Performance Modeling and Evaluation for Information-Driven Networks

Information-driven networks include a large category of net-working systems, where network nodes are aware of infor-mation delivered and thus can not only forward data packetsbut may also perform information processing. In many sit-uations, the quality of service (QoS) in information-drivennetworks is provisioned with the redundancy in information.Traditional performance models generally adopt evaluationmeasures suitable for packet-oriented service guarantee, suchas packet delay, throughput, and packet loss rate. These per-formance measures, however, do not align well with the ac-tual need of information-driven networks. New performancemeasures and models for information-driven networks, de-spite their importance, have been mainly blank, largely be-cause information processing is clearly application depen-dent and cannot be easily captured within a generic frame-work. To fill the vacancy, we present a new performanceevaluation framework particularly tailored for information-driven networks, based on the recent development of stochas-tic network calculus. We analyze the QoS with respect toinformation delivery and study the scheduling problem withthe new performance metrics. Our analytical framework canbe used to calculate the network capacity in information de-livery and in the meantime to help transmission schedulingfor a large body of systems where QoS is stochastically guar-anteed with the redundancy in information.