Improving the cost efficiency of large-scale cloud systems running hybrid workloads - A case study of Alibaba cluster traces

Abstract

The pandemic of coronavirus has dramatically disrupted the retail industry, as many stores are forced to close and people across the world are shelter-in-place with online shopping as the inevitable choice. To meet the rapidly increasing demand for e-commerce, more data centers are expected to provide new or significantly improve existing cloud services that can better support hybrid workloads (e.g. online purchase jobs and batch jobs that support ranking or recommendation systems). Successful cloud systems need to efficiently handle and quickly respond to huge volume of traffic with such hybrid workloads. Meanwhile, it is critical to reduce the total cost of ownership (TCO) for profitability. Improving system utilization is one of the effective techniques to achieve the twin goals of high performance and low TCO. This paper conducts a comprehensive analysis on the 2017 and 2018 cluster traces released by Alibaba, which provides a case study about Alibaba's best practices in improving the performance and cost efficiency of its large-scale cloud systems by consolidating time-sensitive online service jobs with time-insensitive batch jobs. Our investigation indicates that the over-subscription (causing resource waste and low utilization) and under-subscription (causing performance degradation) problems co-exist in the current Alibaba system. We develop a simulator that allows us to evaluate possible solutions to address this problem and their impact on the performance, energy consumption, and TCO. Our experiments show that the estimated TCO can be reduced by $600,000 for the 2018 trace running on over 4,000 machines without compromising performance. The TCO can decrease by nearly $68 million if similar strategy is extrapolated to Alibaba's 432,000 web facing servers.