Exploiting Recommender Service to Enhance Efficiency of Replication Modern, distributed systems are expected nowadays to provide high data availability, increased faulttolerance, low bandwidth consumption, and improved scalability. To cope with the aforementioned problems, the replication mechanisms are often used. However, with the constantly growing scale of modern systems, providing the efficient replication becomes increasingly difficult. One of the ways to increase the replication efficiency is associated with the use of knowledge from social networks. This paper proposes a recommender service that exploits social relationships to provide a list of nodes that will hold replicated data. Such nodes are selected based on the value of the proposed matching degree metric. Our focus is also on evaluating the proposed solution.

To Be or Not to Be: Analyzing & Modeling Social Recommendation in Online Social Networks Firms are now considering to offer rewards to customers who recommend the firmsu0027 productsservices in online social networks (OSN). However, the pros and cons of such social recommendation scheme are still unclear. Thus, it is difficult for firms to design rewarding schemes. Via empirical analysis of data, we first identify key factors that affect the spreading of a firmu0027s product in OSNs. These findings enable us to develop an accurate (i.e., with a high validation accuracy) mathematical model on social recommendations. In particular, our model captures how users decide whether to recommend an item, which is a key factor but often ignored by previous social recommendation models such as the Independent Cascade model. We also design algorithms to infer model parameters. Using these parameters in our model, we uncover conditions when social recommendation can (or cannot) improves a firmu0027s profit. These conditions help a firm to design rewarding schemes. Finally, we extend our model to an online setting and design reinforcement learning algorithms for a firm to dynamically optimize its rewarding schemes.