SCS Faculty Candidate

  • Gates Hillman Centers
  • ASA Conference Room 6115
  • Postdoctoral Research Associate
  • Coordinated Science Lab, University of Illinois at Urbana-Champaign
  • School of Electrical, Computer and Energy Engineering, Arizona State Univesrity

Delay Asymptotics in Computing Systems

With the emergence of big-data technologies, 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 decision-making.  In this talk, I will illustrate how we use asymptotic analysis to characterize delay performance.  The primary focus is on the asymptotic regime where the number of servers in the system becomes large, which well fits the growing scale of today’s cloud computing systems and data centers.  We study the delay of jobs that consist of sub-tasks, where the sub-tasks are processed on different servers in parallel, and a job is completed only when all of its sub-tasks are completed.  Delay of jobs with sub-tasks has been widely studied in the so-called “fork-join” model, but exact analysis is known only for a two-server system.  Finding tight characterizations of job delay in a general system has been an open problem for more than 30 years.  In our work, we consider a variant of the fork-join model, called "limited fork-join," that is more suitable for modern computing systems.  Our results give the first tight characterization of fork-join job delay in large-scale computing systems.

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, working with Prof. R. Srikant and Prof. Lei Ying.  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.

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