A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints
Abstract
In many deployments, computer systems are underutilized -- meaning that applications have performance requirements that demand less than full system capacity. Ideally, we would take advantage of this under-utilization by allocating system resources so that the performance requirements are met and energy is minimized. This optimization problem is complicated by the fact that the performance and power consumption of various system configurations are often application -- or even input -- dependent. Thus, practically, minimizing energy for a performance constraint requires fast, accurate estimations of application-dependent performance and power tradeoffs. This paper investigates machine learning techniques that enable energy savings by learning Pareto-optimal power and performance tradeoffs. Specifically, we propose LEO, a probabilistic graphical model-based learning system that provides accurate online estimates of an application's power and performance as a function of system configuration. We compare LEO to (1) offline learning, (2) online learning, (3) a heuristic approach, and (4) the true optimal solution. We find that LEO produces the most accurate estimates and near optimal energy savings.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 14, 2015
- Source ID
- 10.1145/2786763.2694373
Entities
People
- Henry Hoffmann
- Huazhe Zhang
- John D. Lafferty
- Nikita Mishra
Organizations
- Defense Advanced Research Projects Agency
- Office of Naval Research
- United States Department of Energy
- University of Chicago