Cloud Data Migration Method Based on PSO Algorithm

Geng Yu-shui, Yuan Jiaheng
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引用次数: 5

Abstract

Cloud storage system can play an important role in large-scale, and it supports high-performance cloud applications. To cloud storage systems, data migration is key technology to realize the nodes dynamically extensible and elastic load balancing. How to reduce migration cost of time is the problem that cloud service providers need to solve. Existing research efforts were focused on the data migration issues under the non-virtualized environments, which often do not applicable to cloud storage systems. In response to these challenges, we put data migration issues into the load-balancing scenarios to solve. We propose an algorithm based on particle swarm optimization algorithm which can reduces the cost of time. In the experiment, we can use Yahoo services benchmarking YCSB tool which could verify the validity of the method. It is a test framework designed to help users understand the different cloud computing, database performance.
基于粒子群算法的云数据迁移方法
云存储系统可以在大规模应用中发挥重要作用,支持高性能的云应用。对于云存储系统来说,数据迁移是实现节点动态扩展和弹性负载均衡的关键技术。如何降低迁移的时间成本是云服务提供商需要解决的问题。现有的研究主要集中在非虚拟化环境下的数据迁移问题,而非虚拟化环境往往不适用于云存储系统。为了应对这些挑战,我们将数据迁移问题放到负载平衡场景中来解决。我们提出了一种基于粒子群优化算法的算法,可以减少时间成本。在实验中,我们可以使用Yahoo服务对标YCSB工具来验证方法的有效性。它是一个测试框架,旨在帮助用户了解不同的云计算、数据库性能。
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