一句话：提出 Coach，利用 VM 资源的时间互补模式与预测调度实现全资源超额订阅，提升云平台利用率并控制性能退化。
云平台普遍存在资源利用率不足的问题；对 Azure 虚拟机的刻画显示 CPU 是主要未充分利用资源，但需要整体管理所有资源，且许多 VM 具有时间互补的资源使用模式。Coach 利用长期预测和高效 VM 调度策略挖掘这些模式，并引入 CoachVM：将每种资源分配划分为保证部分与超额订阅部分，同时监控超额资源以检测争用并缓解性能退化，尤其针对对争用敏感且重分配开销高的内存管理。实验表明，Coach 可让平台多承载约 26% 的 VM，且性能退化极小。
Cloud platforms remain underutilized despite multiple proposals to improve their utilization (e.g., disaggregation, harvesting, and oversubscription). Our characterization of the resource utilization of virtual machines (VMs) in Azure reveals that, while CPU is the main underutilized resource, we need to provide a solution to manage all resources holistically. We also observe that many VMs exhibit complementary temporal patterns, which can be leveraged to improve the oversubscription of underutilized resources. Based on these insights, we propose Coach: a system that exploits temporal patterns for all-resource oversubscription in cloud platforms. Coach uses long-term predictions and an efficient VM scheduling policy to exploit temporally complementary patterns. We introduce a new general-purpose VM type, called CoachVM, where we partition each resource allocation into a guaranteed and an oversubscribed portion. Coach monitors the oversubscribed resources to detect contention and mitigate any potential performance degradation. We focus on memory management, which is particularly challenging due to memory's sensitivity to contention and the overhead required to reassign it between CoachVMs. Our experiments show that Coach enables platforms to host up to ~26% more VMs with minimal performance degradation.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2501-11179,
title = {Coach: Exploiting Temporal Patterns for All-Resource Oversubscription in Cloud Platforms},
author = {Benjamin Reidys and Pantea Zardoshti and Íñigo Goiri and Celine Irvene and Daniel S. Berger and Haoran Ma and Kapil Arya and Eli Cortez and Taylor Stark and Eugene Bak and Mehmet Iyigun and Stanko Novakovic and Lisa Hsu and Karel Trueba and Abhisek Pan and Chetan Bansal and Saravan Rajmohan and Jian Huang and Ricardo Bianchini},
booktitle = {ASPLOS},
year = {2025},
doi = {10.1145/3669940.3707226}
}