A calculus for information-driven networks

TLDR: This work develops a new performance evaluation framework particularly tailored for information-driven networks, based on the recent development of stochastic network calculus, which captures the information processing and the QoS guarantee with respect to stochastics information delivery rates, which have never been formally modeled before.

Abstract: Information-driven networks include a large category of networking systems, where network nodes are aware of information delivered and thus can not only forward data packets but may also perform information processing. In many situations, the quality of service (QoS) in information-driven networks is provisioned with the redundancy in information. Traditional performance models generally adopt evaluation measures suitable for packet-oriented service guarantee, such as packet delay, throughput, and packet loss rate. These performance measures, however, do not align well with the actual need of information-driven networks. New performance measures and models for information-driven networks, despite their importance, have been mainly blank, largely because information processing is clearly application dependent and cannot be easily captured within a generic framework. To fill the vacancy, we develop a new performance evaluation framework particularly tailored for information-driven networks, based on the recent development of stochastic network calculus. Particularly, our model captures the information processing and the QoS guarantee with respect to stochastic information delivery rates, which have never been formally modeled before. This analytical model is very useful in deriving theoretical performance bounds for a large body of systems where QoS is stochastically guaranteed with a certain level of information delivery.