一句话：研究云平台内存收割型虚拟机，提出缓冲、批处理与预回收技术，并改造 Hadoop 与 FaaS 适配可变内存，最高省 93% 成本。
论文研究云平台中内存收割的挑战与实现。已有资源收割 VM 只收割 CPU 核心而固定内存，浪费空闲内存变现机会，还可能限制 CPU 收割效果。作者刻画 Azure 中空闲内存的总量与动态，分析内存收割对 VM 创建时间、NUMA 跨节点和页碎片的影响；设计并实现内存收割型虚拟机（MHVM），提出内存缓冲、批处理和预回收技术，并扩展 Hadoop 与 FaaS 平台以适配 MHVM。结果表明：真实平台存在大量可收割内存；MHVM 能有效缓解收割负面影响；Hadoop 与 FaaS 扩展对用户作业和函数隐藏内存大小变化；运行 MHVM 工作负载最多节省 93% 成本。
loud platforms monetize their spare capacity by renting “Spot” virtual machines (VMs) that can be evicted in favor of higher-priority VMs. Recent work has shown that resource-harvesting VMs are more effective at exploiting spare capacity than Spot VMs, while also reducing the number of evictions. However, the prior work focused on harvesting CPU cores while keeping memory size fixed. This wastes a substantial monetization opportunity and may even limit the ability of harvesting VMs to leverage spare cores. Thus, in this paper, we explore memory harvesting and its challenges in real cloud platforms, namely its impact on VM creation time, NUMA spanning, and page fragmentation. We start by characterizing the amount and dynamics of the spare memory in Azure. We then design and implement memory-harvesting VMs (MHVMs), introducing new techniques for memory buffering, batching, and pre-reclamation. To demonstrate the use of MHVMs, we also extend a popular cluster scheduling framework (Hadoop) and a FaaS platform to adapt to them. Our main results show that (1) there is plenty of scope for memory harvesting in real platforms; (2) MHVMs are effective at mitigating the negative impacts of harvesting; and (3) our extensions of Hadoop and FaaS successfully hide the MHVMs’ varying memory size from the users’ data-processing jobs and functions. We conclude that memory harvesting has great potential for practical deployment and users can save up to 93% of their costs when running workloads on MHVMs.
@inproceedings{FuerstNGCSABBIB22,
title = {Memory-harvesting VMs in cloud platforms},
author = {Alexander Fuerst and Stanko Novaković and Íñigo Goiri and Gohar Irfan Chaudhry and Prateek Sharma and Kapil Arya and Kevin Broas and Eugene Bak and Mehmet Iyigun and Ricardo Bianchini},
booktitle = {ASPLOS},
year = {2022},
doi = {10.1145/3503222.3507725}
}