Reliable and adaptive computation offload strategy with load and cost coordination for edge computing

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Weicheng Tang , Donghui Gao , Siyu Yu , Jianbo Lu , Zhiyong Wei , Zhanrong Li , Ningjiang Chen
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引用次数: 0

Abstract

There are several important factors to consider in edge computing systems including latency, reliability, power consumption, and queue load. Task replication requires additional energy costs in mobile edge offloading scenarios based on master-slave replication for fault tolerance. Excessive task offloading may lead to a sharp increase in the total energy consumption of the system including replication costs. Conversely, new tasks cannot enter the waiting queue and are lost, resulting in reliability issues. This paper proposes an adaptive task offloading strategy for balancing the edge node queue load and offloading cost (Lyapunov and Differential Evolution based Offloading schedule strategy, LDEO). The LDEO strategy innovatively customizes the Lyapunov drift-plus-penalty function by incorporating replication redundancy offloading costs to establish a balance model between the queue load and offloading cost. The LDEO strategy computes the optimal offloading decisions with dynamic adjustment characteristics by integrating a low-complexity differential evolution method, aiming to find the optimal balance point that minimizes the offloading cost while maintaining reliability performance. The experimental results show that compared with the existing strategies, LDEO strategy effectively reduces the redundancy of fault tolerance cost and the waiting time under the condition of ensuring that the task will not be discarded over time. It stabilizes the queue length in a reasonable range, controls the waiting time and loss rate of tasks, reduces the extra energy consumption paid by replication redundancy, and effectively realizes the optimal balance under multiple conditions.

针对边缘计算的可靠自适应计算卸载策略与负载和成本协调
边缘计算系统需要考虑几个重要因素,包括延迟、可靠性、功耗和队列负载。在基于主从复制容错的移动边缘卸载场景中,任务复制需要额外的能源成本。过度的任务卸载可能会导致系统总能耗(包括复制成本)急剧增加。相反,新任务无法进入等待队列而丢失,从而导致可靠性问题。本文提出了一种平衡边缘节点队列负载和卸载成本的自适应任务卸载策略(基于 Lyapunov 和差分进化的卸载计划策略,LDEO)。LDEO 策略通过结合复制冗余卸载成本,创新性地定制了 Lyapunov 漂移加惩罚函数,从而建立了队列负载与卸载成本之间的平衡模型。LDEO 策略通过集成低复杂度的微分演化方法,计算出具有动态调整特性的最优卸载决策,旨在找到最优平衡点,在保持可靠性能的同时使卸载成本最小。实验结果表明,与现有的策略相比,LDEO 策略在保证任务不被长期丢弃的前提下,有效降低了容错成本的冗余度,减少了等待时间。它将队列长度稳定在合理范围内,控制了任务的等待时间和丢失率,减少了复制冗余付出的额外能耗,有效实现了多种条件下的最优平衡。
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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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