Title
Software Defined Network Inference With Passive/Active Evolutionary-Optimal Probing (Sniper)
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. This paper proposes a new framework, SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can leads 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. To demonstrate the feasibility of our framework, we also have implemented a prototype of SNIPER in Mininet.
Year
Venue
Field
2015
24TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS AND NETWORKS ICCCN 2015
Matrix completion,Matrix (mathematics),Inference,Computer science,Computer network,Network simulation,Real-time computing,Network topology,Software-defined networking,Network management,Distributed computing,Network performance
DocType
ISSN
Citations 
Conference
1095-2055
2
PageRank 
References 
Authors
0.37
18
5
Name
Order
Citations
PageRank
Mehdi Malboubi1295.90
Yanlei Gong291.27
Xiong Wang3157.31
Chen-Nee Chuah42006161.34
Puneet Sharma52341188.96