Abstract

The problem of minimizing energy for a performance constraint (e.g., Real-time deadline or quality-of-service requirement) has been widely studied, both in theory and in practice. Theoretical models have indicated large potential energy savings, but practical concerns have made these savings hard to realize. Instead, practitioners often rely on heuristic solutions, which achieve good results in practice but tend to be system-specific in efficacy. An example is the race-to-idle heuristic, which makes all resources available until a task completes and then idles. Theory predicts poor energy savings, but practitioners have reported good empirical results. To help bridge the gap between theory and practice, this paper presents a geometrical framework for analyzing the energy optimality of resource allocation under performance constraints. The geometry of the problem allows us to derive an optimal strategy and three …

Date

2015

Authors

David HK Kim, Connor Imes, Henry Hoffmann

Conference

2015 IEEE 3rd international conference on cyber-physical systems, networks, and applications

Pages

78-85

Publisher

IEEE