Characterization and Management of Popular Content in KAD

Abstract

Abstract-The endeavor of this work is to study the impact of content popularity in a large-scale Peer-to-Peer network, namely KAD. Armed with the insights gained from an extensive measurement campaign, we pinpoint several deficiencies of the present KAD design in handling popular content, and provide a series of solutions to address such shortcomings. Among them, we design and evaluate an adaptive load balancing mechanism. Our mechanism is backward compatible with KAD, as it only modifies its inner algorithms, and presents several desirable properties: (i) it drives the process that selects the number and location of peers responsible to store references to objects, based on their popularity; (ii) it solves problems related to saturated peers, that would otherwise entail a significant drop in the diversity of references to objects, and (iii) if coupled with an enhanced content search procedure, it allows a more fair and efficient usage of peer resources, at a reasonable cost. Our evaluation uses a trace-driven simulator that features realistic peer churn and a precise implementation of the inner components of KAD.