Cost-aware online VM purchasing for cloud-based application service providers with arbitrary demands

Abstract

Recent years witness the proliferation of Infrastructure-as-a-Service (IaaS) cloud services, which provide on-demand resources (CPU, RAM, disk, etc.) in the form of virtual machines (VMs) for hosting services of third parties. As such, the way of enabling scalable and dynamic Internet applications has been remarkably revolutionized. More and more Application Service Providers (ASPs) are launching their applications in clouds, eliminating the need to construct and operate their owned IT hardware and software. Given the state-of-the-art IaaS offerings, it is still a problem of fundamental importance how the ASPs should rent VMs from the clouds to serve their application needs, in order to minimize the cost while meeting their job demands over a long run. Cloud providers offer different pricing options to meet computing requirements of a variety of applications. The commonly adopted cloud pricing schemes are (1) reserved instance pricing, (2) on-demand instance pricing, and (3) spot instance pricing. However, the challenge facing an ASP is how these pricing schemes can be blended to accommodate arbitrary demands at the optimal cost. In this thesis, we seek to integrate all available pricing options and design effective online algorithms for the long-term operation of ASPs. We formulate the long-term-averaged VM cost minimization problem of an ASP with time-varying and delay-tolerant workloads in a stochastic optimization model. An efficient online VM purchasing algorithm is designed to guide the VM purchasing decisions of the ASP based on the Lyapunov optimization technique. In stark contrast with the existing studies, our online VM purchasing algorithm does not require any a priori knowledge of the workload or any future information. Moreover, it addresses the possible job interruption due to uncertain availability of spot instances. Rigorous analysis shows that our algorithm can achieve a time-averaged VM purchasing cost with a constant gap from its offline minimum. Trace-driven simulations further verify the efficacy of our algorithm.