Run-time adaptive workload estimation for dynamic voltage scaling
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Cooperative power-aware scheduling in grid computing environments
Journal of Parallel and Distributed Computing
Energy efficient scheduling for real-time systems with mixed workload
EUC'07 Proceedings of the 2007 international conference on Embedded and ubiquitous computing
A QoS Guaranteed Cache Design for Environment Friendly Computing
GREENCOM '11 Proceedings of the 2011 IEEE/ACM International Conference on Green Computing and Communications
Energy and transition-aware runtime task scheduling for multicore processors
Journal of Parallel and Distributed Computing
Power-aware fixed priority scheduling for sporadic tasks in hard real-time systems
Journal of Systems and Software
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This paper describes dynamic voltage scaling (DVS) algorithms for real-time systems with both periodic and aperiodic tasks. Although many DVS algorithms have been developed for real-time systems with periodic tasks, none of them can be used for a system with both periodic and aperiodic tasks because of the arbitrary temporal behaviors of aperiodic tasks. This paper proposes off-line and on-line DVS algorithms that are based on existing DVS algorithms. The proposed algorithms utilize the execution behaviors of scheduling servers for aperiodic tasks. Since there is a tradeoff between the energy consumption and the response time of aperiodic tasks, the proposed algorithms focus on bounding the response time degradation of aperiodic tasks although they delay the response time by stretching the task execution to get high energy savings in mixed task sets. Experimental results show that the proposed algorithms reduce the energy consumption by 48% and 35% over the non-DVS scheme under rate monotonic (RM) scheduling and earliest deadline first (EDF) scheduling, respectively.