TODO: Task Offloading Decision Optimizer for the efficient provision of offloading schemes

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shilin Chen , Xingwang Wang , Yafeng Sun
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引用次数: 0

Abstract

As the volume of data stored on local devices increases, users turn to edge devices to help with processing tasks. Developing offloading schemes is challenging due to the varying configurations of edge devices and user preferences. While traditional methods provide schemes for offloading in various scenarios, they face unavoidable challenges, including the requirement to manage device workloads in real-time, significant computational costs, and the difficulty of balancing multi-objectives in offloading schemes. To solve these problems, we propose the Task Offloading Decision Optimizer, which offers efficient multi-objective offloading schemes that consider real-time device workload and user preference. The proposed offloading scheme contains three goals: reducing task execution time, decreasing device energy consumption, and lowering rental costs. It comprises two essential parts: Scheme Maker and Scheme Assistor. Scheme Maker utilizes deep reinforcement learning, optimizes the internal architecture, and enhances the performance of the operation. It optimizes buffer storage to generate dependable multi-objective offloading schemes considering real-time environmental conditions. Scheme Assistor utilizes the data in the Scheme Maker buffer to enhance efficiency by reducing computational costs. Extensive experiments have proved that the proposed framework efficiently provides offloading schemes considering the real-time conditions of the devices and the users, and it offers offloading schemes that enhance task completion rate by 50%. Compared to the baseline, the task execution time is reduced by 12%, and the device energy consumption is reduced by 11.1%.

TODO:任务卸载决策优化器,用于有效提供卸载方案
随着本地设备上存储的数据量不断增加,用户转而使用边缘设备来帮助完成处理任务。由于边缘设备的配置和用户偏好各不相同,开发卸载方案极具挑战性。虽然传统方法提供了各种场景下的卸载方案,但它们面临着不可避免的挑战,包括要求实时管理设备工作负载、计算成本高昂,以及在卸载方案中平衡多目标的困难。为了解决这些问题,我们提出了 "任务卸载决策优化器"(Task Offloading Decision Optimizer),它提供了考虑实时设备工作量和用户偏好的高效多目标卸载方案。建议的卸载方案包含三个目标:减少任务执行时间、减少设备能耗和降低租赁成本。它包括两个基本部分:方案制定者(Scheme Maker)和方案辅助者(Scheme Assistor)。Scheme Maker 利用深度强化学习,优化内部架构,提高操作性能。它优化缓冲区存储,根据实时环境条件生成可靠的多目标卸载方案。方案辅助器利用方案生成器缓冲区中的数据,通过降低计算成本来提高效率。广泛的实验证明,所提出的框架能有效地提供考虑到设备和用户实时条件的卸载方案,它所提供的卸载方案能将任务完成率提高 50%。与基线相比,任务执行时间缩短了 12%,设备能耗降低了 11.1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
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