Multi-Hop Multi-Task Partial Computation Offloading in Collaborative Edge Computing

Collaborative edge computing (CEC) is a recent popular paradigm where different edge devices collaborate by sharing data and computation resources. One of the fundamental issues in CEC is to make task offloading decision. However, it is a challenging problem to solve as tasks can be offloaded to a d...

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Published in:	IEEE transactions on parallel and distributed systems Vol. 32; no. 5; pp. 1133 - 1145
Main Authors:	Sahni, Yuvraj, Cao, Jiannong, Yang, Lei, Ji, Yusheng
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Cloud computing Collaboration collaborative edge computing Completion time Computation offloading Dependent variables Edge computing Electronic devices Internet of Things Lower bounds network flow scheduling Optimization Processor scheduling Routing Scheduling and task partitioning Spread spectrum communication Task analysis Task scheduling
ISSN:	1045-9219, 1558-2183
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Abstract	Collaborative edge computing (CEC) is a recent popular paradigm where different edge devices collaborate by sharing data and computation resources. One of the fundamental issues in CEC is to make task offloading decision. However, it is a challenging problem to solve as tasks can be offloaded to a device at multi-hop distance leading to conflicting network flows due to limited bandwidth constraint. There are some works on multi-hop computation offloading problem in the literature. However, existing works have not jointly considered multi-hop partial computation offloading and network flow scheduling that can cause network congestion and inefficient performance in terms of completion time. This article formulates the joint multi-task partial computation offloading and network flow scheduling problem to minimize the average completion time of all tasks. The formulated problem optimizes several dependent decision variables including partial offloading ratio, remote offloading device, start time of tasks, routing path, and start time of network flows. The problem is formulated as an MINLP optimization problem and shown to be NP-hard. We propose a joint partial offloading and flow scheduling heuristic (JPOFH) that decides partial offloading ratio by considering both waiting times at the devices and start time of network flows. We also do the relaxation of formulated MINLP problem to an LP problem using McCormick envelope to give a lower bound solution. Performance comparison done using simulation shows that JPOFH leads to up to 32 percent improvement in average completion time compared to benchmark solutions which do not make a joint decision.
AbstractList	Collaborative edge computing (CEC) is a recent popular paradigm where different edge devices collaborate by sharing data and computation resources. One of the fundamental issues in CEC is to make task offloading decision. However, it is a challenging problem to solve as tasks can be offloaded to a device at multi-hop distance leading to conflicting network flows due to limited bandwidth constraint. There are some works on multi-hop computation offloading problem in the literature. However, existing works have not jointly considered multi-hop partial computation offloading and network flow scheduling that can cause network congestion and inefficient performance in terms of completion time. This article formulates the joint multi-task partial computation offloading and network flow scheduling problem to minimize the average completion time of all tasks. The formulated problem optimizes several dependent decision variables including partial offloading ratio, remote offloading device, start time of tasks, routing path, and start time of network flows. The problem is formulated as an MINLP optimization problem and shown to be NP-hard. We propose a joint partial offloading and flow scheduling heuristic (JPOFH) that decides partial offloading ratio by considering both waiting times at the devices and start time of network flows. We also do the relaxation of formulated MINLP problem to an LP problem using McCormick envelope to give a lower bound solution. Performance comparison done using simulation shows that JPOFH leads to up to 32 percent improvement in average completion time compared to benchmark solutions which do not make a joint decision.
Author	Ji, Yusheng Yang, Lei Sahni, Yuvraj Cao, Jiannong
Author_xml	– sequence: 1 givenname: Yuvraj orcidid: 0000-0003-4875-6950 surname: Sahni fullname: Sahni, Yuvraj email: csysahni@comp.polyu.edu.hk organization: Department of Computing, The Hong Kong Polytechnic University, Hong Kong – sequence: 2 givenname: Jiannong orcidid: 0000-0002-2725-2529 surname: Cao fullname: Cao, Jiannong email: csjcao@comp.polyu.edu.hk organization: Department of Computing, The Hong Kong Polytechnic University, Hong Kong – sequence: 3 givenname: Lei orcidid: 0000-0002-8732-3675 surname: Yang fullname: Yang, Lei email: sely@scut.edu.cn organization: School of Software Engineering, South China University of Technology, Guangzhou, China – sequence: 4 givenname: Yusheng orcidid: 0000-0003-4364-8491 surname: Ji fullname: Ji, Yusheng email: kei@nii.ac.jp organization: Information Systems Architecture Research Division, National Institute of Informatics, Tokyo, Japan
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SubjectTerms	Cloud computing Collaboration collaborative edge computing Completion time Computation offloading Dependent variables Edge computing Electronic devices Internet of Things Lower bounds network flow scheduling Optimization Processor scheduling Routing Scheduling and task partitioning Spread spectrum communication Task analysis Task scheduling
Title	Multi-Hop Multi-Task Partial Computation Offloading in Collaborative Edge Computing
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