Analysis of and Optimization for Write-dominated Hybrid Storage Nodes in Cloud

Liu, Shuyang; Wang, Shucheng; Cao, Qiang; Lu, Ziyi; Jiang, Hong; Yao, Jie; Dong, Yuanyuan; Yang, Peihui

doi:10.1145/3357223.3362705

Cited by 14 publications

(1 citation statement)

References 17 publications

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“…Modern cloud environments often host much more diverse types of applications, some of which feature unique characteristics (e.g., short-lived tasks [33]) that are not commonly found in traditional data center environments. Also, the workloads in MSRC are generally read-dominant [34], while the workloads in cloud environments are often write-dominant due to the heavy use of read caches in cloud applications [27,45]. Such mismatches motivate the need of collecting and analyzing comprehensive block-level I/O traces from real-world cloud block storage systems in large-scale production.…”

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Wang²,

Lee³

et al. 2022

Preprint

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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 six 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.* An earlier version of this article appeared in [24]. In this extended version, we further include the workload traces from Tencent Cloud Block Storage [51] in our analysis. We extend our findings to show the commonalities and differences between the cloud block storage workloads from Alibaba Cloud and Tencent Cloud Block Storage.

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Section: Introductionmentioning

confidence: 99%