文献类型：期刊文献

中文题名：边缘计算中基于能耗感知的容错协同任务执行算法

英文题名：Energy-aware Fault-tolerant Collaborative Task Execution Algorithm in Edge Computing

作者：薛艳芬[1];高继梅[1];范贵生[2];虞慧群[2];许亚杰[1]

机构：[1]黄河交通学院智能工程学院,河南焦作454950;[2]华东理工大学信息科学与工程学院,上海200000

年份：2021

卷号：48

期号：S01

起止页码：374

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：国家自然科学基金(61702334,61772200);上海自然科学基金(17ZR1406900,17ZR1429700);上海市科技项目创新行动计划(16511101000);上海应用技术大学协同创新基金(XTCX2016-20);华东理工大学教育教学规律与方法研究项目(ZH1726108)。

语种：中文

中文关键词：边缘计算;计算卸载;容错;重新提交;副本;

外文关键词：Edge computing;Computing offloading;Fault-tolerant;Resubmission;Replication;

摘要：边缘计算已被设想成为增强资源贫乏的智能设备计算能力的有效解决方案。通过任务卸载用户可以将计算复杂的任务卸载到边缘云端执行来满足其对资源的需求。然而,其仍然需要解决能量消耗、可靠性和延时的问题。文中提出了一种基于能耗感知的容错协同任务执行算法,以在减少设备能耗的同时保证卸载到边缘云上的任务成功执行。具体地,首先设计了一种具有容错能力的能耗感知协同任务执行模型,该模型通过将计算卸载模型和容错模型相结合,从而在应用程序的截止完成时间内减少设备能耗。然后,提出了一种基于能耗感知的容错协同任务执行调度算法,该算法包括协同任务执行、初始化调度和在线调度。协同任务执行是通过部分关键路径分析和one-climb策略来确定任务的执行决策;初始化调度是从副本和重新提交中为在边缘端执行的任务选择容错策略,以在发生故障时可针对任务采取相应容错措施;在线调度是在发生故障时实时调整容错策略以确保任务成功处理。最后,在3种具有代表性的任务拓扑上进行了广泛的仿真实验,评估了3种不同方案在任务完成率、能耗比方面的性能差异。结果表明,无论是截止完成时间、传输速率还是容错率的变化,该方法都可以保证任务在截止时间内顺利完成,相比协同任务执行更可靠,而且相比本地执行设备消耗的能量可至少减少30%。
Edge computing has been envisioned as an effective solution to enhance the computing capabilities for resource-constrained mobile devices.It allows users to satisfy the resource requirement by offloading heavy computing tasks to the edge cloud.However,it still needs to commit to solving the issues of energy consumption and reliability.This paper firstly proposes an energy-aware collaborative task execution scheduling model,which combines computing offloading model and fault-tolerant model to reduce energy consumption while improving reliability of edge computing within time constraints of tasks.Then,an energy-aware fault-tolerant collaborative task execution scheduling algorithm including collaborative task execution,initial scheduling and online scheduling is proposed to improve reliability while reducing energy consumption.The collaborative task execution is to determine the execution decision of tasks by partial critical path analysis and one-climb policy.The initial scheduling is to determine the fault-tolerant strategy from replication and resubmission for tasks executed on the edge cloud,ensuring the tasks processing successfully.The online scheduling is to adjust the fault-tolerant strategy in real time when a fault occurs.Finally,through extensive simulation experiments with the three different representative task topologies,the performance difference under three different scenarios in terms of the task completion rate and the energy consumption ratio are evaluated.Results show that the proposed method is more reliable than collaborative task execution and more energy-aware than local execution in terms of the change of the deadline,the data transmission rate,and the fault tolerance rate.