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Cloud data centers have started utilizing erasure coding in large-scale storage systems to ensure high reliability with limited overhead compared to replication. However, data recovery in erasure coding incurs high network bandwidth consumption compared to replication. Cloud storage systems also play an important role in the energy consumption of data centers. Heuristic proactive recovery algorithms select all data blocks from failure-predicted disk/machine and perform proactive replication that contributes to huge recovery bandwidth savings. However, they fail to optimize the selection. Optimization can further improve resource savings. To address this issue, we propose a recovery algorithm that applies minimization on data blocks selected for proactive replication by considering the necessary and appropriate constraints that are constructed based on the system's current network traffic and data duplication information. We evaluate the proposed algorithm using extensive simulations. Experiments show that the recovery algorithm reduces network traffic by 60% and storage overhead by 46% compared to the heuristic proactive recovery approach. Also, the proposed proactive recovery methods reduce the storage system's energy consumption by up to 52% compared to replication.

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