Abstract: Today’s era of cloud computing and big data is powered by massive data centers. The focus of my research is on resource allocation problems that arise in the design and operation of these large-scale data centers. Analyzing these systems exactly is usually intractable, and a usual approach is to study them in various asymptotic regimes with heavy traffic being a popular one. In this talk I will illustrate two methods for heavy-traffic analysis that we developed in my research. First, I will present the drift method to study scheduling in data center networks. I will present this result in the context of the so-called generalized switch, which subsumes several resource allocation problems in data centers. Second, I will present the use of novel transform techniques to characterize the tail behavior of delay for efficient load balancing of jobs on servers. The use of these techniques in non-heavy-traffic regimes will also be presented.

Bio: Daniela Hurtado-Lange is a Ph.D. candidate in Operations Research at the Georgia Institute of Technology (Georgia Tech), and her advisor is Professor Siva Theja Maguluri. She got her Master’s of Science and Bachelor’s in Industrial Engineering at the Pontifical Catholic University of Chile (PUC). She has been awarded with the Tennenbaum fellowship and Becas Chile, and her research was recognized with the second prize on the INFORMS JFIG 2020 paper competition. Her research interests are broadly on Applied Probability, specifically on performance analysis of a large class of stochastic processing networks.