A scalable architecture for maintaining packet latency measurements

Latency has become an important metric for network monitoring since the emergence of new latency-sensitive applications (e.g., algorithmic trading and high-performance computing). In this paper, to provide latency measurements at both finer (e.g., packet) as well as flexible (e.g., flow subsets) levels of granularity, we propose an architecture called MAPLE that essentially stores packet-level latencies in routers and allows network operators to query the latency of arbitrary traffic sub-populations. MAPLE is built using a scalable data structure called SVBF with small storage needs.