Pareto-optimization-based run-time task scheduling for embedded systems
Proceedings of the 1st IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Power-aware QoS Management in Web Servers
RTSS '03 Proceedings of the 24th IEEE International Real-Time Systems Symposium
Dynamic Mapping in Energy Constrained Heterogeneous Computing Systems
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Feedback Scheduling of Power-Aware Soft Real-Time Tasks
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Dynamic Voltage Scaling in Multitier Web Servers with End-to-End Delay Control
IEEE Transactions on Computers
Fast multidimension multichoice knapsack heuristic for MP-SoC runtime management
ACM Transactions on Embedded Computing Systems (TECS)
High performance dynamic voltage/frequency scaling algorithm for real-time dynamic load management
Journal of Systems and Software
PAAS: Power Aware Algorithm for Scheduling in High Performance Computing
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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In this paper we propose a novel scheduling framework for a dynamic real-time environment with energy constraints. This framework dynamically adjusts the CPU voltage/frequency so that no task in the system misses its deadline and the total energy savings of the system are maximized. In this paper we consider only realistic, discrete-level speeds.Each task in the system consumes a certain amount of energy, which depends on a speed chosen for execution. The process of selecting speeds for execution while maximizing the energy savings of the system requires the exploration of a large number of combinations, which is too time consuming to be computed on-line. Thus, we propose an integrated heuristic methodology which executes an optimization procedure in a low computation time. This scheme allows the scheduler to handle power-aware real-time tasks with low cost while maximizing the use of the available resources and without jeopardizing the temporal constraints of the system. Simulation results show that our heuristic methodology is able to generate power-aware scheduling solutions with near-optimal performance.