Abstract
Edge computing is a new paradigm, which provides storage, computing, and network resources between the traditional cloud data center and terminal devices. In this paper, we concentrate on the application-driven task offloading problem in edge computing by considering the strong dependencies of sub-tasks for multiple users. Our objective is to joint optimize the total delay and energy generated by applications, while guaranteeing the quality of services of users. First, we formulate the problem for the application-driven tasks in edge computing by jointly considering the delays and the energy consumption. Based on that, we propose a novel Application-driven Task Offloading Strategy (ATOS) based on deep reinforcement learning by adding a preliminary sorting mechanism to realize the joint optimization. Specifically, we analyze the characteristics of application-driven tasks and propose a heuristic algorithm by introducing a new factor to determine the processing order of parallelism sub-tasks. Finally, extensive experiments validate the effectiveness and reliability of the proposed algorithm. To be specific, compared with the baseline strategies, the total cost reduction by ATOS can be up to 64.5% on average.
Highlights
This paper studies the application-driven tasks constructed by several sub-tasks with strong dependencies, and the sub-tasks that belong to one application can process on different devices
We propose an application-driven task offloading strategy (ATOS) based on deep reinforcement learning (DRL) by adding a preliminary sorting mechanism to realize the joint optimization of the delays and energy consumption
We study the application-driven task offloading in edge computing by considering the strong dependencies of sub-tasks
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. One extreme solution is offloading strategy by minimizing the transmission cost for Ai , which processes all sub-tasks on local devices with the order a1 → a2 → a3 This solution has the highest delay due to the limited capacity of local devices. We concentrate on the application-driven task offloading problem in edge computing by considering the strong dependencies of sub-tasks. Our problem poses several unique challenges as follows: (i) Since the capabilities of edge nodes and local devices are limited and different, it is nontrivial that finding a feasible strategy to complete the sub-task within improving the total cost for users during the offloading process. We discuss the offloading problem for the application-driven tasks in edge computing, and we optimize the total cost of users which jointly consider the delays and energy consumption.
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