A learning-based measurement framework for traffic matrix inference in software defined networks

Journal Article · Tue Dec 12 23:00:00 EST 2017 · Computers and Electrical Engineering

DOI:https://doi.org/10.1016/j.compeleceng.2017.11.020· OSTI ID:1526500

Malboubi, Mehdi ^[1]; Peng, Shu-Ming ^[1]; Sharma, Puneet ^[2]; Chuah, Chen-Nee ^[1]

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1. University of California, Davis, CA (United States)
2. Hewlett Packard Labs, Palo Alto, CA (United States)

Here, we propose an intelligent framework for Traffic Matrix (TM) inference in Software Defined Networks (SDN) where the Ternary Content Addressable Memory (TCAM) entries of switches are partitioned into two parts to: (1) effectively aggregate part of incoming flows for aggregate measurements, and (2) de-aggregate and directly measure the most informative flows for per-flow measurements. These measurements are then processed to effectively estimate the size of network flows. Under hard resource constraints of limited TCAM sizes, we show how to design the optimal and efficient-compressed flow aggregation matrices. We propose an optimal Multi-Armed Bandit (MAB) based algorithm to adaptively measure the most rewarding flows. We evaluate the performance of our framework using real traffic traces from different network environments and by considering two main applications: TM estimation and Heavy Hitter (HH) detection. Moreover, we have implemented a prototype of our framework in Mininet to demonstrate its effectiveness.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC); National Science Foundation (NSF)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1526500
• Journal Information: Computers and Electrical Engineering, Journal Name: Computers and Electrical Engineering Journal Issue: C Vol. 66; ISSN 0045-7906
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
97 MATHEMATICS AND COMPUTING
compressed sensing
multi-armed bandit algorithms
network measurement and inference
traffic matrix estimation
software defined networking