Abstract
In this talk I give an overview of the algorithms we have developed at UCSD to significantly lower the energy consumption in computing systems. We derived optimal power management strategies for stationary workloads that have been implemented both in HW and SW. Run-time adaptation can be done via an online learning algorithm that selects among a set of policies. We generalize the algorithm to include thermal management since we found that minimizing the power consumption does not necessarily reduce the overall energy costs. To reduce the performance costs typically associated with state of the art thermal management techniques, we developed a new set of proactive management policies. The experimental results using real datacenter workloads on an actual multicore system show that our proactive technique is able to dramatically reduce the adverse effects of temperature by over 60%. Most recently we have shown that symbiotic scheduling of workloads in virtualized environments can lead to average 15% energy savings with 20% performance benefit in high utilization scenarios.
I will also present some of the recent work we had done to address the energy savings in battery powered and energy harvesting systems. We are designing a new kind of “citizen infrastructure”, CitiSense, as an end-to-end health and environmental information system with near real-time data streams and feedback loops from the system to the sensing, processing, and actuation infrastructure. We have developed adaptive algorithms to tradeoff accuracy of computation versus the available energy for such systems, while taking into account the energy harvesting capabilities.
Biography
Tajana Simunic Rosing is currently an Assistant Professor in Computer Science Department at UCSD. Her research interests are energy efficient computing, embedded and wireless systems. Tajana’s work on event driven dynamic power management laid the mathematical foundations for the engineering problem, devised a globally optimal solution and more importantly defined the framework for future researchers to approach these kinds of problems in embedded system design. Her recent results demonstrate the importance of joint power and thermal management in multicore server systems in order to minimize the overall energy cost. Furthermore, she developed a novel class of proactive thermal management policies that can lower the incidence of hot spots in multicore processors by up to 60% with no performance impact. Her current work is focused on developing energy efficient scheduling policies for virtualized server environments and on energy efficiency in population area healthcare networks.
From 1998 until 2005 she was a full time research scientist at HP Labs while also leading research efforts at Stanford University. She finished her PhD in EE in 2001 at Stanford, concurrently with finishing her Masters in Engineering Management. Her PhD topic was dynamic management of power consumption. Prior to pursuing the PhD, she worked as a senior design engineer at Altera Corporation. She obtained the MS in EE from University of Arizona. Her MS thesis topic was high-speed interconnect and driver-receiver circuit design. She has served at a number of Technical Paper Committees, and is currently an Associate Editor of IEEE Transactions on Mobile Computing. In the past she has been an Associate Editor of IEEE Transactions on Circuits and Systems.