A Fast and Efficient Task Offloading Approach in Edge-Cloud Collaboration Environment

Author:

Liu Linyuan1,Zhu Haibin2ORCID,Wang Tianxing3,Tang Mingwei4

Affiliation:

1. Department of E-Commerce, Nanjing Audit University, Nanjing 211815, China

2. Collaborative Systems Laboratory, Nipissing University, North Bay, ON P1B 8L7, Canada

3. School of Business, Nanjing Audit University, Nanjing 211815, China

4. School of Computer Science, Nanjing Audit University, Nanjing 211815, China

Abstract

Edge-cloud collaboration fully utilizes the advantages of sufficient computing resources in cloud computing and the low latency of edge computing and better meets the needs of various Internet of Things (IoT) application scenarios. An important research challenge for edge-cloud collaboration is how to offload tasks to edge and cloud quickly and efficiently, taking into account different task characteristics, resource capabilities, and optimization objectives. To address the above challenge, we propose a fast and efficient task offloading approach in edge-cloud collaboration systems that can achieve a near-optimal solution with a low time overhead. First, it proposes an edge-cloud collaborative task offloading model that aims to minimize time delay and resource cost while ensuring the reliability requirements of the tasks. Then, it designs a novel Preprocessing-Based Task Offloading (PBTO) algorithm to quickly obtain a near-optimal solution to the Task Offloading problem in Edge-cloud Collaboration (TOEC) systems. Finally, we conducted extended simulation experiments to compare the proposed PBTO algorithm with the optimal method and two heuristic methods. The experimental results show that the total execution time of the proposed PBTO algorithm is reduced by 87.23%, while the total cost is increased by only 0.0004% compared to the optimal method. The two heuristics, although better than PBTO in terms of execution time, have much lower solution quality, e.g., their total costs are increased by 69.27% and 85.54%, respectively, compared to the optimal method.

Funder

Natural Sciences and Engineering Research Council of Canada

the Social Sciences and Humanities Research Council of Canada (SSHRC) Insight Grant

the National Social Science Fund of China

the Significant Project of Jiangsu College Philosophy and Social Sciences Research

the Planning Fund Project of Humanities and Social Sciences Research of Ministry of Education

Publisher

MDPI AG

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Efficient Pareto based approach for IoT task offloading on Fog–Cloud environments;Internet of Things;2024-10

2. Task Offloading in Edge Computing System with Deep Reinforcement Learning and Reward Shaping;2024 9th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA);2024-04-25

3. A Deep Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing;2024 International Conference on Integrated Circuits and Communication Systems (ICICACS);2024-02-23

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