An Analytical Framework for Resource Allocation Between Data and Delayed Network State Information

The data transmission performance of a network protocol is closely related to the amount of available information about the network state. In general, more network state information results in better data transmission performance. However, acquiring such state information expends network bandwidth resource. Thus, a trade-off exists between the amount of network state information collected, and the improved protocol performance due to this information. A framework has been developed in the previous efforts to study the optimal trade-off between the amount of collected information and network performance. However, the effect of information delay is not considered in the previous analysis. In this paper, we extend the framework to study the relationship between the amount of collected state information and the achievable network performance under the assumption that information is subject to delay. Based on the relationship we could then obtain the optimal resource allocation between the data transmission and network state information acquisition in a time-varying network. We have considered both memoryless and memory-exploited scenarios in our framework. Structures of the Pareto optimal information collection and decision-making strategies are discussed. Examples of multiuser scheduling and multi-hop routing are used to demonstrate the framework's application to practical network protocols.