Less Can Be More: Micro-managing VMs in Amazon EC2
Less Can Be More: Micro-managing VMs in Amazon EC2
复制标题
少即是多：对 Amazon EC2 中的虚拟机进行微观管理
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
E. Smirni
中科院分区：
文献类型：
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作者：
Jiawei Wen;Lei Lu;G. Casale;E. Smirni
微实例（t1. micro）是一类Amazon EC2虚拟机（VM），可为具有短暂CPU需求的应用程序提供最低的运营成本。随着处理的进行，EC2以一种复杂的、不可预测的方式限制微实例的CPU容量。本文旨在使微实例更可预测和更有效地使用。首先，我们提出了一个EC2微实例的特性，评估成本，性能，空闲和CPU节流之间的复杂的相互作用。接下来，我们定义自适应算法来管理CPU消耗，方法是在运行时学习工作负载特征，并注入空闲来减少主机级的限制。我们表明，梯度山战略导致有利的结果。对于CPU受限的工作负载，我们观察到相当一部分作业（高达65%）的端到端时间甚至比更昂贵的m1短四倍。小班。我们的算法大大减少了微实例上作业执行时间的长尾，即使是小实例也能进行有利的比较。
Micro instances (t1. micro) are the class of Amazon EC2 virtual machines (VMs) offering the lowest operational costs for applications with short bursts in their CPU requirements. As processing proceeds, EC2 throttles CPU capacity of micro instances in a complex, unpredictable, manner. This paper aims at making micro instances more predictable and efficient to use. First, we present a characterization of EC2 micro instances that evaluates the complex interactions between cost, performance, idleness and CPU throttling. Next, we define adaptive algorithms to manage CPU consumption by learning the workload characteristics at runtime and by injecting idleness to diminish host-level throttling. We show that a gradient-hill strategy leads to favorable results. For CPU bound workloads, we observe that a significant portion of jobs (up to 65%) can have end-to-end times that are even four times shorter than those of the more expensive m1. small class. Our algorithms drastically reduce the long tails of job execution times on the micro instances, resulting to favorable comparisons against even small instances.