Please use this identifier to cite or link to this item: https://doi.org/10.1109/TPDS.2008.55
Title: Design of fast and efficient energy-aware gradient-based scheduling algorithms heterogeneous embedded multiprocessor systems
Authors: Goh, L.K.
Veeravalli, B. 
Viswanathan, S.
Keywords: Embedded systems
Energy-aware scheduling
Heterogeneous multiprocessor scheduling
Power management
Real-time scheduling
Issue Date: 2009
Citation: Goh, L.K., Veeravalli, B., Viswanathan, S. (2009). Design of fast and efficient energy-aware gradient-based scheduling algorithms heterogeneous embedded multiprocessor systems. IEEE Transactions on Parallel and Distributed Systems 20 (1) : 1-12. ScholarBank@NUS Repository. https://doi.org/10.1109/TPDS.2008.55
Abstract: In this paper, we present two heuristic energy-aware scheduling algorithms (EGMS and EGMSIV) for scheduling task precedence graphs in an embedded multiprocessor system having processing elements with dynamic voltage scaling capabilities. Unlike most energy-aware scheduling algorithms that consider task ordering and voltage scaling separately from task mapping, our algorithms consider them in an integrated way. EGMS uses the concept of energy gradient to select tasks to be mapped onto new processors and voltage levels. EGM-SIV extends EGMS by introducing intra-task voltage scaling using a Linear Programming (LP) formulation to further reduce the energy consumption. Through rigorous simulations, we compare the performance of our proposed algorithms with a few approaches presented in the literature. The results demonstrate that our algorithms are capable of obtaining energy-efficient schedules using less optimization time. On the average, our algorithms produce schedules which consume 10 percent less energy with more than 47percent reduction in optimization time when compared to a few approaches presented in the literature. In particular, our algorithms perform better in generating energy-efficient schedules for larger task graphs. Our results show a reduction of up to 57 percent in energy consumption for larger task graphs compared to other approaches. © 2009 IEEE.
Source Title: IEEE Transactions on Parallel and Distributed Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/82132
ISSN: 10459219
DOI: 10.1109/TPDS.2008.55
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