(1) Network Science, Inference and Cyber-Security


(2) Real-time and Large-scale Computing

  • image The increasing digitalization in many aspects of life leads to a proliferation of data inventory. At the same time, the emergence of software frameworks like MapReduce/Hadoop/Spark allows us to pursue large-scale problems in computer science. We are currently developing mathematical theories and software for several cyberspace intelligence analytics. In [1-2], we design real-time bidding algorithms for variable cloud computation services in Amazon Web Services. The challenge is: What is the optimal price to bid for high-value and low-cost cloud computation by intelligently matching buyers and sellers? We are currently studying several fundamental problems for large-scale computing in MapReduce and artificial intelligence (AI) applications.

    [1] L. Zheng, C. Joe-Wong, C. W. Tan, M. Chiang and X. Wang, How to Bid the Cloud?, ACM SIGCOMM 2015.
    [2] L. Zheng, C. Joe-Wong, C. Brinton, C. W. Tan, S. Ha and M. Chiang, On the Viability of a Cloud Virtual Service Provider, ACM SIGMETRICS 2016.

(3) Network Optimization by Perron-Frobenius Theory


(4) Information Theory

  • Information-theoretic problems often possess surprisingly beautiful structures associated with optimization theory that shed insights to designing optimal systems. In [2], we addressed the Gaussian interference channel rate optimization problem based on the discovery of an intriguing zig-zag phenomenon with the use of superposition coding. In [1], we discovered that linear programming and convex relaxation ideas can be used to automate the process of mathematically proving and disproving very large-scale information theory inequalities, which are traditionally difficult to prove by hand. We are currently working on the software automation (software forthcoming in Fall 2018) and using the software for analyzing some problems in mathematics.

    [1] S. W. Ho, C. W. Tan and R. W. Yeung, Proving and Disproving Information Inequalities, Proc. of IEEE Symp. of Information Theory, 2014.
    [2] Y. Zhao, C. W. Tan, A. S. Avestimehr, S. N. Diggavi and G. J. Pottie, On the Maximum Achievable Sum-rate with Successive Decoding in Interference Channels, IEEE Transactions on Information Theory, Vol. 58, No. 6, pp. 3798-3820, 2012.

(5) Wireless Communications Technologies


(6) Performance Evaluation: How good is our Internet Routing Protocol?

  • A fundamental question in Internet TCP/IP engineering is: “What is the cost of routing TCP flows over a single path or multiple routes?” The answer can guide the decision on whether to support multipath routing –an expensive operation– in the Internet. We show that optimal routing can in fact be achieved using a randomized TCP/IP algorithm to load-balance large proportion of Internet traffic with only a small group of users sparsely routing over more than one path [2]. Our work in [1, 2] lend theoretical evidence that the current single-path routing, e.g., the OSPF Internet protocol, can be improvised to scale up for multipath routing in next-generation software-defined networks.

    [1] M. Wang, C. W. Tan, W. Xu and A. Tang, Cost of Not Splitting in Routing: Characterization and Estimation, IEEE/ACM Transactions on Networking, Vol. 19, No. 6, pp. 1849-1859, 2011.
    [2] Y. Bi, C. W. Tan and A. Tang, Network Utility Maximization with Path Cardinality Constraints, IEEE INFOCOM, 2016.

(7) Power Network Optimization