Real-time Network Monitoring Supporting Percentile Error Objectives

We report on the versatility of A-GAP for supporting different types of accuracy objectives. Previously, we considered accuracy objectives expressed in terms of the average error. In this paper, we focus on percentile error objectives. A-GAP is a protocol for continuous monitoring of network state variables. Network state variables are computed from device counters using aggregation functions, such as SUM, AVERAGE and MAX. A-GAP is designed to achieve a given monitoring accuracy with minimal overhead. AGAP is decentralized and asynchronous to achieve robustness and scalability. It executes on an overlay that interconnects management processes on the devices. On this overlay, the protocol maintains a spanning tree and updates the network state variables through incremental aggregation. Based on a stochastic model, it dynamically configures local filters that control whether an update is sent towards the root of the tree. We evaluate A-GAP through simulation using real traces for an ISP topology (Abovenet). The results prove the versatility of AGAP for supporting different types of accuracy objectives. The results also show that we can effectively control the trade-off between accuracy and protocol overhead, and that the overhead can be reduced significantly by allowing small errors.