Computing Histograms of Local Variables for Real-Time Monitoring using Aggregation Trees

In this paper we present a protocol for the continuous monitoring of a local network state variable. Our aim is to provide a management station with the value distribution of the local variables across the network, by means of partial histogram aggregation, with minimum protocol overhead. Our protocol is decentralized and asynchronous to achieve robustness and scalability, and it executes on an overlay interconnecting management processes in network devices. On this overlay, the protocol maintains a spanning tree and updates the histogram of the network state variables through incremental aggregation. The protocol allows to control the trade-off between protocol overhead and a global accuracy objective. This functionality is implemented by a dynamic configuration of local error filters that control whether an update is sent towards the management station or not. We evaluate our protocol by means of simulations. Our results demonstrate the controllability of our method in a wide selection of scenarios, and the scalability of our protocol for large-scale networks.