QoS-Aware Joint Offloading and Power Control Using Deep Reinforcement Learning in MEC

Li, Xiang; Chen, Yu

doi:10.1109/wpmc50192.2020.9309513

Cited by 1 publication

(1 citation statement)

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“…Li and Chen 60 applied a two‐step solution for improving QoS while offloading. The initial step was to reduce the queueing delay using the DDPG technique.…”

Section: Quality Of Service Based Offloading 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

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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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“…Li and Chen 60 applied a two‐step solution for improving QoS while offloading. The initial step was to reduce the queueing delay using the DDPG technique.…”

Section: Quality Of Service Based Offloading In Ecmentioning

confidence: 99%