一句话：提出首个时间负载感知的云虚拟磁盘放置方案 Tela，通过预测负载实现削峰、负载均衡并防止过载。
云块存储依赖云虚拟磁盘（CVD）为云虚拟机提供块接口。将用户订阅的 CVD 分配到数据中心的物理存储仓库，即 CVD 放置，会显著影响资源利用率、负载均衡和 I/O 性能。已有工作未考虑云负载的时间波动，导致负载不均、资源利用率低和仓库频繁过载。为此，论文提出 Tela，首个时间负载感知的 CVD 放置方案。Tela 使用一系列可解释模型预测 CVD 的时间负载特征与取值，以及仓库中可能出现的峰值负载。在这些预测指导下，Tela 按负载模式将 CVD 放入仓库，以实现削峰和负载均衡，同时避免过载。实验结果显示，Tela 显著优于当前最先进方案：过载发生次数减少 86.8–93.8%，P99 过载持续时间减少 92.6%，负载不均衡降低 36.7–44.4%。
Cloud Block Storage (CBS) relies on Cloud Virtual Disks (CVDs) to provide block interfaces to Cloud Virtual Machines. The process of allocating user-subscribed CVDs to physical storage warehouses in cloud data centers, known as CVD placement, significantly impacts resource utilization, load balancing, and I/O performance. However, previous works have failed to account for temporal fluctuations in cloud loads, resulting in imbalanced loads, low resource utilization, and frequent warehouse overloads. To address these issues, we propose Tela, the first temporal load-aware CVD placement scheme. Using a series of interpretable models, Tela predicts the temporal load characteristics and values of CVDs, as well as potential peak loads in warehouses. Guided by these predictions, TELA places CVDs into warehouses according to their load patterns, aiming for peak shaving and load balancing while preventing overloads. Experimental results show that Tela significantly outperforms the state-of-the-art scheme, reducing overload occurrences by 86.8-93.8%, reducing P99 overload duration by 92.6%, and decreasing load imbalance by 36.7-44.4%.
@inproceedings{TanLWLLQ0XT25,
title = {<scp>Tela:</scp>
A Temporal Load-Aware Cloud Virtual Disk Placement Scheme},
author = {Difan Tan and Jiawei Li and Hua Wang and Xiaoxiao Li and Wenbo Liu and Zijin Qin and Ke Zhou and Ming Xie and Mengling Tao},
booktitle = {ASPLOS},
year = {2025},
doi = {10.1145/3669940.3707252}
}