DyRAC: Cost-aware Resource Assignment and Provider Selection for Dynamic Cloud Workloads

Yannis Sfakianakis, M. Marazakis, A. Bilas
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引用次数: 1

Abstract

A primary concern for cloud users is how to minimize the total cost of ownership of cloud services. This is not trivial to achieve due to workload dynamics. Users need to select the number, size, type of VMs, and the provider to host their services based on available offerings. To avoid the complexity of re-configuring a cloud service, related work commonly approaches cost minimization as a packing problem that minimizes the resources allocated to services. However, this approach does not consider two problem dimensions that can further reduce cost: (1) provider selection and (2) VM sizing. In this paper, we explore a more direct approach to cost minimization by adjusting the type, number, size of VM instances, and the provider of a cloud service (i.e. a service deployment) at runtime. Our goal is to identify the limits in service cost reduction by online re-deployment of cloud services. For this purpose, we design DyRAC, an adaptive resource assignment mechanism for cloud services that, given the resource demands of a cloud service, estimates the most cost-efficient deployment. Our evaluation implements four different resource assignment policies to provide insight into how our approach works, using VM configurations of actual offerings from main providers (AWS, GCP, Azure). Our experiments show that DyRAC reduces cost by up to 33% compared to typical strategies.
云用户的主要关注点是如何最小化云服务的总拥有成本。由于工作负载的动态性,实现这一点并不容易。用户需要根据可用的产品选择虚拟机的数量、大小、类型以及托管其服务的提供商。为了避免重新配置云服务的复杂性,相关工作通常将成本最小化作为最小化分配给服务的资源的打包问题。然而,这种方法没有考虑可以进一步降低成本的两个问题维度:(1)提供商选择和(2)VM大小。在本文中,我们通过在运行时调整VM实例的类型、数量、大小和云服务的提供商(即服务部署)来探索一种更直接的最小化成本的方法。我们的目标是确定通过在线重新部署云服务来降低服务成本的限制。为此,我们设计了DyRAC,这是一种针对云服务的自适应资源分配机制,在给定云服务的资源需求的情况下,可以估计最具成本效益的部署。我们的评估实现了四种不同的资源分配策略,使用主要提供商(AWS、GCP、Azure)的实际产品的VM配置,以深入了解我们的方法是如何工作的。我们的实验表明,与典型策略相比,DyRAC降低了高达33%的成本。
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