부산대학교 도서관

The rapid growth of data processing demands in large-scale data centers (DCs) has led to increased brown energy (BE) consumption, which has negative environmental impacts. Since most DCs are now powered by both BE and renewable energy (RE), migrating workloads from DCs with insufficient RE to DCs with sufficient RE can decrease the total BE consumption in the network. However, selecting a destination DC is challenging due to the uncertainty of the network and the additional cost associated with using network devices for the migration. This paper proposes to optimize the DC selection and the efficient virtual machine transfer between DCs, minimizing the costs of BE consumption, optical network devices, and migration. Specifically, we formulate the DC selection as a multi-armed bandit problem and estimate the lowest migration cost at each round using the lower confidence bound. We adopt the optical grooming technique to reduce the cost of optical devices used during the migration. We compare our algorithm with the KUBE and -Greedy algorithms on the NSFNET and show that it reduces the total cost by 4.6% and 12.8%, respectively, while having lower regret. We demonstrated the effectiveness of optical grooming by achieving a 12 % reduction in network costs.