Abstract: This paper analyzed and studied the dynamic behavior rules on power management and control in complex network storage systems. An Ideal Energy-Efficient Data Placement model (IEEDP) for distributed storage systems was proposed based on the analysis of the disk power model in network storage systems. On the basis of IEEDP, by combining the techniques of data migration and data replication, a 2-D cellular automata model, named Green Network Storage System model (GNSSCA) was proposed. The simulation results show that some complicated temporal and spatial behaviors evolve from the adjustment of local cells. The overall number of replicas of the system increases as the load becomes heavier, and finally it tends to a stable sate. Moreover, when the load is light, it can be seen that there is an approximate power law distribution of the entropy of request queue length.

Key words: Network storage system, Power management, Dynamic behavior, Cellular automata, Data migration, Data replication