A survey of research on computation offloading in mobile cloud computing

Jin, Xiaomin; Hua, Wenqiang; Wang, Zhongmin; Chen, Yanping

doi:10.1007/s11276-022-02920-2

Cited by 29 publications

(13 citation statements)

References 140 publications

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“…MEC helps to extend the lifetime of the battery of mobile devices, and the MEC saves 42% of energy consumption compared to MCC as stated on [116]. Finally, computation offloading in MCC considers the network as a stable environment, which means that after the offloading decision is made, the task will migrate to the main cloud side without considering the network fluctuations that could happen during the offloading process, such as user mobility during offloading, which will disconnect the connection between the mobile device side and the cloud side [94,98,117]. On the contrary, MEC helps to get the best solution in the worst case of network fluctuations during task offloading and researchers in the area of MEC try to find solutions in various network issues that affect the computation offloading mechanism [53,[118][119][120][121][122].…”

Section: Offloading Comparison Between MCC and Mecmentioning

confidence: 99%

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Maray

Shuja

2022

Mobile Information Systems

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Cloud and mobile edge computing (MEC) provides a wide range of computing services for mobile applications. In particular, mobile edge computing enables a computing and storage infrastructure provisioned closely to the end-users at the edge of a cellular network. The small base stations are deployed to establish a mobile edge network that can be coined with cloud infrastructure. A large number of enterprises and individuals rely on services offered by mobile edge and clouds to meet their computational and storage demands. Based on user behavior and demand, the computational tasks are first offloaded from mobile users to the mobile edge network and then executed at one or several specific base stations in the mobile edge network. The MEC architecture has the capability to handle a large number of devices that in turn generate high volumes of traffic. In this work, we first provide a holistic overview of MCC/MEC technology that includes the background and evolution of remote computation technologies. Then, the main part of this paper surveys up-to-date research on the concepts of offloading mechanisms, offloading granularities, and computational offloading techniques. Furthermore, we discuss the offloading mechanism in the static and dynamic environment along with optimization techniques. We further discuss the challenges and potential future directions for MEC research.

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Section: Offloading Comparison Between MCC and Mecmentioning

confidence: 99%

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Maray

Shuja

2022

Mobile Information Systems

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“…3 System Model EC in the industrial internet pays more attention to latency and security than traditional EC [25]. Fig.…”

Section: Workmentioning

confidence: 99%

Edge Computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

Chen¹,

Bai²,

Jin³

et al. 2023

Computers, Materials &Amp; Continua

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Deploying task caching at edge servers has become an effective way to handle compute-intensive and latency-sensitive tasks on the industrial internet. However, how to select the task scheduling location to reduce task delay and cost while ensuring the data security and reliable communication of edge computing remains a challenge. To solve this problem, this paper establishes a task scheduling model with joint blockchain and task caching in the industrial internet and designs a novel blockchain-assisted caching mechanism to enhance system security. In this paper, the task scheduling problem, which couples the task scheduling decision, task caching decision, and blockchain reward, is formulated as the minimum weighted cost problem under delay constraints. This is a mixed integer nonlinear problem, which is proved to be nonconvex and NP-hard. To solve the optimal solution, this paper proposes a task scheduling strategy algorithm based on an improved genetic algorithm (IGA-TSPA) by improving the genetic algorithm initialization and mutation operations to reduce the size of the initial solution space and enhance the optimal solution convergence speed. In addition, an Improved Least Frequently Used algorithm is proposed to improve the content hit rate. Simulation results show that IGA-TSPA has a faster optimal solution-solving ability and shorter running time compared with the existing edge computing scheduling algorithms. The established task scheduling model not only saves 62.19% of system overhead consumption in comparison with local computing but also has great significance in protecting data security, reducing task processing delay, and reducing system cost.

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“…The emergence of computing offloading technology provides new opportunities to solve such problems [ 5 , 6 ]. TDs can offload computing tasks to cloud platforms or edge computing networks, thereby significantly shortening task execution time and extending TD's battery life [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review on internet of things task offloading in multi-access edge computing

Dayong,

Bin Abu Bakar,

Isyaku

et al. 2024

Heliyon

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A survey of research on computation offloading in mobile cloud computing

Cited by 29 publications

References 140 publications

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Edge Computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

A comprehensive review on internet of things task offloading in multi-access edge computing

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