An In-Depth Analysis of Cloud Block Storage Workloads in Large-Scale Production
Jinhong Li, Qiuping Wang, Patrick P. C. Lee, Chao Shi
Abstract
Cloud block storage systems support diverse types of applications in modern cloud services. Characterizing their I/O activities is critical for guiding better system designs and optimizations. In this paper, we present an in-depth analysis of production cloud block storage workloads through the block-level I/O traces of billions of I/O requests collected from Alibaba Cloud. We study the characteristics of load intensity, spatial patterns, and temporal patterns. Also, we present a comparative study on our traces and the notable public block-level I/O traces from Microsoft Research Cambridge, and identify the commonalities and differences of the two sets of traces. Finally, we provide 15 findings and discuss their implications on load balancing, cache efficiency, and storage cluster management in a cloud block storage system. Our traces are now released for public use.