Language-Directed Hardware Design for Network Performance Monitoring

By Srinivas Narayana, Anirudh Sivaraman, Vikram Nathan, Prateesh Goyal, Venkat Arun, Mohammad Alizadeh, Vimalkumar Jeyakumar, Changhoon Kim. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2017

337 citations recorded by the source corpus.

Network performance monitoring today is restricted by existing switch support for measurement, forcing operators to rely heavily on endpoints with poor visibility into the network core. Switch vendors have added progressively more monitoring features to switches, but the current trajectory of adding specific features is unsustainable given the ever-changing demands of network operators. Instead, we ask what switch hardware primitives are required to support an expressive language of network performance questions. We believe that the resulting switch hardware design could address a wide variety of current and future performance monitoring needs. We present a performance query language, Marple, modeled on familiar functional constructs like map, filter, groupby, and zip. Marple is backed by a new programmable key-value store primitive on switch hardware. The key-value store performs flexible aggregations at line rate (e.g., a moving average of queueing latencies per flow), and scales to millions of keys. We present a Marple compiler that targets a P4-programmable software switch and a simulator for high-speed programmable switches. Marple can express switch queries that could previously run only on end hosts, while Marple queries only occupy a modest fraction of a switch's hardware resources.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6de1045de20e79ae5b40284fad431ed501dd3f68

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In Computer Networks and Communications

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Citations in the corpus

Elastic sketch: adaptive and fast network-wide measurements (2018, methodology): “Recently, well-known systems for measurements include UnivMon [2], Trumpet [40], OpenSketch [11], FlowRadar [18], SketchVisor [12], Marple[41], Pingmesh[42], and DREAM[43].”
PINT: Probabilistic In-band Network Telemetry (2020, methodology): “Application Description Measurement Primitives Per-packet aggregation Congestion Control [22, 29, 40, 46] Congestion Control with in-network support timestamp, port utilization, queue occupancy Congestion Analysis [17, 38, 57] Diagnosis of short-lived congestion events queue occupancy Network Tomogr”
Sonata: query-driven streaming network telemetry (2018, methodology): “Some approaches rely on programmable data-plane targets such as PISA switches to execute telemetry tasks entirely in the data plane [20, 28, 44].”