Cost-Time Performance of Scaling Applications on the Cloud

Abstract

Recent advancements in big data processing and machine learning, among others, increase the resource demand for running applications with larger problem sizes. Elastic cloud computing resources with pay-per-use pricing offers new opportunities where large application execution is constrained only by the cost budget. Given a cost budget and a time deadline, this paper introduces a measurement-driven analytical modeling approach to determine the largest Pareto-optimal problem size and its corresponding cloud configuration for execution. We evaluate our approach with a set of representative applications that exhibit a range of resource demand growth patterns on Amazon AWS cloud. We show the existence of cost-time-size Pareto-frontier with multiple sweet spots meeting user constraints. To characterize the cost-performance of cloud resources, we use Performance Cost Ratio (PCR) metric. We extend Gustafson's fixed-time scaling in the context of cloud, and, investigate fixed-cost-time scaling of applications and show that using resources with higher PCR yields better cost-time performance. We discuss a number of useful insights on the trade-off between the execution time and the largest Pareto-optimal problem size, and, show that time deadline could be tightened for a proportionately much smaller reduction of problem size.