Characterization and Management of Popular Content in KAD

he endeavor of this work is to study the impact of content popularity in a large-scale Peer-to-Peer network, namely kad. Based on an extensive measurement campaign, we pinpoint several deficiencies of kad in handling popular content and provide a series of improvements to address such shortcomings. Our work reveals that keywords, which are associated to content, may become popular for two distinct reasons. First, we show that some keywords are intrinsically popular because they are common to many disparate contents: in such case we ameliorate kad by introducing a simple mechanism that identifies stopwords. Then, we focus on keyword popularity that directly relates to popular content. We design and evaluate an adaptive load balancing mechanism that is backward compatible with the original implementation of kad. Our scheme features the following properties: (i) it drives the process that selects the location of peers responsible to store references to objects, based on object popularity; (ii) it solves problems related to saturated peers that would otherwise inflict a significant drop in the diversity of references to objects, and (iii) if coupled with a load-aware content search procedure, it allows for a more fair and efficient usage of peer resources.