Query Planning for Robust and Scalable Hybrid Network Telemetry Systems

Abstract

Network telemetry systems have become hybrid combinations of state-of-the-art stream processors and modern programmable data-plane devices. However, the existing designs of such systems have not focused on ensuring that these systems are also deployable in practice, i.e., able to scale and deal with the dynamics in real-world traffic and query workloads. Unfortunately, efforts to scale these hybrid systems are hampered by severe constraints on available compute resources in the data plane (e.g., memory, ALUs). Similarly, the limited runtime programmability of existing hardware data-plane targets critically affects efforts to make these systems robust. This paper presents the design and implementation of DynaMap, a new hybrid telemetry system that is both robust and scalable. By planning for telemetry queries dynamically, DynaMap allows the remapping of stateful dataflow operators to data-plane registers at runtime. We model the problem of mapping dataflow operators to data-plane targets formally and develop a new heuristic algorithm for solving this problem. We implement our algorithm in prototype and demonstrate its feasibility with existing hardware targets based on Intel Tofino. Using traffic workloads from different real-world production networks, we show that our prototype of DynaMap improves performance on average by 1-2 orders of magnitude over state-of-the-art hybrid systems that use only static query planning.