Performance Modeling and Evaluation for Information-Driven Networks

TL;DR

This paper introduces a new performance evaluation framework for information-driven networks, focusing on information delivery quality and scheduling, using stochastic network calculus to better align with the unique needs of such systems.

Contribution

It develops a novel performance model tailored for information-driven networks, addressing the limitations of traditional packet-oriented metrics and incorporating information processing considerations.

Findings

Framework enables calculation of network capacity for information delivery.

Supports transmission scheduling with stochastic QoS guarantees.

Addresses the gap in performance modeling for information-driven networks.

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 present a new performance evaluation framework particularly tailored for information-driven networks, based on the recent development of stochastic network calculus. We analyze the QoS with respect to information delivery and study the scheduling problem with the new performance metrics. Our analytical framework can be used to calculate the network capacity in information delivery and in the meantime to help transmission scheduling for a large body of systems where QoS is stochastically guaranteed with the redundancy in information.