Leave Them Microseconds Alone: 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). To satisfy the need, researchers have proposed new architectures such as LDA and RLI that can provide finegrained latency measurements. However, these architectures are fundamentally ossified in their design as they are designed to provide only a specific pre-configured aggregate measurement—either average latency across all packets (LDA) or per-flow latency measurements (RLI). Network operators, however, need latency measurements at both finer (e.g., packet) as well as flexible (e.g., flow subsets) levels of granularity. To bridge this gap, 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 scalable data structures with small storage needs (uses only 12.8 bits/pkt), and uses optimizations such as range queries to reduce the query bandwidth significantly (by a factor of 10 compared to the naive).