Energy-aware replica selection for data-intensive services in cloud

With the increasing energy cost in data centers, an energy efficient approach to provide data intensive services in the cloud is highly in demand. This thesis solves the energy cost reduction problem of data centers by formulating an energy-aware replica selection problem in order to guide the distribution of workload among data centers. The current popular centralized replica selection approaches address such problems but they lack scalability and are vulnerable to a crash of the central coordinator. Also, they do not take total data center energy cost as the primary optimization target. We propose a simple decentralized replica selection system implemented with two distributed optimization algorithms (consensus-based distributed projected subgradient method and Lagrangian dual decomposition method) to work with clients as a decentralized coordinator. We also compare our energy-aware replica selection approach with the replica selection where a round-robin algorithm is implemented. A prototype of the decentralized replica selection system is designed and developed to collect energy consumption information of data centers. The results show that in the best case scenario of our experiments, the total energy cost using the Lagrangian dual decomposition method is 17.8% less than a baseline round-robin method and 15.3% less than consensus-based distributed projected subgradient method. Also, the prototype is proved to be working efficiently with low computation and communication overhead. The proposed decentralized energy-aware replica selection system can also be easily adapted to the real world cloud environment