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Exploiting Dynamic Workload Variation in Low Energy Preemptive Task Scheduling
| Content Provider | CiteSeerX |
|---|---|
| Author | Leung, Lap-Fai Tsui, Chi-Ying |
| Abstract | A novel energy reduction strategy to maximally exploit the dynamic workload variation is proposed for the offline voltage scheduling of preemptive systems. The idea is to construct a fully-preemptive schedule that leads to minimum energy consumption when the tasks take on approximately the average execution cycles yet still guarantees no deadline violation during the worst-case scenario. End-time for each sub-instance of the tasks obtained from the schedule is used for the on-line dynamic voltage scaling (DVS) of the tasks. For the tasks that normally require a small number of cycles but occasionally a large number of cycles to complete, such a schedule provides more opportunities for slack utilization and hence results in larger energy saving. The concept is realized by formulating the problem as a Non-Linear Programming (NLP) optimization problem. Experimental results show that, by using the proposed scheme, the total energy consumption at runtime is reduced by as high as 60 % for randomly generated task sets when comparing with the static scheduling approach only using worst case workload. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Dynamic Workload Variation Low Energy Preemptive Task Scheduling Energy Consumption Offline Voltage Scheduling On-line Dynamic Voltage Scaling Fully-preemptive Schedule Preemptive System Case Workload Slack Utilization Deadline Violation Hence Result Worst-case Scenario Static Scheduling Approach Novel Energy Reduction Strategy Non-linear Programming Average Execution Cycle Total Energy Consumption Task Set |
| Content Type | Text |
