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Parallel Particle Swarm Optimization for Reducing Data Redundancy in Heterogeneous Cloud Storage
Abstract— Cloud storage is usually distributed infrastructure, where data is not stored in a single device but is spread to several storage nodes which are located in different areas. To ensure data availability some amount of redundancy has to be maintained. But introduction of data redundancy leads to additional costs such as extra storage space and communication bandwidth which required for restoring data blocks. In the existing system, the storage infrastructure is considered as homogeneous where all nodes in the system have same online availability which leads to efficiency losses. The proposed system considers that distributed storage system is heterogeneous where each node exhibit different online availability. Monte Carlo Sampling is used to measure the online availability of storage nodes. The parallel version of Particle Swarm Optimization is used to assign redundant data blocks according to their online availability. The optimal data assignment policy reduces the redundancy and their associated cost.
Index Terms— cloud storage, data redundancy, online availability, optimal data assignment, particle swarm optimization
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International Journal for Trends in Technology & Engineering © 2015 IJTET JOURNAL