SPINE: Stochastic Performance Bounds for Information-Centric Communication Networks

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The integration of content caching and packet forwarding functionalities is a recent evolutionary step in communication networks that is a consequence of a shift in the raison d'être of networking from connecting hosts to delivering content. Despite the recent rise of caching-capable networks, such as the concept of Information-Centric Networking (ICN), as candidates for future Internet architectures, the foundations of the interactions of caching and forwarding elements are still not well understood. Most methods are based on currently established content-delivery architectures that do not capture caching and queueing interactions on the network layer. For ICN, where, in addition, traditional notions of traffic flows and end-to-end connections do not hold, such methods introduce performance modeling errors that cannot be explained by these approaches.In this project, we aim at deriving a queuing theory based methodology for the performance analysis and adaptive control of information-centric networks that utilize stochastic communication systems. Such systems include caching-capable routers and schedulers under time-varying available bandwidth resources and bursty traffic. We expect that formulations from queueing theory and stochastic network calculus, such as Fork-Join models and statistical service curves, have the potential to yield new insights, e.g., regarding the optimality of ICN scheduling algorithms, which can lead to application-tailored communication schemes in ICN. We anticipate that adaptive control methods, such as Markov decision processes, paired with statistical inference algorithms allow gaining insights on fundamental performance limits for adaptive ICN communications. The expected results of this proposal will provide a fundamental understanding of the design space of ICN and enable rigorous optimizations of ICN protocols and applications that are beyond the state-of-the-art of today's theories, for example enabling optimal ICN packet scheduling for ICN traffic with Markov-additive cross traffic.