Computer Science
Generalized sketch families for network traffic measurement
Document Type
Article
Abstract
Traffic measurement provides critical information for network management, resource allocation, traffic engineering, and attack detection. Most prior art has been geared towards specific application needs with specific performance objectives. To support diverse requirements with efficient and future-proof implementation, this paper takes a new approach to establish common frameworks, each for a family of traffic measurement solutions that share the same implementation structure, providing a high level of generality, for both size and spread measurements and for all flows. The designs support many options of performance-overhead tradeoff with as few as one memory update per packet and as little space as several bits per flow on average. Such a family-based approach will unify implementation by removing redundancy from different measurement tasks and support reconfigurability in a plug-n-play manner. We demonstrate the connection and difference in the design of these traffic measurement families and perform experimental comparisons on hardware/software platforms to find their tradeoff, which provide practical guidance for which solutions to use under given performance goals.
Publication Title
Performance Evaluation Review
Publication Date
2020
Volume
48
Issue
1
First Page
63
Last Page
64
ISSN
0163-5999
DOI
10.1145/3393691.3394191
Keywords
big network data, generalized sketch families, network traffic measurement
Repository Citation
Zhou, You; Zhang, Youlin; Ma, Chaoyi; Chen, Shigang; and Odegbile, Olufemi O., "Generalized sketch families for network traffic measurement" (2020). Computer Science. 176.
https://commons.clarku.edu/faculty_computer_sciences/176
APA Citation
Zhou, Y., Zhang, Y., Ma, C., Chen, S., & Odegbile, O. O. (2020). Generalized sketch families for network traffic measurement. ACM SIGMETRICS Performance Evaluation Review, 48(1), 63-64.