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首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >Profiling-Based Workload Consolidation and Migration in Virtualized Data Centers
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Profiling-Based Workload Consolidation and Migration in Virtualized Data Centers

机译：虚拟数据中心中基于分析的工作负载合并和迁移

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摘要

Improving energy efficiency of data centers has become increasingly important nowadays due to the significant amounts of power needed to operate these centers. An important method for achieving energy efficiency is server consolidation supported by virtualization. However, server consolidation may incur significant degradation to workload performance due to virtual machine (VM) co-location and migration. How to reduce such performance degradation becomes a critical issue to address. In this paper, we propose a profiling-based server consolidation framework which minimizes the number of physical machines (PMs) used in data centers while maintaining satisfactory performance of various workloads. Inside this framework, we first profile the performance losses of various workloads under two situations: running in co-location and experiencing migrations. We then design two modules: (1) consolidation planning module which, given a set of workloads, minimizes the number of PMs by an integer programming model, and (2) migration planning module which, given a source VM placement scenario and a target VM placement scenario, minimizes the number of VM migrations by a polynomial time algorithm. Also, based on the workload performance profiles, both modules can guarantee the performance losses of various workloads below configurable thresholds. Our experiments for workload profiling are conducted with real data center workloads and our experiments on our two modules validate the integer programming model and the polynomial time algorithm.
机译：由于运营这些数据中心需要大量的电能，因此如今提高数据中心的能源效率变得越来越重要。实现能源效率的重要方法是虚拟化支持的服务器整合。但是，由于虚拟机（VM）的共置和迁移，服务器整合可能导致工作负载性能显着下降。如何减少这种性能下降成为要解决的关键问题。在本文中，我们提出了一个基于配置文件的服务器合并框架，该框架可最大程度地减少数据中心中使用的物理机（PM）的数量，同时保持令人满意的各种工作负载性能。在此框架内，我们首先分析两种情况下各种工作负载的性能损失：在同一位置运行和经历迁移。然后，我们设计两个模块：（1）合并计划模块，在给定一组工作负载的情况下，通过整数编程模型将PM的数量减至最少；（2）迁移计划模块，在给定源VM放置方案和目标VM的情况下放置方案，通过多项式时间算法最大程度地减少了VM迁移的次数。此外，基于工作负载性能配置文件，两个模块都可以保证各种工作负载的性能损失低于可配置的阈值。我们的工作负载分析实验是在实际的数据中心工作负载下进行的，而我们在两个模块上的实验验证了整数编程模型和多项式时间算法。

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