Latency-Aware Data Placements for Operational Cost Minimization of Distributed Data Centers

Abstract

A large amount of data are stored in geographically distributed data centers interconnected by the Internet. The power consumption for running the servers storing the data and inter-data center network transport for data update among the multiple data copies stored in different data centers impose a significant operational cost on cloud service providers. Few research places data replicas in the data centers by jointly taking both electricity consumption and network transport into account under the user access latency requirement constraints. However, there is an intrinsic tradeoff between the electricity consumption of the data centers and the network transport cost for data update. In this paper, we tackle the problem of data placement in data centers with the aim to minimize the operational cost under user access latency requirements, assuming all the data have K copies. We propose an effective algorithm, Latency-aware and operational Cost minimization Data Placement (LCDP), which partitions the data into multiple data groups according to the data access rates and greedily selects K data centers incurring the minimum cost for each data in each data group. We prove that algorithm LCDP is \(\frac{1}{2} \ln |\mathrm {U}|\)-approximation to the data placement problem, where \(|\mathrm {U}|\) is the number of users. Our simulation results demonstrate that the proposed algorithm can effectively reduce the power consumption cost, the network transport cost, and the operational cost of the data centers.