Joint Optimization of Auto-Scaling and Adaptive Service Placement in Edge Computing

Ye Li, Haitao Zhang, W. Tian, Huadong Ma
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引用次数: 3

Abstract

In edge computing environment where network connections are often unstable and workload intensity changes frequently, the proper scaling mechanism and service placement strategy based on microservices are needed to ensure the edge services can be provided consistently. However, the common elastic scaling mechanism nowadays is threshold-based responsive scaling and has reaction time in the order of minutes, which is not suitable for delay-sensitive applications in the edge computing environment. Moreover, auto-scaling strategy and service replica placement are considered separately. If the scaled service replicas are misplaced on the edge nodes with limited resources or significant communication latency between upstream and downstream neighbours, the Quality of Service (QoS) cannot be guaranteed even with the auto-scaling mechanism. In this paper, we study the joint optimization of dynamic auto-scaling and adaptive service placement, and define it as a task delay minimization problem while satisfying resource and bandwidth constraints. Firstly, we design a multi-stage auto-scaling model based on workload prediction and performance evaluation of edge nodes to dynamically create an appropriate number of service replicas. Secondly, we propose a Dynamic Adaptive Service Placement (DASP) approach to iteratively place each service replica by using Adaptive Discrete Binary Particle Swarm Optimization (ADBPSO) algorithm. DASP can determine the current optimal placement strategy according to dynamic service replica scaling decision in a short time. The placement results of the current round will guide the optimization of the next cycle iteratively. The experimental evaluation shows that our approach significantly outperforms the existing methods in reducing the average task response time.
边缘计算中自缩放和自适应服务布局的联合优化
在网络连接不稳定、工作负载强度变化频繁的边缘计算环境中,需要适当的扩展机制和基于微服务的服务放置策略来保证边缘服务的一致性提供。然而,目前常见的弹性缩放机制是基于阈值的响应缩放,其反应时间在分钟量级,不适合边缘计算环境中对延迟敏感的应用。此外,还分别考虑了自动伸缩策略和服务副本的放置。如果扩展后的服务副本被放置在资源有限或上下游邻居之间通信延迟较大的边缘节点上,则即使使用自动扩展机制也无法保证服务质量(QoS)。本文研究了动态自伸缩和自适应服务布局的联合优化问题,并将其定义为满足资源和带宽约束的任务延迟最小化问题。首先,设计了基于工作负载预测和边缘节点性能评估的多阶段自动扩展模型,动态创建适当数量的服务副本;其次,我们提出了一种动态自适应服务放置(DASP)方法,通过自适应离散二进制粒子群优化(ADBPSO)算法迭代放置每个服务副本。DASP可以根据动态的服务副本扩展决策在短时间内确定当前的最优放置策略。当前一轮的投放结果将迭代地指导下一轮的优化。实验评估表明,我们的方法在减少平均任务响应时间方面明显优于现有方法。
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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