Software defined network inference with evolutionary optimal observation matrices

Journal Article · Mon Sep 11 00:00:00 EDT 2017 · Computer Networks

DOI:https://doi.org/10.1016/j.comnet.2017.09.001· OSTI ID:1525271

Malboubi, Mehdi ^[1]; Gong, Yanlei ^[2]; Yang, Zijun ^[2]; Wang, Xiong ^[2]; Chuah, Chen-Nee ^[1]; Sharma, Puneet ^[3]

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1. Univ. of California, Davis, CA (United States)
2. University of Electronic Science and Technology of China, Chengdu (China)
3. Hewlett-Packard (HP) Labs, Palo Alto, CA

A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics. In today's large-scale networks, this is a challenging task due to the scarcity of network measurement resources and the hard constraints that this imposes. Here, this paper proposes a new framework, called SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can lead to the best achievable estimation accuracy using Matrix Completion (MC) techniques. To cope with the complexity of designing large-scale optimal observation matrices, we use the Evolutionary Optimization Algorithms (EOA) which directly target the ultimate estimation accuracy as the optimization objective function. We evaluate the performance of SNIPER using both synthetic and real network measurement traces from different network topologies and by considering two main applications for per-flow size and delay estimations. Our results show that SNIPER can be applied to a variety of network performance measurements under hard resource constraints. For example, by measuring only 8.8% of all per-flow path delays in Harvard network [1], congested paths can be detected with probability of 0.94. Finally, to demonstrate the feasibility of our framework, we also have implemented a prototype of SNIPER in Mininet.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1525271
• Journal Information: Computer Networks, Journal Name: Computer Networks Journal Issue: P1 Vol. 129; ISSN 1389-1286
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Matrix completion
Network inference
Passive and active network measurement
Software defined networking