Impact of socially based demand on the efficiency of caching strategy

Abstract

Abstract-Routers are often faced with a variety of users throughout a day. These users make requests, of which the most bandwidth consuming and least delay tolerant are requests for internet content including videos. To save cost of uploading all requested data, routers use content-aware caching schemes. The efficiency of such schemes is dependent on router ability to predict the use of the currently requested content. At the same time, research on social networks indicates that users with social ties have the correlated mobility patterns and the correlated interests in requested content. In this paper, we demonstrate that these correlations between mobility and between interests can be used to improve efficiency of content-based caching network caching by taking into account social ties between users visiting a router. To this end, we built a model for router visits and user demand based on social relations. We then used this model to create a content-aware caching scheme called "SOcial Content Caching Scheme (SOCCS)". Our results demonstrate that by using information about frequency of visit by members of different social communities combined with topic tags on the content requested by each community, the performance of routers can be increased remarkably compared to their performance with random caching. Moreover, such socially based caching is robust to changes in many parameters of our model that affect visits by mobile nodes, the tags of content that they request, and how history is incorporated into caching decisions.

Machine Learning with partially labeled Data for Indoor Outdoor Detection

“…Recent technological breakthroughs have extended the mobile phones' features, functions and capabilities, which are now used for more than just communicating or affording applications. Recently, mobile devices are being utilized to know the consuming habits of individuals and communities [1], [2], [3]. Thus, our purpose is to inject this learned cognition into mobile 5G networks to help them grow smarter and be more efficient when faced to the increasing complexity of network management combined with numerous new applications and their heterogeneous needs.…”