2014 Poster Sessions : Improving Resource-Efficiency in Cloud Computing

Student Name : Christina Delimitrou
Advisor : Christos Kozyrakis
Research Areas: Computer Systems
Abstract:
Cloud computing promises flexibility and high performance for users and lower costs for operators. However, datacenters today operate at very low utilization, wasting power and posing scalability limitations. There are several reasons behind datacenter underutilization, including exaggerated resource reservations, workload interference and platform heterogeneity.

In this work we present Quasar a cluster management system that is both resource-efficient and QoS-aware. Quasar moves away from the traditional reservation-based approach in cluster management to a more performance-centric approach. It also leverages efficient data mining techniques to determine the application preferences in terms of resources without resorting to exhaustive workload profiling. We have evaluated Quasar on a large EC2 cluster under various workload scenarios and have shown that it improves utilization by 47% while preserving per-application performance requirements.

Bio:
Christina Delimitrou is a Ph.D. student in the Electrical Engineering Department at Stanford University, where she works with Professor Christos Kozyrakis. She is mainly interested in computer architecture and computer systems, and specifically she works on QoS-aware techniques for scheduling and resource management in large-scale datacenters. In the past she has also worked on datacenter application and system modeling. Christina has earned an MS from Stanford University in December 2011. More information can be found in: http://www.stanford.edu/~cdel/