Cloud computing offers a cost-effective and elastic computing paradigm that facilitates large scale data storage and analytics. By deploying virtualization technologies in the datacenter, cloud enables efficient resource management and isolation for various big data applications. Since the hotspots (i.e., overloaded machines) can degrade the performance of these applications, virtual machine migration has been utilized to perform load balancing in the datacenters to eliminate hotspots and guarantee Service Level Agreements (SLAs). However, the previous load balancing schemes make migration decisions based on deterministic resource demand estimation and workload characterization, without considering their stochastic properties. By studying real world traces, we show that the resource demand and workload of virtual machines are highly dynamic and bursty, which can cause these schemes to make inefficient migrations for load balancing. To address this problem, in this paper we propose a stochastic load balancing scheme which aims to provide probabilistic guarantee against the resource overloading with virtual machine migration, while minimizing the total migration overhead. Our scheme effectively addresses the prediction of the distribution of resource demand and the multidimensional resource requirements with stochastic characterization. Moreover, as opposed to the previous works that measure the migration cost without considering the network topology, our scheme explicitly takes into account the distance between the source physical machine and the destination physical machine for a virtual machine migration. The trace-driven experiments show that our scheme outperforms the previous schemes in terms of SLA violation and the migration cost.

中文翻译：

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数据中心虚拟资源管理的随机负载平衡

云计算提供了一种经济高效且具有弹性的计算范式，可促进大规模数据存储和分析。通过在数据中心部署虚拟化技术，云为各种大数据应用提供了高效的资源管理和隔离。由于热点（即过载的机器）会降低这些应用程序的性能，因此已利用虚拟机迁移在数据中心执行负载平衡，以消除热点并保证服务水平协议 (SLA)。然而，之前的负载平衡方案基于确定性资源需求估计和工作负载特征做出迁移决策，而没有考虑它们的随机特性。通过研究现实世界的痕迹，我们表明虚拟机的资源需求和工作负载是高度动态和突发的，这可能导致这些方案为负载平衡进行低效迁移。为了解决这个问题，在本文中，我们提出了一种随机负载平衡方案，旨在为虚拟机迁移带来的资源过载提供概率保证，同时最小化总迁移开销。我们的方案有效地解决了资源需求分布的预测和具有随机特征的多维资源需求。此外，与之前在不考虑网络拓扑的情况下测量迁移成本的工作相反，我们的方案明确考虑了虚拟机迁移的源物理机和目标物理机之间的距离。跟踪驱动的实验表明，我们的方案在违反 SLA 和迁移成本方面优于以前的方案。