Virtualization Management and Data Center Monitoring for Energy-efficient Clouds

Energy and thermal management in the cloud has become a primary concern as the global footprint of large-scale compute systems reaches significant levels, and continues to increase with impressive pace, following the ever-increasing compute demand and data explosion. Various new control knobs across systems, middleware, virtualization and facilities levels promote many interesting optimization opportunities for improving energy efficiency. However, these are coupled with significant management and monitoring challenges for effectively exploiting the present opportunities.

In this talk we first look at some of the potential opportunities and emerging challenges for improving the energy-efficiency in clouds, and highlight our research at virtualization, distributed systems and data center levels targeting these opportunities and challenges. We describe an effective distributed resource management approach based on a novel, black-box virtual machine (VM) demand estimation technique, which serves as a key enabler for improving the energy efficiency of virtualized systems via consolidation-driven, dynamic VM placement. We demonstrate that this demand estimation technique is highly-general across virtualization technologies and can very accurately track actual VM demands even under extreme consolidation and resource constraints. With multiple real-system prototypes, we present the dramatic improvements in the performance and agility of energy-aware virtualization management when guided by demand estimation. Next, we briefly look at the increasing cost and complexity of monitoring in the cloud and describe a new approach based on mobile, autonomous robots for reliable, fast and cost-effective monitoring and management in data centers. We demonstrate the operation principles of actual robot prototypes that are deployed in various live data centers, and discuss their applications to predictive and diagnostic analytics for energy, thermal and asset management.

Bio:
Canturk Isci is a Research Staff Member in IBM TJ Watson Research Center. His research interests include virtualization, data center energy and thermal management, microarchitectural and system-level techniques for energy-efficient and adaptive computing. Prior to joining IBM Research, Canturk was a Senior Member of Technical Staff at VMware, where we he worked on distributed resource and power management, performance and scalability of virtualized systems. He is the recipient of a best paper award in ICAC 2011, best research poster in VMworld 2008 and academic fellowships from British Council, Princeton and Bilkent University. He serves as the industry chair in IEEE Computer Society, Special Technical Community on Sustainable Computing. Canturk has a B.S. in Electrical Engineering from Bilkent University, an M.Sc. with Distinction in VLSI System Design from University of Westminster, and a Ph.D. in Electrical Engineering from Princeton University.