A Dependent Task Offloading Method for Joint Time Delay and Energy Consumption

Zhang Junna; Bao Xiang; Chen Jiawei; Zhao Xiaoyan; Yuan Peiyan; Wang Shangguang

doi:10.7544/issn1000-1239.202220779

Journal of Computer Research and Development > 2023 > 60(12): 2770-2782. > DOI: 10.7544/issn1000-1239.202220779

Zhang Junna, Bao Xiang, Chen Jiawei, Zhao Xiaoyan, Yuan Peiyan, Wang Shangguang. A Dependent Task Offloading Method for Joint Time Delay and Energy Consumption[J]. Journal of Computer Research and Development, 2023, 60(12): 2770-2782. DOI: 10.7544/issn1000-1239.202220779

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A Dependent Task Offloading Method for Joint Time Delay and Energy Consumption

1.
School of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan 453007
2.
Henan Provincial Engineering Laboratory of Smart Commerce and Internet of Things Technology (Henan Normal University), Xinxiang, Henan 453007
3.
State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications), Beijing 100876

Funds: This work was supported by the National Natural Science Foundation of China (61902112, 62072159) and the Science and Technology Development Foundation of Henan Province (222102210011).

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Author Bio:
Zhang Junna: born in 1979. PhD, associate professor, master supervisor. Member of CCF. Her main research interests include edge computing and service computing

Bao Xiang: born in 1997. Master candidate. Student member of CCF. His main research interests include edge computing and service computing

Chen Jiawei: born in 1998. Master candidate. Student member of CCF. His main research interests include edge computing and service computing

Zhao Xiaoyan: born in 1981. PhD, associate professor, master supervisor. Member of CCF. Her main research interests include edge computing and D2D communication

Yuan Peiyan: born in 1978. PhD, professor, master supervisor. Member of CCF. His main research interests include edge computing and crowd sensing

Wang Shangguang: born in 1982. PhD, professor, PhD supervisor. Distinguished member of CCF. His main research interests include service computing, mobile cloud computing, and Internet of vehicles and network security
Received Date: August 31, 2022
Revised Date: January 29, 2023
Available Online: September 19, 2023

Graphical Abstract

Abstract

Abstract

Edge computing deploys computing and storage resources on the edge of the network closed to users, so that users can offload high-latency and energy-intensive applications to the edge of the network for execution to reduce application latency and local energy consumption. Existing offloading research usually assumes that the offloaded tasks are independent of each other, and the edge server caches all the services required for task execution. However, in real scenarios, there are often dependent between tasks, and edge servers can only cache limited services due to their limited storage resources. To this end, we propose a dependent task offloading method that balances latency and energy consumption (i.e., cost) under the constraints of limited computing resources and service caches on edge servers. First, the constraints in the research problem are relaxed to be transformed into a convex optimization problem. A convex optimization tool is used to find the optimal solution, which is used to calculate the priority of offloading tasks. Then, the tasks are offloaded to the edge server with the least cost according to the priority. If multiple dependent tasks are offloaded to different edge servers, an improved particle swarm optimization is used to solve the optimal transmission power of edge servers to minimize the total cost. Finally, sufficient experiments are performed based on real datasets to verify the effectiveness of the proposed method. The experimental results show that the proposed method can reduce the total cost by approximately 8% to 23% compared with other methods.
- edge computing,
- task offloading,
- task dependency,
- service cache,
- improved particle swarm optimization

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References (29)

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