Adaptive Algorithms for Efficient Content Management in Social Network Services

Identifying the set of resources that are expected to receive the majority of requests in the near future, namely hot set, is at the basis of most content management strategies of any Web-based service. Here we consider social network services that open interesting novel challenges for the hot set identification. Indeed, social connections among the users and variable user access patterns with continuous operations of resource upload/download determine a highly variable and dynamic context for the stored resources. We propose adaptive algorithms that combine predictive and social information, and dynamically adjust their parameters according to continuously changing workload characteristics. A large set of experimental results show that adaptive algorithms can achieve performance close to theoretical ideal algorithms and, even more important, they guarantee stable results for a wide range of workload scenarios.