequency, local resource access (for example, GPS, in-
put module, or sensors), resource requirements, com-
putational intensity, and bandwidth consumption.1
Context-Aware Computation Offloading
In context-aware computation offloading, decisions
are made in favor of performance enhancement,
energy efficiency, and application execution. Con-
text awareness is necessary because computation
offloading isn’t always beneficial. To prove this, we
developed three Android-based applications:
• app-1 finds the sum of 0.1 million numbers,
• app-2 cracks a five-character password, and
• app-3 multiplies a 750 × 750 matrix.
All applications were capable of offloading compu-
tations to the Google App Engine (F1 instance) us-
ing HTTP requests via Wi-Fi and 3G connections.
The applications were executed on a Sony Xperia S
erating Systems, and Applications, Springer, 2010,
pp. 161–174.
4. V. March et al., “μCloud: Towards a New Paradigm
of Rich Mobile Applications,” Procedia Computer
Science, 2011, vol. 5, pp. 618–624
5. M. Satyanarayanan et al., “The Case for VM-Based
Cloudlets in Mobile Computing,” IEEE Pervasive
Computing, vol. 8, no. 4, 2009, pp. 14–23.
6. I. Giurgiu et al., “Calling the Cloud: Enabling Mobile
Phones as Interfaces to Cloud Applications,” Proc.
ACM/IFIP/USENIX 10th Int’l Conf. Middleware (Mid-
dleware 09), 2009, pp. 83–102.
7. R.K. Ma, K.T. Lam, and C.-L. Wang, “eXCloud: Trans-
parent Runtime Support for Scaling Mobile Applica-
tions in Cloud,” Proc. Int’l Conf. Cloud and Service
Computing (CSC), 2011, pp. 103–110.
8. E. Cuervo et al., “MAUI: Making Smartphones Last
Longer with Code Offload,” Proc. Int’l Conf. Mo-
bile Systems, Applications, and Services, 2010, pp.
49–62.
9. S. Kosta et al., “ThinkAir: Dynamic Resource Alloca-
tion and Parallel Execution on the Cloud for Mobile
Code Offloading,” Proc. IEEE INFOCOM, 2012, pp.
945–953.
Table A. Current mobile cloud application development models.
Applications model Description
CloneCloud2
Offloads resource-intensive processes from a mobile device to a mobile device clone
that’s maintained on the nearby infrastructure (personal computers or servers) or cloud.
Xinwen Zhang and
colleagues’ model3
Partitions an application into multiple components (weblets), which are executed
locally or offloaded to the cloud, depending on the available local resources and user
preference.
μCloud4
Focuses application development using heterogeneous components (presented as a
directed graph) that can execute on a smartphone, cloud, or both.
Mahadev Satyanarayanan
and colleagues’ model5
Uses an augmented execution technique in which smartphone computations are
offloaded to a VM on a resource-rich computer or group of computers (a cloudlet).
Ioana Giurgiu and
colleagues’ model6
Distributes functional layers among the smartphone and cloud/nearby infrastructure,
and deploys an application’s functional components (bundles) dynamically, based on
optimal deployment analysis.
eXCloud7
Bases computation offloading on VM-instance level and performs on-demand code/
data migration to the cloud.
MAUI8
Uses dynamic application partitioning and supports method-level offloading to the
cloud/nearby infrastructure.
ThinkAir9
Uses an energy model to make context-aware offloading decisions and supports
method-level offloading to a smartphone clone in the cloud.
5. 46 I EEE CLO U D COM PU T I N GW W W.COM PU T ER .O RG /CLO U D COM PU T I N G
MOBILE CLOUD
smartphone to achieve performance and energy ef-
ficiency. All the applications had different computa-
tional complexities and required a variable amount
of data.
When app-1 was executed, it took more time
in terms of communications (offloading) and over-
consumed the smartphone energy than a local ex-
ecution, proving computation offloading to be
unfavorable in terms of energy efficiency and per-
formance enhancement. When app-2 and app-3
were executed, the applications took less time and
consumed less energy than a local execution. Con-
sequently, for app-2 and app-3, the computation
offloading is favorable in terms of energy efficiency
and performance enhancement. However, the per-
formance and energy gain ratio of app-2 and app-3
vary because of the computational complexity and
the amount of data offloaded to the cloud. Hence, to
achieve the required level of performance or energy
efficiency, the offloading decisions must be context
aware or offloading might not provide any benefit.
Context-aware offloading decisions involve vari-
ous entities, as Figure 2 illustrates. The important
context-awareness aspects of mobile cloud comput-
ing are objective awareness, performance awareness,
energy awareness, and resource awareness.
Objective awareness. Because the three main fea-
tures of mobile cloud computing—performance
enhancement, energy efficiency, and execution sup-
port—are interlinked, achieving one objective could
adversely affect the others. For instance, in some cas-
es, computation offloading is favorable for application
execution but unfavorable in terms of energy efficien-
cy or performance enhancement. This phenomenon
depends mainly on the nature of the application and
its model type. Consequently, many models focus on
a particular feature.1
However, a few models support
multiple features (performance enhancement, energy
efficiency, and execution support) at the same time,
where the enhancement ratio of performance to ener-
gy efficiency is set dynamically based on the runtime
conditions or according to the user’s preferences.
Objective awareness is important for computa-
tion offloading because it not only helps to prioritize
a user’s preference for mobile cloud computing fea-
tures but also guides an application model to parti-
tion an application accordingly.
Performance awareness. In general, computation
offloading is favorable in terms of performance en-
hancement when an application’s computational
time on a smartphone is high compared to the sum
of computation offloading time and cloud computa-
tional time. Therefore, to make favorable offloading
decisions, the application models need to be aware of
the task’s local (smartphone-based) and cloud-based
computational time. This information is provided by
the profilers, which are responsible for monitoring the
local and cloud-based executions of a computational
task. Alternatively, in some scenarios, the offloading
decisions are based on the difference between the
available resources of the smartphone and cloud.
Energy awareness. The execution of a resource-
intensive computational task on a smartphone
consumes a considerable amount of energy. Like-
wise, offloading a computational task to the cloud
consumes a smartphone’s energy in terms of com-
putational request preparation, communications
for offloading, and result integration. Computation
offloading is favorable for energy efficiency when
the energy required for smartphone-based compu-
tations is high compared to the energy required for
computation offloading.
Therefore, favorable offloading decisions in this
context depend on the energy awareness of the ap-
plication models (that is, the energy required for lo-
cal execution versus computation offloading). The
required energy information is estimated by the
smartphone energy consumption models.8
For com-
Cloud
Context awareness
Performance
enhancement
Energy efficiency
Execution support
Energy required for smartphone computations
Energy required for computation offloading
Resources available on the smartphone
Resources available in the cloud
Cloud computational time
Smartphone computational time
Computation offloading time
Communication technology
Network, bandwidth, latency
Wi-FiBTS
Mobile
device
FIGURE 2. Entities involved in context-aware computation offloading.
These entities include smartphone resources, communication
technology (Wi-Fi or cellular), network bandwidth, data size and
location, available cloud resources, and application model/computation
offloading technique (BTS: base transceiver station).
6. M AY/J U N E 201 5 I EEE CLO U D COM PU T I N G 47
putation offloading to be energy efficient, the deci-
sion is made based on an application’s energy profile
and an estimate of the amount of energy required
for communications per size of data.
Resource awareness. Computation offloading for
application execution is performed to execute an
application in a resource-constrained environment.
This occurs when the smartphone resources are in-
sufficient for execution or the available resources are
overloaded. Supporting computation offloading in
such scenarios requires resource awareness about the
smartphone and cloud, particularly when offloading
to a virtual cloud.
For example, consider a scenario where a quad-
core smartphone offloads to a virtual cloud with
limited resources that are shared among multiple
users. To make an optimum offloading decision for
the application execution, the application models
use information about available resources at the
smartphone and cloud.
Challenges in Context-Aware Mobile Cloud
Computing
Mobile cloud computing faces many challenges in per-
forming context-aware computation offloading. Here,
we identify and discuss six of the most important chal-
lenges that hamper mobile cloud application models
from making context-aware offloading decisions.
Application Partitioning
As discussed in the previous sections, application
partitioning is critical for computation offloading.
However, identifying resource-intensive components
is a challenge because there’s no hard rule for defin-
ing a component’s intensity. For instance, an applica-
tion component might be resource intensive (in terms
of computational time) for a low processing power
smartphone but not for a high processing power one.
Even if intensity is defined in terms of computa-
tional complexity, application partitioning is still an
issue. For instance, static partitioning and decisions
regarding the components’ execution location is not a
foolproof solution and might fail in a number of sce-
narios.1
Even though dynamic context-aware parti-
tioning has an edge over static partitioning, it requires
timely repartitioning of applications to accommodate
changes caused by the mobile environment and in-
consistently available resources (on a virtual cloud).
Computational Time
A task’s computational time varies based on available
smartphone/cloud resources, the task’s nature, and
the input data’s size. Therefore, to enhance perfor-
mance using mobile cloud computing, the application
must be aware of the task’s computational time on the
smartphone and cloud. The challenge in doing this is
that smartphones have different hardware specifica-
tions and architectures (single core, dual core, and
quad core). Therefore, there’s no predefined compu-
tational time for any application or its components.
We can estimate an application’s worst-case ex-
ecution time, which is a correct solution to some
extent. However, as discussed previously, the ap-
plications can be partitioned dynamically based on
the runtime condition, which can further change
depending on its current environment. Therefore,
estimating the worst-case execution time for every
possible partitioning pattern isn’t an optimal solu-
tion because it might incur a large computational
overhead on the smartphone.
Moreover, the computational time of an applica-
tion (or its components) in the cloud can vary based
on available resources. For instance, in a virtual
cloud environment, a single server/personal computer
is shared among multiple users, and its load varies
from time to time, which ultimately affects execution
time. Furthermore, data input size and type of code
instructions (integer or floating point) are important
factors that can affect a task’s computational time.
Given these factors, estimating the computa-
tional time of an application or its components is
a complex problem. Although this can be partially
resolved by profiling smartphone and cloud-based
executions,9
the overhead of profiling every execu-
tion of a component against variable size data input
is worth consideration.
Computational Energy
To make smartphone applications energy efficient
through mobile cloud computing, the applications
must be aware of the amount of energy required for
smartphone-based application execution and com-
putation offloading. Otherwise, incorrect offloading
decisions can overconsume smartphones’ energy be-
cause of a high amount of communication between
the smartphone and the cloud about offloading.
The challenge is that the amount of energy
consumed by applications depends on the smart-
phone model (hardware type and specifications).
For example, two smartphones with different types
of processors (single core versus quad core) will
consume different amounts of energy. Moreover,
an application’s energy consumption can vary de-
pending on the CPU frequency and utilization level.
The problem is compounded by the fact that
smartphones don’t provide low-level energy infor-
mation for computations and communications. For
7. 48 I EEE CLO U D COM PU T I N GW W W.COM PU T ER .O RG /CLO U D COM PU T I N G
MOBILE CLOUD
instance, in Android OS, developers can only access
information about smartphone battery level (total
battery remaining) and the percentage of smart-
phone energy that’s consumed by a particular ap-
plication (see http://developer.android.com/training/
monitoring-device-state/battery-monitoring.html),
which is insufficient for making energy-aware
offloading decisions.
As proof, we can execute a resource-intensive ap-
plication on a smartphone for 1 minute and compare
the battery level readings before and after the execu-
tion. Most of the time, there’s no change in the read-
ings. Consequently, monitoring energy consumption
of multiple components of an application in terms of
computations becomes a challenging task.
As mentioned earlier, the application models use
energy-consumption models to overcome this issue.
However, the energy models are developed by execut-
ing predefined computational and communicational
tasks on a smartphone, and the energy consumption
is measured using external hardware (power meter).
Further, energy consumption coefficients are defined
based on the monitored readings. Consequently, the
energy models are valid only for the monitored smart-
phone and might provide inaccurate readings on un-
known (new model) smartphones.8
Offloading Time
Some people might compare smartphone and cloud
computational times to check if the computation
offloading enhances performance. However, this
comparison doesn’t guarantee the required perfor-
mance gain, because computation offloading takes a
considerable amount of time in terms of communi-
cation between the smartphone and the cloud. Apart
from this, in some application models, computation
offloading incurs considerable delay on the smart-
phone (request preparation time and result integra-
tion time) and cloud (request marshalling time and
result preparation time). Therefore, context-aware ap-
plication models must utilize most of the highlighted
parameters to make optimum offloading decisions.
The challenge in doing so is that the aforemen-
tioned delays on the smartphone and cloud can vary
with time.10
Moreover, the communication link
quality of mobile networks is never consistent, and
offloading time varies depending on signal strength,
network bandwidth, latency, mobility, cell size (in
cellular networks), and network load. Therefore,
communication link quality estimation and predic-
tion of associated delays are challenging issues.
To address these challenges, some researchers
use profilers to monitor the required information,
which is later used in decision making, whereas oth-
er researchers use runtime link monitoring by send-
ing a small amount of data to the cloud to estimate
the communication time. Alternatively, some re-
searchers use the most recent communication histo-
ry information to estimate the communication time.
However, these techniques incur computational and
communicational overhead on the smartphone and
might fail because of mobile network fluctuating
characteristics.
Offloading Energy
As with offloading time, estimating the amount of
energy required for computational offloading is a
challenging task, because the offloading process in-
curs a variable computational overhead on the smart-
phone (as discussed previously). Moreover, energy
consumption varies based on the communication
technology, bandwidth, transmission power/radio
state, amount of data, cell size (in cellular networks),
and most importantly, communication pattern (pack-
et size and the interval between packet sending).
We estimate offloading energy using techniques
similar to those discussed in the previous section. It
has similar pros and cons.
Application Support
Smartphones support a wide range of applications,
each with variable characteristics and resource de-
mands. For instance, a mathematical tool might take
small data input and perform large computations,
whereas an antivirus application might require large
data input to perform large computations. Conse-
quently, an application model that can improve per-
formance or energy efficiency by using runtime data
offloading might do well for one type of application
and fail for others. This variability exists because
existing application models use a single offloading
technique to achieve a particular objective for a pre-
defined application type.
Therefore, new application models are required
that can support different types of applications by
using multiple offloading techniques in a single
model. However, making an application model in-
telligent enough to characterize the applications
properly and apply optimal offloading technique is
challenging.
n light of current developments and challenges
in context-aware mobile cloud computing, we
propose several actions. First, benchmarking the
available smartphone processors against desktop
system processors and common cloud instances will
highlight the differences between their computa-
8. M AY/J U N E 201 5 I EEE CLO U D COM PU T I N G 49
tional powers and expected performance enhance-
ment (irrespective of the communication time). In
addition, smartphone vendors must release energy
information datasheets for their smartphones so
that precise energy consumption coefficients of dif-
ferent computational and communicational enti-
ties can be known. Smartphone operating systems
must also evolve in terms of information provision
so that detailed energy consumption information of
a particular task in terms of computations and com-
munications is available to both applications and
developers. Finally, mobile cloud computing ser-
vice models must be standardized so the application
models can use common offloading techniques.
Cloud storage and application-specific services,
such as Apples’ iCloud and Siri, already appear on
smartphones. Before long, cloud-based computing
applications for smartphones will appear in the mar-
ket that will enhance mobile users’ experience in
terms of performance, energy efficiency, and execu-
tion support. However, this demands context aware-
ness in multiple aspects. Efforts are being made to
achieve the desired level of context awareness, but
existing solutions aren’t up to the mark. Therefore,
more efforts are required to make this technology
grow. Cloud computing might not be on the horizon,
but its powered technologies, such as mobile cloud
computing, are still emerging, and with the wide
range of open issues, this technology won’t be going
anywhere soon.
References
1. A.R. Khan et al., “A Survey of Mobile Cloud
Computing Application Models,“ IEEE Comm.
Surveys Tutorials, vol. 16, no. 1, 2014, pp.
393–413.
2. T. Xing et al., “MobiCloud: A Geo-Distributed
Mobile Cloud Computing Platform,“ Proc. Int’l
Conf. Network and Service Management (CNSM
12), 2012, pp. 164–168.
3. R. Lacuesta et al., “Spontaneous Ad Hoc Mo-
bile Cloud Computing Network,“ Scientific
World J., vol. 2014, 2014, article 232419; doi:
10.1155/2014/232419.
4. H. Modares et al., “Security in Mobile Cloud
Computing,” Mobile Networks and Cloud Com-
puting Convergence for Progressive Services and
Applications, J.J.P.C. Rodrigues and J. Lloret,
eds., 2013, pp. 79–91.
5. A.R. Khan et al., “Pirax: Framework for Appli-
cation Piracy Control in Mobile Cloud Environ-
ment,“ J. Super Computing, vol. 68, no. 2, 2014,
pp. 753–776.
6. M. Satyanarayanan et al., “The Case for VM-
Based Cloudlets in Mobile Computing,“ IEEE Per-
vasive Computing, vol. 8, no. 4, 2009, pp. 14–23.
7. J. Niu et al., “Bandwidth-Adaptive Application
Partitioning for Execution Time and Energy Op-
timization,“ Proc. IEEE Int’l Conf. Communica-
tions (ICC 13), 2013, pp. 3660–3665.
8. L. Zhang et al., “Accurate Online Power Esti-
mation and Automatic Battery Behavior Based
Power Model Generation for Smartphones,”
Proc. Int’l Conf. Hardware/Software Codesign
and System Synthesis, 2010, pp. 105–114.
9. A.R. Khan et al., “MobiByte: An Application De-
velopment Model for Mobile Cloud Computing,”
J. Grid Computing, Apr. 2015, pp. 1–24; doi:
10.1007/s10723-015-9335-x.
10. B.-G. Chun et al., “CloneCloud: Elastic Execu-
tion between Mobile Device and Cloud,” Proc.
Int’l Conf. Computer Systems, 2011, pp. 301–314.
ATTA UR REHMAN KHAN is an assistant professor
in the Department of Computer Science at COMSATS
Institute of Information Technology (CIIT), Pakistan,
and a freelance ICT consultant. His research interests
include mobile computing, cloud computing, ad hoc
networks, distributed systems, and security. Khan has a
PhD in mobile cloud computing from the University of
Malaya. Contact him at dr@attaurrehman.com.
MAZLIZA OTHMAN is an associate professor with
the Faculty of Computer Science and IT at the Uni-
versity of Malaya. Her research interests include perva-
sive computing and self-organizing networks. Othman
has a PhD in mobile computing from the University
of London. She is the author of Principles of Mobile
Computing and Communications (Auerbach Publica-
tions, 2007). Contact her at mazliza@um.edu.my.
FENG XIA is a full professor in the School of Soft-
ware, Dalian University of Technology, China. His
research interests include social computing, mobile
computing, and cyber-physical systems. Xia has a PhD
in control science and engineering from Zhejiang
University, Hangzhou, China. He’s a senior member
of IEEE, IEEE Computer Society, IEEE SMC Soci-
ety, ACM, and ACM SIGWEB. Xia is the correspond-
ing author. Contact him at f.xia@ieee.org.
ABDUL NASIR KHAN is an assistant professor in
the Department of Computer Science, COMSATS
Institute of Information Technology. His research in-
terests include various aspects of network security and
distributed computing. Khan has a PhD in mobile
cloud security from the University of Malaya. Con-
tact him at anasir@ciit.net.pk.