Socially aware data partitioning for distributed storage of social data

Online social networking has become ubiquitous. For a social storage system to keep pace with increasing amounts of user data and activities, a natural solution is to deploy more servers. An important design problem then is how to partition the data across the servers so that server efficiency and load balancing can both be maximized. Although data partitioning is well-studied in the literature of distributed data systems, social data storage presents a unique challenge because of the social locality in data access: we need to factor in not only how actively users read and write their own data but also how often socially connected users read the data of one another. We investigate the socially aware data partitioning problem by modeling it as a multi-objective optimization problem and exploring the applicability of evolutionary algorithms in order to achieve highly-efficient and well-balanced data partitions. Especially, we propose a solution framework that is closer to being optimal than existing techniques are, which is substantiated in our evaluation study.