Task Offloading for Multi-Gateway-Assisted Mobile Edge Computing Based on Deep Reinforcement Learning

Chu, Xianyang; Zhu, Muhua; Mao, Hongyan; Qiu, Yunzhou

doi:10.1109/smc53654.2022.9945614

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…The authors developed a Markov game-based model for the computation outsourcing problem in 5G MEC. Chu et al 150…”

Section: Deep Reinforcement Learning and Mathematicalmentioning

confidence: 99%

“…The authors developed a Markov game‐based model for the computation outsourcing problem in 5G MEC. Chu et al 150 have found that combining offloading requests from several terminals with a single EdSr might be difficult. Edge gateways were installed close to terminal devices to solve this problem and reduce the load for processing EdSrs.…”

Section: Energy‐based Co Techniques In Ecmentioning

confidence: 99%

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Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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SummaryIn today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state‐of‐the‐art CO techniques in edge computing (EC) in the context of energy, Quality of Service (QoS), authentication and traceability. In this SLR, the evolution of offloading techniques is analyzed in detail. A total of 138 articles, spanning from 2016 to 2023 (till date), have been classified into QoS, energy, and authentication and traceability‐based CO techniques. The optimization‐based techniques are the most preferred choices to improve the QoS and reduce energy in the research field of CO in EC. In addition, this paper explores the significant issues and challenges that require further investigation. For future research, energy, QoS, and data provenance for dependent or dynamic task offloading in mobility scenarios can be explored further.

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“…The authors developed a Markov game-based model for the computation outsourcing problem in 5G MEC. Chu et al 150…”

Section: Deep Reinforcement Learning and Mathematicalmentioning

confidence: 99%

Section: Energy‐based Co Techniques In Ecmentioning

confidence: 99%

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

Deep Reinforcement Learning-based scheduling for optimizing system load and response time in edge and fog computing environments

Wang,

Goudarzi,

Gong

et al. 2024

Future Generation Computer Systems

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Task Offloading for Multi-Gateway-Assisted Mobile Edge Computing Based on Deep Reinforcement Learning

Cited by 2 publications

References 18 publications

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Deep Reinforcement Learning-based scheduling for optimizing system load and response time in edge and fog computing environments

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