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Title: Software defined network inference with evolutionary optimal observation matrices

Abstract

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 inmore » Mininet.« less

Authors:
 [1];  [2];  [2];  [2];  [1];  [3]
  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
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1525271
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Computer Networks
Additional Journal Information:
Journal Volume: 129; Journal Issue: P1; Journal ID: ISSN 1389-1286
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Passive and active network measurement; Network inference; Matrix completion; Software defined networking

Citation Formats

Malboubi, Mehdi, Gong, Yanlei, Yang, Zijun, Wang, Xiong, Chuah, Chen-Nee, and Sharma, Puneet. Software defined network inference with evolutionary optimal observation matrices. United States: N. p., 2017. Web. doi:10.1016/j.comnet.2017.09.001.
Malboubi, Mehdi, Gong, Yanlei, Yang, Zijun, Wang, Xiong, Chuah, Chen-Nee, & Sharma, Puneet. Software defined network inference with evolutionary optimal observation matrices. United States. doi:10.1016/j.comnet.2017.09.001.
Malboubi, Mehdi, Gong, Yanlei, Yang, Zijun, Wang, Xiong, Chuah, Chen-Nee, and Sharma, Puneet. Mon . "Software defined network inference with evolutionary optimal observation matrices". United States. doi:10.1016/j.comnet.2017.09.001. https://www.osti.gov/servlets/purl/1525271.
@article{osti_1525271,
title = {Software defined network inference with evolutionary optimal observation matrices},
author = {Malboubi, Mehdi and Gong, Yanlei and Yang, Zijun and Wang, Xiong and Chuah, Chen-Nee and Sharma, Puneet},
abstractNote = {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.},
doi = {10.1016/j.comnet.2017.09.001},
journal = {Computer Networks},
number = P1,
volume = 129,
place = {United States},
year = {2017},
month = {9}
}

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