AUTHORS: Kun Ma, Qiuchen Cheng, Bo Yang

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ABSTRACT: Live data migration in the cloud is responsible to migrate blocks of data of emigration node to several immigration node. However, live data migration strategy is a NP-hard problem like task scheduling. Recently, in-stream processing is the immediate need in many practical applications. Therefore, we explore a real-time live data migration strategy with stream processing framework in this paper. First, the migration cost and balance model is introduced as the metrics to evaluate data migration strategy. Subsequently, a live data migration strategy with particle swarm optimization is proposed. Afterwards, we implement this method using stream processing framework. The experimental results show the best performance of our method in all

KEYWORDS: load balancing, stream processing, data migration, particle swarm optimization

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