Language-Directed Hardware Design for Network Performance Monitoring
Name
marple-sigcomm17.pdf
Description
Accepted version
Size
1.14 MB
Format
Adobe PDF
Checksum (MD5)
40f3fe9f080b89d9addf77c48499fe2f
Author(s)
Narayana, Srinivas
Sivaraman, Anirudh
Nathan, Vikram
Goyal, Prateesh
Arun, Venkat
Alizadeh, Mohammad
Jeyakumar, Vimalkumar
Kim, Changhoon
Date Issued
August 7, 2017
Publisher
ACM
Citation
Narayana, Srinivas, Sivaraman, Anirudh, Nathan, Vikram, Goyal, Prateesh, Arun, Venkat et al. 2017. "Language-Directed Hardware Design for Network Performance Monitoring."
Version
Author's final manuscript
Abstract
© 2017 ACM. 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 highspeed 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3098822.3098829
