Algorithms for Service Reliability Guarantee in Parked Vehicle Assisted Edge Computing

Chen Ming-qiu; Huang Jia-le; Wu Ji-gang

doi:10.12052/gdutxb.230104

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Algorithms for Service Reliability Guarantee in Parked Vehicle Assisted Edge Computing

更新时间：2026-05-27

- Algorithms for Service Reliability Guarantee in Parked Vehicle Assisted Edge Computing
- Vol. 41, Issue 4, Pages: 80-88(2024)
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.230104
  CLC： TP391
- Published：2024
- 稿件说明：
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陈明秋, 黄家乐, 武继刚. 面向停驻车辆辅助边缘计算的服务可靠增强算法[J]. 广东工业大学学报, 2024, 41(4): 80-88.

Chen Ming-qiu, Huang Jia-le, Wu Ji-gang. Algorithms for Service Reliability Guarantee in Parked Vehicle Assisted Edge Computing[J]. 2024, 41(4): 80-88.
陈明秋, 黄家乐, 武继刚. 面向停驻车辆辅助边缘计算的服务可靠增强算法[J]. 广东工业大学学报, 2024, 41(4): 80-88. DOI： 10.12052/gdutxb.230104.

Chen Ming-qiu, Huang Jia-le, Wu Ji-gang. Algorithms for Service Reliability Guarantee in Parked Vehicle Assisted Edge Computing[J]. 2024, 41(4): 80-88. DOI： 10.12052/gdutxb.230104.

摘要

停驻车辆辅助边缘计算(Parked Vehicle Assisted Edge Computing

PVEC) 可充分利用停驻车辆闲置资源，有效缓解车载边缘计算中的资源供需矛盾。然而，因停车行为具有不确定性，车主可突然中止车辆对外提供的计算服务。这将增大用户获取可靠计算服务的难度。为此，本文提出一个面向PVEC的服务可靠增强问题，并引入任务复制技术，将该问题转化为任务副本卸载问题，目标是最小化任务副本的平均服务时延。同时，证明了该问题的NP难解性。为求解问题，提出一种贪婪算法(Greedy Algorithm

GA)，优先为具有较大数据量的任务选取一组服务时延最小且满足其服务可靠需求的停驻车辆。此外，还提出一种增强遗传算法(Enhanced Genetic Algorithm

EGA)，以进一步优化算法GA的解。实验结果表明，针对用户服务可靠需求变化的情形，与现有的冗余最小化算法及非任务复制算法相比，算法EGA和GA有效降低了任务副本的平均服务时延。

Abstract

Parked vehicle assisted edge computing (PVEC) is effective to alleviate the imbalance between supply and demand of resources in vehicular edge computing

by utilizing the idle resources in parked vehicles (PVs). However

the computing services provided by the PVs can be abruptly aborted due to uncertain parking behaviours. This makes it hard to meet the requirements of users on service reliability. To address this issue

this paper formulates an optimization problem for service reliability guarantee. Then

a task replication technique is introduced to transform the formulated problem into a replication offloading problem

with the goal of minimizing the average completion time of task replications. The NP-hardness of the formulated problem is proved. A greedy algorithm (GA) is proposed to solve the formulated problem

by carefully offloading the replicas of the tasks with large data sizes to the PVs

which can provide the computing services with service guarantee and short completion time. Meanwhile

an enhanced genetic algorithm (EGA) is proposed to refine the solution generated by the proposed algorithm GA. Experimental results show that the proposed GA and EGA algorithms outperform the baseline algorithms in terms of the average completion time of task replications for different requirements of users on service reliability.

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references

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