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[06-15] Rethinking the cloud for next-generation applications


Title: Rethinking the cloud for next-generation applications


Speaker: Tian Guo (Assistant Professor, Worcester Polytechnic Institute)


Time: 10:00am, June 15th (Friday), 2018


Venue: Room 334, Building 5, Institute of Software, Chinese Academy of Sciences



Today’s cloud platforms are facing unprecedented diverse workloads from both millions of mobile users and billions of Internet-of-Things (IoT) devices. These workloads introduces new challenges and dynamics that differ from traditional workloads. However, current cloud platforms are agnostic to the type of end-devices and are not well suited to emerging application needs. My work argues that these trends require a rethinking of current cloud architectures and focuses on the challenges of handling the dynamics introduced by these next-generation applications.

In this talk, I will describe two aspects of cloud design: handling demand-side dynamics from emerging cloud workloads and handling supply-side dynamics from unpredictable cloud resources. Specifically, I will first describe approaches to optimize user-perceived performance for both global workloads that exhibit spatial variations and an exemplary workload of mobile deep inference. Next, I will introduce mechanisms to effectively support running applications on transient servers---servers with unpredictable availability. Finally, I will conclude my talk with future work in cloud research to handle emerging mobile and IoT applications.



Tian Guo is an Assistant Professor in the Computer Science Department
at Worcester Polytechnic Institute. Her research interests include distributed systems, cloud computing, mobile computing and cloud-enabled IoTs. Her current focus is on handling dynamics introduced by new cloud workloads and emerging cloud platforms. She received her Ph.D. and M.S. in Computer Science from the University of Massachusetts Amherst in 2013 and 2016, respectively, and her B.E. in Software Engineering from Nanjing University in 2010. Her work is supported in part by the NSF CRII grant CNS-1755659.