VioLinn: Proximity-aware Edge Placementwith Dynamic and Elastic Resource Provisioning

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Klervie Toczé, Ali J. Fahs, G. Pierre, S. Nadjm-Tehrani
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引用次数: 0

Abstract

Deciding where to handle services and tasks, as well as provisioning an adequate amount of computing resources for this handling, is a main challenge of edge computing systems. Moreover, latency-sensitive services constrain the type and location of edge devices that can provide the needed resources. When available resources are scarce there is a possibility that some resource allocation requests are denied. In this work, we propose the VioLinn system to tackle the joint problems of task placement, service placement, and edge device provisioning. Dealing with latency-sensitive services is achieved through proximity-aware algorithms that ensure the tasks are handled close to the end-user. Moreover, the concept of spare edge device is introduced to handle sudden load variations in time and space without having to continuously overprovision. Several spare device selection algorithms are proposed with different cost/performance tradeoffs. Evaluations are performed both in a Kubernetes-based testbed and using simulations and show the benefit of using spare devices for handling localized load spikes with higher quality of service (QoS) and lower computing resource usage. The study of the different algorithms shows that it is possible to achieve this increase in QoS with different tradeoffs against cost and performance.
动态和弹性资源配置的邻近感知边缘放置
决定在哪里处理服务和任务,以及为这种处理提供足够的计算资源,是边缘计算系统的主要挑战。此外,对延迟敏感的服务限制了能够提供所需资源的边缘设备的类型和位置。当可用资源稀缺时,一些资源分配请求可能会被拒绝。在这项工作中,我们提出VioLinn系统来解决任务放置、服务放置和边缘设备供应的联合问题。处理对延迟敏感的服务是通过邻近感知算法实现的,该算法确保任务在靠近最终用户的地方处理。此外,引入了备用边缘设备的概念,以处理时间和空间上的突然负载变化,而不必持续过度供应。提出了几种具有不同成本/性能权衡的备用设备选择算法。评估在基于kubernetes的测试平台和模拟中执行,并显示了使用备用设备处理局部负载峰值的好处,具有更高的服务质量(QoS)和更低的计算资源使用。对不同算法的研究表明,通过对成本和性能的不同权衡来实现QoS的增加是可能的。
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
CiteScore
5.20
自引率
3.70%
发文量
0
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