Diagnosis and Control of Network Variability by Massively Accessed Servers

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This proposal seeks funding to investigate a number of basic research problems within the general framework of network-aware server protocols and services. Tackling these problems is critical for the continued scalability of the Internet. Mass servers are popular Internet servers which produce a substantial fraction of the traffic flowing through the network. Currently, such Mass servers are tuned to optimize their own performance (e.g. capacity, latency, I/O utilization), while overlooking the opportunity to monitor network conditions and to use that information advantageously. This information could be used both to improve network behavior (e.g. alleviate the problems resulting from congestion) and in further optimizing the performance of servicing requests. Thus, Mass servers are uniquely positioned (1) to observe and diagnose network conditions by tracking the flows that they generate, and (2) to manage and control network resources by better regulating and scheduling the traffic they inject into the network. These goals must be pursued over a wide spectrum of time scales to maximize the beneficial impact that can be achieved. On the shortest time scales, a Mass server can minimize packet loss by smoothing the otherwise bursty process of injecting packets into the network. At medium time scales, a Mass server can perform aggregate congestion management by bundling like connections to avoid the burstiness that results from competition among flows. At even longer time scales, a Mass server can map persistent hotspots in the network and optimize scheduling of connections to mitigate the impact of overusing those resources. These objectives are representative of projects which the researchers intend to undertake on these various time scales. The research work outlined in this proposal aims at achieving these benefits through the derivation of new measurement and analysis techniques to enable diagnosis of network conditions, and the development of new services and protocol to enable efficient network resource management and control. Proposed work in network measurement and diagnosis at Mass servers focuses on the use of passive and active probing techniques for the identification of network bottlenecks (e.g. congestion conditions) along various time scales. The techniques to be used range from multi-resolution analysis using discrete wavelet transforms, to maximum likelihood estimators for packet loss estimation. Empowered with such diagnostic information, the proposed work in network resource management and control at Mass servers focuses on alleviating the problems associated with network bottlenecks (e.g. large delays and jitters, low and unfair resource utilization) along various time scales. The techniques to be used range from the development of traffic pacing protocols to alleviate burstiness at the sub-round-trip-time scale, to aggregate congestion control algorithms for improving utilization of network resources and reducing jitter. A key component of the proposed work is implementation and prototyping. To that end, the utility of the tools and protocols developed will be demonstrated by integrating them into three modular components for distribution to the research community, namely: (1) BEACON: A collection of network measurement and diagnosis tools, (2) TURNPIKE: A collection of network management and control protocols and services, and (3) BACKBAY: A platform that supports the integration of BEACON and TURNPIKE functionality into a high performance web server architecture. The pursuit of the research goals outlined in this proposal is timely. Achieving these goals will leapfrog current piecemeal attempts aiming at supporting Internet growth. The research team assembled to pursue these ambitious goals has made significant, nationally-recognized contributions to the understanding of Internet traffic characteristics and has an established record in software development and technology transfer. Boston University is committed to supporting this team through substantial financial and infrastructural commitments that complement and leverage the support sought from NSF.