A framework for balancing information collection and data transmission

Network protocol performance is closely related to knowledge about the network state. However, acquiring such knowledge expends network bandwidth resource. Thus a trade off exists between the amount of bandwidth resource expended in acquiring knowledge about network state, and the improved protocol performance due to such knowledge. Previous work used rate distortion theory to calculate the minimum information required for certain network performance. However, this limit is asymptotic and might not be achievable due to the introduced infinite delay. This work develops a non-asymptotic framework to find a practical bound of the required information for certain network performance, and the strategies for implementing network information collection. The framework is illustrated by a wireless scheduling problem to show the quantitative relationship between collected traffic information and network throughput. Furthermore, we calculate the effective data rate by considering the overhead of network information collection, and determine the optimal resource allocation between information collection and data transmission.