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Workload balancing and adaptive resource management for the swift storage system on cloud

机译：云上快速存储系统的工作负载平衡和自适应资源管理

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摘要

The demand for big data storage and processing has become a challenge in today's industry. To meet the challenge, there is an increasing number of enterprises adopting distributed storage systems. Frequently, in these systems, storage nodes intensively holding hotspot data could become system bottlenecks while storage nodes without hotspot data might result in low utilization of computing resource. This stems from the fact that almost all the typical distributed storage systems only provide data-amount-oriented balancing mechanisms without considering the different access load of data. To eliminate the system bottlenecks and optimize the resource utilization, there is a demand for such distributed storage systems to employ a workload balancing and adaptive resource management framework. In this paper, we propose a framework of workload balancing and resource management for Swift, a widely used and typical distributed storage system on cloud. In this framework, we design workload monitoring and analysis algorithms for discovering overloaded and underloaded nodes in the cluster. To balance the workload among those nodes, Split, Merge and Pair Algorithms are implemented to regulate physical machines while Resource Reallocate Algorithm is designed to regulate virtual machines on cloud. In addition, by leveraging the mature architecture of distributed storage systems, the framework resides in the hosts and operates through API interception. To demonstrate its effectiveness, we conduct experiments to evaluate it. And the experimental results show the framework can achieve its goals.
机译：对大数据存储和处理的需求已成为当今行业的挑战。为了应对挑战，越来越多的企业采用分布式存储系统。通常，在这些系统中，密集存储热点数据的存储节点可能会成为系统瓶颈，而没有热点数据的存储节点可能会导致计算资源利用率低。这是因为几乎所有典型的分布式存储系统都只提供面向数据量的平衡机制，而不考虑数据的不同访问负载。为了消除系统瓶颈并优化资源利用率，需要这种分布式存储系统采用工作负载平衡和自适应资源管理框架。在本文中，我们为Swift提出了工作负载平衡和资源管理的框架，Swift是一种在云上广泛使用且典型的分布式存储系统。在此框架中，我们设计了工作负载监视和分析算法，用于发现集群中的过载和欠载节点。为了平衡这些节点之间的工作负载，实施了拆分，合并和配对算法以管理物理机，而资源重新分配算法则被设计为管理云上的虚拟机。此外，通过利用分布式存储系统的成熟体系结构，该框架驻留在主机中并通过API拦截进行操作。为了证明其有效性，我们进行了实验以对其进行评估。实验结果表明该框架可以实现其目标。

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