부산대학교 도서관

Edge computing is considered a promising computing paradigm that can mitigate energy consumption and workload of end devices through task offloading to edge servers. Albeit with high potential, edge computing is still challenged by various forms of “system noise”, e.g., node failures, system failures, and poor network conditions. To this end, distributed coded computing has been proposed for alleviating such effects by introducing redundancy into the computation. However, traditional distributed coded computing only focuses on leveraging the unreliable computing resource, and this indeed increases the risk of task non-completion within the acceptable timeframe. To address this problem, in this paper, we propose a partial distributed coded computing framework that can leverage the reliable and unreliable resources in the edge environment. We further investigate the problem of how to offload the coded subtasks for energy reduction while meeting the task tolerant latency. To tackle the computation complexity, we then propose an Iterated Greedy Algorithm. The experimental results verify the efficiency of our proposed algorithm, and it can reduce the energy consumption by 20% compared with other algorithms.