Joint computation offloading and parallel scheduling to maximize delay-guarantee in cooperative MEC systems

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外文摘要：The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95％,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.

外文关键词：

Edge computingComputation offloadingParallel schedulingMobile-edge cooperationDelay guarantee

作者：

Mian Guo、Mithun Mukherjee、Jaime Lloret、Lei Li、Quansheng Guan、Fei Ji

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作者单位：

School of Electronics and Information,Guangdong Polytechnic Normal University,Guangzhou,China

School of Artificial Intelligence,Nanjing University of Information Science and Technology,Nanjing,China

Universitat Politecnica de Valencia,46022,Valencia,Spain

South China University of Technology,China

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基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of the Jiangsu Higher Education Institutions of China

项目编号：

619011286227310921KJB510032

出版年：

2024

DOI：

10.1016/j.dcan.2022.09.020

数字通信与网络(英文)

ISSN：

年,卷(期)：2024.10(3)