Understanding and Leveraging Cluster Heterogeneity for Efficient Execution of Cloud Services

S. Shukla, D. Ghosal, M. Farrens
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引用次数: 2

Abstract

Cloud warehouses are becoming increasingly heterogeneous by introducing different types of processors of varying speed and energy-efficiency. Developing an optimal strategy for distributing latency-critical service (LC-service) requests across multiple instances in a heterogeneous cluster is non-trivial. In this paper, we present a detailed analysis of the impact of cluster heterogeneity on the achieved server utilization and energy footprint to meet the required service-level latency bound (SLO) of LC-services. We develop cluster-level control plane strategies to address two forms of cluster heterogeneity - capacity and energy-efficiency. First, we propose Maximum-SLO-Guaranteed Capacity (MSG-Capacity) proportional load balancing for LC-Services to address the capacity heterogeneity and show that it can achieve higher utilization than naive performance-based heterogeneity awareness. Then, we present Efficient-First (E-First) heuristic-based Instance Scaling to address the efficiency heterogeneity. Finally, to address the bi-dimensional (capacity and energy-efficiency) heterogeneity, we superimpose the two approaches to propose Energy-efficient and MSG-Capacity (E2MC) based control-plane strategy that maximizes utilization while minimizing the energy footprint.
理解和利用集群异构以实现云服务的高效执行
通过引入不同速度和能效的不同类型的处理器,云仓库正变得越来越异构。开发跨异构集群中的多个实例分发延迟关键服务(LC-service)请求的最佳策略并非易事。在本文中,我们详细分析了集群异构对实现的服务器利用率和能源足迹的影响,以满足lc服务所需的服务级别延迟界限(SLO)。我们开发了集群级控制平面策略来解决两种形式的集群异质性——容量和能源效率。首先,我们提出了LC-Services的最大慢速保证容量(MSG-Capacity)比例负载平衡,以解决容量异构问题,并表明它可以实现比单纯的基于性能的异构感知更高的利用率。然后,我们提出了基于效率优先(E-First)启发式的实例缩放来解决效率异质性。最后,为了解决双向(容量和能源效率)异质性,我们将两种方法叠加在一起,提出了基于能效和MSG-Capacity (E2MC)的控制平面策略,以最大化利用率,同时最小化能源足迹。
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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