An In-Depth Comparative Analysis of Cloud Block Storage Workloads: Findings and Implications

TL;DR

This paper provides a comprehensive analysis of cloud block storage workloads from Alibaba and Tencent, revealing key characteristics and differences that inform system design and optimization strategies.

Contribution

It offers the first detailed comparison of cloud block storage workloads across multiple providers, highlighting commonalities and differences to guide improvements.

Findings

Identified load intensity patterns across workloads.

Revealed spatial and temporal access patterns.

Compared cloud workloads with enterprise data center traces.

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 comparative analysis of production cloud block storage workloads through the block-level I/O traces of billions of I/O requests collected from two production systems, Alibaba Cloud and Tencent Cloud Block Storage. We study their characteristics of load intensities, spatial patterns, and temporal patterns. We also compare the cloud block storage workloads with the notable public block-level I/O workloads from the enterprise data centers at Microsoft Research Cambridge, and identify the commonalities and differences of the three sources of traces. To this end, we provide 6 findings through the high-level analysis and 16 findings through the detailed analysis on load intensity, spatial patterns, and temporal patterns. We discuss the implications of our findings on load balancing, cache efficiency, and storage cluster management in cloud block storage systems.