Tracking the State of Large Dynamic Networks Via Reinforcement Learning

A Network Inventory Manager (NIM) is a software solution that scans, processes and records data about all devices in a network. We consider the problem faced by a NIM that can send out a limited number of probes to track changes in a large, dynamic network. The underlying change rate for the Network Elements (NEs) is unknown and may be highly non-uniform. The NIM should concentrate its probe budget on the NEs that change most frequently with the ultimate goal of minimizing the weighted Fraction of Stale Time (wFOST) of the inventory. However, the NIM cannot discover the change rate of a NE unless the NE is repeatedly probed.We develop and analyze two algorithms based on Reinforcement Learning to solve this exploration-vs-exploitation problem. The first is motivated by the Thompson Sampling method and the second is derived from the Robbins-Monro stochastic learning paradigm. We show that for a fixed probe budget, both of these algorithms produce a potentially unbounded improvement in terms of wFOST compared to the baseline algorithm that divides the probe budget equally between all NEs. Our simulations of practical scenarios show optimal performance in minimizing wFOST while discovering the change rate of the NEs.