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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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TITLE: Resource Allocation Problems in Cloud Computing
ABSTRACT:
Cloud computing services are growing at an exponential rate and with it the cost of providing these services. For cost effectiveness, providers need to rely on multi-tenancy and resource sharing among tenants, since statically reserving resources for a tenant is prohibitively expensive. A major consequence of resource sharing is that the performance of one tenant can be adversely affected by resource demands of other co-located tenants. One such resource that is essential for good performance of a tenant’s workload is memory. I will talk about the problem of effectively sharing memory in multi-tenant settings.
Service level agreement (SLA) gives a framework that defines and enforces accountability of the service provider to the tenant even when memory is not statically reserved on behalf of the tenant. We model the memory allocation problem as an online convex optimization problem that incorporates a rich variety of SLAs representing the diversity of clients’ requirements for resources as well as quality of service. We then design a primal-dual algorithm that builds on classical caching algorithms but can work under multi-tenant scenarios involving SLAs and overbooking. We use the framework of competitive analysis to analyze the performance of the algorithm. We will also describe results based on extensive experiments that demonstrate the effectiveness of our solution in practice.
Bio: Mohit Singh is a researcher in the theory group at Microsoft Research, Redmond. His research interests include discrete optimization, approximation algorithms and convex optimization. Previously, he was an Assistant Professor at McGill University from 2010-2011 and a post-doctoral researcher at Microsoft Research, New England from 2008-2009. He obtained his Ph.D. from Tepper School of Business, Carnegie Mellon University and his doctoral thesis received the Tucker prize in 2009 given by the Mathematical Optimization Society. He has also received the best paper award for his work on the traveling salesman problem at the Annual Symposium on Foundations of Computer Science (FOCS) 2011.
