Optimization of stream-based live data migration strategy in the cloud

摘要

Live data migration in the cloud is responsible to migrate blocks of data from one emigration node to several immigration nodes. However, live data migration strategy is a NP-hard problem like task scheduling. Recently, in-stream processing is a new technique to process large-scale data nearly instantaneously. This framework works fast that all decisions aremade without a continuous stream of events. In this paper,weexplore a real-time live datamigration strategywith stream processing paradigm. First, the nonlinearmigration costmodel and balance model are introduced as the metrics to evaluate the data migration strategy. Subsequently, a live data migration strategy with particle swarm optimization (PSO) is proposed. Two improvement measures called loop context and particle grouping are proposed.As an improvement of stream processing framework, nested loop context structure is a feedback to support iterative optimization algorithm. As an improvement of PSO, grouping particles before in-stream processing are to speed up the convergence rate of PSO. Afterwards, we rebuild stream processing framework to implement these methods. The experimental results show the best performance of our method.