Network traffic measurement provides the basis for many applications, ranging from network management to accounting and security; yet the current state-of-the-art in network metrology is inadequate, providing surprisingly little visibility into detailed network behaviors and often requiring significant manual intervention to operate. Such practice becomes increasingly ineffective as the networks grow both in size and complexity. To make matters worse, even when the fine-grained measurement is available, analyzing vast amounts of raw data poses a major computational challenge. Useful features are often buried in noisy data and meaningful analysis often requires reducing the feature space to make the analysis more computationally feasible. Furthermore, processing delays make it difficult to react to possible security attacks and network management issues in a timely fashion.
We seek capable candidates who are interested in joining our ongoing effort on developing Machine-Learning based Autonomous Network Metrology, which supports multi-scale, pervasive, and dynamically tunable network measurement and analytics in Software Defined Networks (SDN). As an Associate, you can contribute both systematically (by participating in the system development) and analytically (by developing Machine Learning based algorithms). You are encouraged to contact us directly for more information regarding this opportunity.
Z. Zha, A. Wang, Y. Guo, D. Montgomery, S. Chen: "Instrumenting Open vSwitch with Monitoring Capabilities: Design and Challenges", ACM SOSR 2018.
A. Wang, Y. Guo, S. Chen, F. Hao, T. Lakshman, D. Montgomery, K. Sriram: "vPROM: vSwitch Enhanced Programmable Measurement in SDN", IEEE ICNP 2017.
Guo Y, Stolyar AL, Walid A: “Shadow-Routing Based Dynamic Algorithms for Virtual Machine Placement in a Networked Cloud". IEEE Transactions on Cloud Computing, 2015
Adhikari VK, Guo Y, et al: “Measurement Study of Netflix, Hulu, and a Tale of Three CDNs". IEEE/ACM Transaction on Networking (ToN) 23(6): 1984-1997, 2015
Yang X, Guo Y, Liu Y: “Bayesian-inference Based Recommendation in Online Social Networks”. IEEE Transactions on Parallel and Distributed Systems 24(4): 642-651, 2013
Network measurement; Software Defined Networking (SDN); Machine learning; Network programming; System development; SDN run-time system; Algorithm design;