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Weina Wang- Delay Bounds And Asymptotics In Cloud Computing Systems

Seminar: Distinguished Lecture Series | February 21 | 3:30-5 p.m. | 3110 Etcheverry Hall

Weina Wang, Illinois Urbana-Campaign

Industrial Engineering & Operations Research

With the emergence of big-data technologies, cloud computing systems are growing rapidly in size and becoming more and more complex, making it costly to conduct experiments and simulations. Therefore, modeling computing systems and characterizing their performance analytically are more critical than ever in identifying bottlenecks, informing system design, and facilitating provisioning. In this talk, I will illustrate how we study the delay performance in cloud computing systems from different modeling perspectives. First, I will focus on the delay of jobs that consist of multiple tasks, where the tasks can be processed in parallel on different servers, and a job is completed only when all its tasks are completed. Such jobs with parallel tasks are prevalent in today’s cloud computing systems. While the delay of individual tasks has been extensively studied, job delay has not been well-understood, even though job delay is the most important metric of interest to end users. In our work, we establish a stochastic upper bound on job delay using properties of associated random variables, and show its tightness in an asymptotic regime where the number of servers in the system and the number of tasks in a job both become large. After this, I will also briefly summarize our results on delay characterization for data-processing tasks where the locality of data needs to be considered, and for data transfer in large-scale datacenter networks.

Weina Wang is a joint postdoctoral research associate in the Coordinated Science Lab at the University of Illinois at Urbana-Champaign, and in the School of ECEE at Arizona State University. She received her B.E. from Tsinghua University and her Ph.D. from Arizona State University, both in Electrical Engineering. Her research lies in the broad area of applied probability and stochastic systems, with applications in cloud computing, data centers, and privacy-preserving data analytics. Her dissertation received the Dean’s Dissertation Award in the Ira A. Fulton Schools of Engineering at Arizona State University in 2016. She received the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS 2016.