Decentralized Computation Offloading Game for Mobile Cloud Computing

Chen, Xu

doi:10.1109/tpds.2014.2316834

Cited by 806 publications

(414 citation statements)

References 27 publications

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“…Most existing studies focused on o oading of mobile users' tasks to remote clouds by exploring di erent pricing models or di erent e cient energy-conserve task scheduling approaches, such as [10 16]. For example, the author of [14] formulates the decentralized computation o oad decision making problem among mobile device users as a decentralized computation o oading game, analyzed the structural property of the game and showed that the game always admits a Nash equilibrium. The authors of [15] investigates the problem of how to conserve energy for the resource-constrained mobile device, by optimally executing mobile applications in either the local mobile device or the cloud.…”

Section: Related Workmentioning

confidence: 99%

Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds

et al. 2016

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Nowadays, although the data processing capabilities of the modern mobile devices are developed in a fast speed, the resources are still limited in terms of processing capacity and battery lifetime. Some applications, in particular the computationally intensive ones, such as multimedia and gaming, often require more computational resources than a mobile device can afford. One way to address such a problem is that the mobile device can o oad those tasks to the centralized cloud with data centers, the nearby cloudlet or ad hoc mobile cloud. In this paper, we propose a data o oading and task allocation scheme for a cloudlet-assisted ad hoc mobile cloud in which the master device (MD) who has computational tasks can access resources from nearby slave devices (SDs) or the cloudlet, instead of the centralized cloud, to share the workload, in order to reduce the energy consumption and computational cost. A two-stage Stackelberg game is then formulated where the SDs determine the amount of data execution units that they are willing to provide, while the MD who has the data and tasks to o oad sets the price strategies for di erent SDs accordingly. By using the backward induction method, the Stackelberg equilibrium is derived. Extensive simulations are conducted to demonstrate the e ectiveness of the proposed scheme.

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Section: Related Workmentioning

confidence: 99%

Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds

et al. 2016

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“…Consider maximum data computation is 100 GB and maximum offered bandwidth is Hz [11]. For this scenario, we have chosen not to offload until 10% of maximum data is to be computed or 10% of maximum BW is offered.…”

Section: Probability Of Offloadingmentioning

confidence: 99%

An Approach to Improve the Performance of Mobile Computation Technology Using Data Offloading

Islam

Tareq²,

Mohona

et al. 2017

International Journal of Modern Research in Electrical and Elec

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Modern mobile phones have a powerful processing unit that can perform multiple operations simultaneously. But the main constrain is the processing power and energy required to drive it. Cloud computing has come as a blessing for the mobile computing as it possesses a vast resource, high storage capacity and high processing power. In Mobile Cloud Computing (MCC), computation is offloaded to the cloud, cloud processes the data and generates information; and sends it back to the mobile device. Probability of offloading depends on line bandwidth, line length, size of data, processing speed of mobile, processing speed of cloud, storage and energy capacity of mobile etc. This paper presents a model for efficient data offloading decision, depending on the total execution time of a task when the data computation happens in the mobile and when it is offloaded to the cloud. An algorithm has been proposed here on data offloading decision. As thus an equation has been proposed to observe the probability of offloading process.

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“…Most of these research devised a variety of approaches to implement energy saving, time saving and both energy and time saving strategies in computation offloading for mobile cloud computing [28], [10], [22], [7]. Some of these researches have proposed sending computationally intensive tasks to nearby mobile devices with higher computational resources [28], [10].…”

Section: Related Workmentioning

confidence: 99%

“…Others have identified elastic computation offloading methods in which resource intensive applications are partitioned into user interaction activities and computational activities and some parts of the computational activities are sent to surrogates or remote servers for efficient and timely processing whiles the mobile devices process the other parts locally [7], [18], [24]. Some other works have also proposed the complete cloning, mirroring or imaging of a mobile device system together with its operating system and applications and sending the mirrored or cloned image to a clone cloud server located at a remote place for processing and the results sent back to the mobile devices [10].…”

Section: Related Workmentioning

confidence: 99%

Resilient Framework for Distributed Computation Offloading: Overview, Challenges and Issues

Agbesi¹,

Abdulai²,

Ferdinand³

et al. 2017

ijacsa

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Abstract-Gradually, mobile and smart computing devices are becoming pervasive and prevalent in society and also increasingly being used to undertake the daily tasks and business activities of individuals and organizations worldwide as compared to their desktop counterparts. But these mobile and smart computing devices are resource constrained and sometimes lack the needed computational capacities; thus, memory, energy, storage, and processor to run the plethora of resource intensive applications available for mobile users. There is a lot of benefit of offloading resource demanding applications and intensive computations from mobile devices to other systems with higher resource capacities in the cloud. Mobile cloud computing is a form of cloud computing that seeks to enhance the capacity and capabilities of mobile and smart computing devices by enabling mobile devices to offload some computational tasks to the cloud for processing which otherwise would have been a challenge. The study setup an experiment to investigate computation offloading for mobile devices. The study also presented an energy model for computation offloading. It was observed during the experiment that by offloading intensive applications from mobile and smart computing devices to other systems with higher resource capacities, a great amount of resource efficiency is achieved.

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Decentralized Computation Offloading Game for Mobile Cloud Computing

Cited by 806 publications

References 27 publications

Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds

Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds

An Approach to Improve the Performance of Mobile Computation Technology Using Data Offloading

Resilient Framework for Distributed Computation Offloading: Overview, Challenges and Issues

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