Reliable Client Accounting for Hybrid Content-Distribution Networks

Content distribution networks (CDNs) have started to adopt hybrid designs, which employ both dedicated edge servers and resources contributed by clients. Hybrid designs combine many of the advantages of infrastructurebased and peer-to-peer systems, but they also present new challenges. This paper identifies reliable client accounting as one such challenge. Operators of hybrid CDNs are accountable to their customers (i.e., content providers) for the CDN’s performance. Therefore, they need to offer reliable quality of service and a detailed account of content served. Service quality and accurate accounting, however, depend in part on interactions among untrusted clients. Using the Akamai NetSession client network in a case study, we demonstrate that a small number of malicious clients used in a clever attack could cause significant accounting inaccuracies. We present a method for providing reliable accounting of client interactions in hybrid CDNs. The proposed method leverages the unique characteristics of hybrid systems to limit the loss of accounting accuracy and service quality caused by faulty or compromised clients. We also describe RCA, a system that applies this method to a commercial hybrid content-distribution network. Using trace-driven simulations, we show that RCA can detect and mitigate a variety of attacks, at the expense of a moderate increase in logging overhead. Disciplines Computer Sciences Comments Aditya, P., Zhao, M., Lin, Y., Haeberlen, A., Druschel, P., Maggs B., Wishon, B., Reliable Client Accounting for P2P-Infrastructure Hybrids, 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI'12), April 2012 Author(s) Paarijaat Aditya, Mingchen Zhao, Yin Lin, Andreas Haeberlen, Peter Druschel, Bruce Maggs, and Bill Wishon This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/606 Reliable Client Accounting for P2P-Infrastructure Hybrids Paarijaat Aditya† Mingchen Zhao‡ Yin Lin Andreas Haeberlen‡ Peter Druschel† Bruce Maggs Bill Wishon †Max Planck Institute for Software Systems (MPI-SWS) ‡University of Pennsylvania Duke University Akamai Technologies