Networking for Big Data: Theory, Algorithms and Applications
In the era of big data, domain experts in various science and engineering fields are facing unprecedented challenges in global data distribution, processing, access and analysis, and in the coordinated use of limited computing, storage and network resources. To meet this challenge, data-centric network architectures have been proposed, which focus on enabling end users to obtain the data they want, rather than to communicate with specific nodes.
In this talk, we present new frameworks for the optimization of key functionalities supported by data-centric networking, which are broadly applicable to content delivery networks, peer-to-peer networks and edge computing networks. The frameworks enable the joint optimization of (in-network) caching and traffic engineering for content distribution, as well as joint computation scheduling, caching and request forwarding for distributed computing. We discuss two classes of distributed and adaptive algorithms for the joint optimizations, one based on throughput optimal control and another based on convex relaxation and stochastic gradient ascent. We analytically provide optimality guarantees for the algorithms in terms of relevant performance metrics, and show using numerical evaluations that the algorithms significantly outperform baseline policies over a broad array of network settings.
Finally, we discuss an ongoing project which implements the optimization frameworks and algorithms to accelerate data distribution and computation in the Large Hadron Collider (LHC) high-energy physics network, one of the largest data applications in the world.
Edmund Yeh received his B.S. in Electrical Engineering with Distinction and Phi Beta Kappa from Stanford University in 1994. He then studied at Cambridge University on the Winston Churchill Scholarship, obtaining his M.Phil in Engineering in 1995. He received his Ph.D. in Electrical Engineering and Computer Science from MIT under Professor Robert Gallager in 2001. He is currently Professor of Electrical and Computer Engineering at Northeastern University. He was previously Assistant and Associate Professor of Electrical Engineering, Computer Science, and Statistics at Yale University. He has held visiting positions at MIT, Stanford, Princeton, UC Berkeley, NYU, EPFL, and TU Munich.
Professor Yeh was one of the PIs on the original NSF-funded FIA Named Data Networking project. He will serve as General Chair for ACM SIGMETRICS 2020 and TPC Co-Chair for ACM MobiHoc 2021. He is the recipient of the Alexander von Humboldt Research Fellowship, the Army Research Office Young Investigator Award, the Winston Churchill Scholarship, the National Science Foundation and Office of Naval Research Graduate Fellowships, the Barry M. Goldwater Scholarship, the Frederick Emmons Terman Engineering Scholastic Award, and the President’s Award for Academic Excellence (Stanford University). Professor Yeh has received three Best Paper Awards, including awards at the 2017 ACM Conference on Information-Centric Networking (ICN), and at the 2015 IEEE International Conference on Communications (ICC) Communication Theory Symposium.
Hosted by Professor Lixia Zhang
Date(s) - Nov 14, 2019
2:00 pm - 3:00 pm
Engineering VI – Room 289
404 Westwood Plaza, Los Angeles California 90095