Black-Box Performance Evaluation of Elastic Block Storage: Contract, Rate-Limiting Model, and Software Exploration

2026-07-22

AI Summary

This study addresses the significant performance gap between cloud-based Elastic Block Storage (EBS) and local SSDs, a disparity exacerbated by users’ limited understanding of EBS behavioral characteristics and lack of effective adaptation strategies. From an end-user perspective, the authors conduct black-box evaluations of elastic SSDs offered by major cloud providers and introduce, for the first time, an ESSD performance contract encompassing four key behavioral traits and five actionable configuration recommendations. They propose an enhanced rate-limiting model that jointly accounts for bandwidth and IOPS constraints with fine-grained token replenishment. Through a detailed case study on RocksDB, they derive practical software-level optimization guidelines. Experimental results demonstrate that their approach effectively mitigates I/O latency spikes, offering cloud storage users concrete and effective performance tuning strategies.

Abstract

Elastic block storage (EBS) with the storage-compute disaggregated architecture is a key component in modern cloud infrastructure. EBS offers users storage resources in the form of elastic solid-state drives (ESSDs). Nonetheless, despite recent efforts that have documented EBS architectures from the provider's perspective, how ESSDs perform differently from local SSDs and how host software should adapt accordingly have not been sufficiently studied. In this paper, we conduct a user-centric, black-box performance characterization of ESSDs from Amazon AWS and Alibaba Cloud. We make three main contributions: (1) an ESSD contract that presents four behavioral observations and five actionable implications for software adaptation, (2) a refined I/O rate-limiting model combining bandwidth-IOPS dual limiting and fine-grained token refilling to suppress latency spikes, and (3) a case study on RocksDB that derives four guidelines on cache management, I/O regulation, storage budget utilization, and compression algorithms. Collectively, we hope these contributions can serve as a practical reference for EBS users to understand and exploit the distinctive performance properties of ESSDs.

Problem

• Elastic Block Storage
• ESSD
• performance characterization
• software adaptation
• cloud storage

Innovation

• Elastic Block Storage
• Black-box Performance Evaluation
• Rate-limiting Model
• Software Adaptation
• Cloud Storage