Cross-Server Computation Offloading for Multi-Task Mobile Edge Computing

Shi, Yongpeng; Xia, Yujie; Gao, Ya

doi:10.3390/info11020096

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…A comparative analysis of the performance in delay and energy consumption of the proposed method with the algorithms proposed in [16] and [20] under the same conditions is provided. Simulations are conducted for the average time delay of different algorithms with different numbers of user mobile devices.…”

Section: Comparative Analysismentioning

confidence: 99%

“…e algorithm proposed in [20] has a larger average time delay whose variation rate is low as the user amount grows. Because of that, the performance of time delay is sacrificed to save more energy.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Ref. [20] Ref. least average time delay and energy consumption for task execution under different situations.…”

Section: Proposed In This Articlementioning

confidence: 99%

“…However, the safety interruption problem is not taken into consideration when the user devices are energy-constrained. To solve the problem of limited computational resources in edge computing architectures, Shi et al studied the problem of cross-server computation offloading and the collaboration between multiple edge servers for multitask mobile edge computing and proposed a greedy approximation algorithm, which can greatly reduce the overall energy consumption [20]. However, this method does not give a specific approach to improve the QoS of users.…”

Section: Introductionmentioning

confidence: 99%

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Computation Offloading Strategy for IoT Using Improved Particle Swarm Algorithm in Edge Computing

2022

Wireless Communications and Mobile Computing

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To address the problems of high energy consumption and time delay of the offloading strategies in traditional edge computing, a computation offloading strategy for the Internet of Things (IoT) using the improved Particle Swarm Optimization (PSO) in edge computing is proposed. First, a system model and an optimization objective function are constructed based on the communication model for the uplink transmission and the multiuser personalized computation task load model while considering constraints from multiple aspects. Then, the PSO is used to update the position of particles by encoding them and calculating the fitness values to find the optimal solution of the task offloading strategies, which greatly reduces the energy consumption during the task allocation process in the system. Finally, the simulations are conducted to compare the proposed method with two other algorithms in terms of the average time delay and energy consumption under different numbers of user mobile devices and data transmission rates. The simulation results showed that the average time delay and energy consumption of the proposed method are the smallest in different cases. And, the average delay and energy consumption are 0.205 s and 0.2 J, respectively, when the number of users’ mobile devices is 80, which are better than the other two comparison algorithms. Therefore, the proposed method can reduce the task execution delay with less energy consumption.

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Section: Comparative Analysismentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

“…Ref. [20] Ref. least average time delay and energy consumption for task execution under different situations.…”

Section: Proposed In This Articlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computation Offloading Strategy for IoT Using Improved Particle Swarm Algorithm in Edge Computing

2022

Wireless Communications and Mobile Computing

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“…A greedy approximation algorithm (GAA) is proposed in [85] that effectively reduces the energy consumption of the mobile devices during task offloading in MEC. This work formulates a problem of cross-server computation offloading that optimize the mobile devices' energy consumption under the constraints of computing resource and task completing time.…”

Section: ) Gaamentioning

confidence: 99%

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

et al. 2020

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The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation.

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Role of fog‐assisted Industrial Internet of Things: A systematic review

Sree

2022

Trans Emerging Tel Tech

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The manufacturing industry is benefiting a lot since the Industrial Internet of Things (IIoT) deploys sensors and actuators to calibrate, audit, and analyze data to enhance efficiency and profitability, thereby automating systems in the industrial sector. The exponential growth of smart devices in the industrial sector requires real‐time connectivity, low latency, nearby storage, secure communication, and reliability. To meet this demand, fog computing is effectively used in IIoT applications. Fog computing solves most of the problems in the fourth industrial revolution, which is the interface between end‐user devices and cloud/web services. Fog also allows for local processing with minimal acceptable latency. This article focuses on how fog computing becomes part of IIoT and also explores the architecture of fog‐assisted IIoT applications. The taxonomy is built by classifying the existing literature to reveal the salient features of fog computing in IIoT. This article also discusses IIoT security solutions as well as research problems of fog‐assisted IIoT. This article also outlines the research challenges of fog‐assisted IIoT and also discusses the security solutions of IIoT.

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Cross-Server Computation Offloading for Multi-Task Mobile Edge Computing

Cited by 8 publications

References 31 publications

Computation Offloading Strategy for IoT Using Improved Particle Swarm Algorithm in Edge Computing

Computation Offloading Strategy for IoT Using Improved Particle Swarm Algorithm in Edge Computing

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

Role of fog‐assisted Industrial Internet of Things: A systematic review

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