Joint Offloading and Resource Allocation Using Deep Reinforcement Learning in Mobile Edge Computing

Zhang, Xinjie; Zhang, Xinglin; Yang, Wentao

doi:10.1109/tnse.2022.3184642

Cited by 23 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Efficient optimization techniques based on binary offloading have acquired substantial attention for a real‐time MEC system, but efficient algorithms for partial offloading under time‐varying channels have received less attention. An energy‐efficient technique proposed by Zhang et al 127 used DRL to optimize the total energy consumption in a real‐time multi‐user MEC system.…”

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

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.

show abstract

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

“…In addition, Bozorgchenani et al [28] modeled task-offloading in MEC as a constrained multi-objective optimization problem that minimizes both the energy consumption and task processing delay of the mobile devices. More recently, Zhang et.al [29] proposed an energy-saving algorithm based on deep reinforcement learning to optimize the overall energy cost in real-time multi-user MEC systems. However, the delay and energy consumption performance may bear distinct weight coefficients, for instance, the system focuses on the delay performance by increasing the delay weight, which consequently places higher requirements for optimizing and offloading the system task.…”

Section: Related Workmentioning

confidence: 99%

Task-Offloading and Resource Allocation Strategy in Multidomain Cooperation for IIoT

et al. 2023

View full text Add to dashboard Cite

This study proposes a task-offloading and resource allocation strategy in multidomain cooperation (TARMC) for the industrial Internet of Things (IIoT) to resolve the problem of the non-uniform distribution of task computation among various cluster domain networks in the IIoT and the solidification of traditional industrial wireless network architecture, which produces low efficiency of static resource allocation and high delay in closed-loop data processing. Based on the closed-loop process of task interaction of intelligent terminals in wireless networks, the proposed strategy constructs a network model of multidomain collaborative task-offloading and resource allocation in IIoT for flexible and dynamic resource allocation among intelligent terminals, edge servers, and cluster networks. Considering the partial offloading mechanism, various tasks were segmented into multiple subtasks marked at bit-level per demand, which enabled local and edge servers to process all subtasks in parallel. Moreover, this study established a utility function for the closed-loop delay and terminal energy consumption of task processing, which transformed the process of multidomain collaborative task-offloading and resource allocation into the problem of task computing revenue. Furthermore, an improved Cuckoo Search algorithm was developed to derive the optimal offloading position and resource allocation decision through an alternating iterative method. The simulation results revealed that TARMC performed better than strategies.

show abstract

“…Maximum delays of minutes to hours are tolerable for some mobile applications that handle machine learning model training and personal health analytics. When edge resources are scarce or expensive, offloading these workloads to powerful public cloud servers for processing is a possible option [48]. New participative sensing applications are a great example of big data delay-tolerant applications.…”

Section: -Application Typementioning

confidence: 99%

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Baktayan¹,

Al-Baltah²

2022

Sustainable Engineering and Innovation

View full text Add to dashboard Cite

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.

show abstract

Joint Offloading and Resource Allocation Using Deep Reinforcement Learning in Mobile Edge Computing

Cited by 23 publications

References 36 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

Task-Offloading and Resource Allocation Strategy in Multidomain Cooperation for IIoT

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Contact Info

Product

Resources

About