Joint Task Offloading and Resource Allocation for IoT Edge Computing With Sequential Task Dependency

被引:51
作者
An, Xuming [1 ]
Fan, Rongfei [2 ]
Hu, Han [1 ]
Zhang, Ning [3 ]
Atapattu, Saman [4 ]
Tsiftsis, Theodoros A. [5 ]
机构
[1] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China
[2] Beijing Inst Technol, Sch Cyberspace Sci & Technol, Beijing 100081, Peoples R China
[3] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada
[4] Univ Melbourne, Dept Elect & Elect Engn, Parkville, Vic 3010, Australia
[5] Jinan Univ, Sch Intelligent Syst Sci & Engn, Zhuhai 519070, Peoples R China
基金
中国国家自然科学基金;
关键词
Task analysis; Fading channels; Resource management; Servers; Internet of Things; Optimization; Energy consumption; Internet of Things (IoT); mobile-edge computing (MEC); resource allocation; sequential task dependency; task offloading; MOBILE; COMPUTATION;
D O I
10.1109/JIOT.2022.3150976
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Incorporating mobile-edge computing (MEC) in the Internet of Things (IoT) enables resource-limited IoT devices to offload their computation tasks to a nearby edge server. In this article, we investigate an IoT system assisted by the MEC technique with its computation task subjected to sequential task dependency, which is critical for video stream processing and other intelligent applications. To minimize energy consumption per IoT device while limiting task processing delay, task offloading strategy, communication resource, and computation resource are optimized jointly under both slow and fast-fading channels. In slow fading channels, an optimization problem is formulated, which is nonconvex and involves one integer variable. To solve this challenging problem, we decompose it as a 1-D search of task offloading decision problem and a nonconvex optimization problem with task offloading decision given. Through mathematical manipulations, the nonconvex problem is transformed to be a convex one, which is shown to be solvable only with the simple Golden search method. In fast-fading channels, optimal online policies depending on the instant channel state are derived even though they are entangled. In addition, it is proved that the derived policy will converge to the offline policy when the channel coherence time is low, which can help save extra computation complexity. Numerical results verify the correctness of our analysis and the effectiveness of our proposed strategies over the existing methods.
引用
收藏
页码:16546 / 16561
页数:16
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