一句话：首次研究可突发虚拟机特征，提出非参数统计资源分配器 Audible，兼顾高利用率与容量违规约束。
云提供商为提升资源利用率推出可突发虚拟机（BVM），但其负载特征和资源分配尚缺乏研究。本文首次基于某大型云厂商生产系统 trace 分析 BVM 特征，并研究 BVM 资源分配方法。作者用现场数据驱动的大规模模拟，将新方法与实践中两种基线（固定超额订阅比、基于中心极限定理）对比，发现传统方法要么利用率低，要么服务器容量违规率高。受负载研究启发，提出 Audible：一种基于非参数统计模型的轻量级、负载无关调度方法，无需训练 ML 模型或调参。结果表明 Audible 在严格限制服务器容量违规的同时实现高系统利用率。
In an effort to increase the utilization of data center resources cloud providers have introduced a new type of virtual machine (VM) offering, called a burstable VM (BVM). Our work is the first to study the characteristics of burstable VMs (based on traces from production systems at a major cloud provider) and resource allocation approaches for BVM workloads. We propose new approaches for BVM resource allocation and use extensive simulations driven by field data to compare them with two baseline approaches used in practice. We find that traditional approaches based on using a fixed oversubscription ratio or based on the Central Limit Theorem do not work well for BVMs: They lead to either low utilization or high server capacity violation rates. Based on the lessons learned from our workload study, we develop a new approach to BVM scheduling, called Audible, using a non-parametric statistical model, which makes the approach light-weight and workload independent, and obviates the need for training machine learning models and for tuning their parameters. We show that Audible achieves high system utilization while being able to enforce stringent requirements on server capacity violations.
@inproceedings{JandaghiMMEAS24,
title = {AUDIBLE: A Convolution-Based Resource Allocator for Oversubscribing Burstable Virtual Machines},
author = {Seyedali Jokar Jandaghi and Kaveh Mahdaviani and Amirhossein Mirhosseini and Sameh Elnikety and Cristiana Amza and Bianca Schroeder},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3620666.3651376}
}