Paper Info
Title
Online Energy-Efficient Task-Graph Scheduling for Multicore Platforms
Abstract
Numerous directed acyclic graph (DAG) schedulers have been developed to improve the energy efficiency of various multicore platforms. However, these schedulers make a priori assumptions about the relationship between the task dependencies, and they are unable to adapt online to the characteristics of each application without offline profiling data. Therefore, we propose a novel energy-efficient online scheduling solution for the general DAG model to address the two aforementioned problems. Our proposed scheduler is able to adapt at run-time to the characteristics of each application by making smart foresighted decisions, which take into account the impact of current scheduling decisions on the present and future deadline miss rates and energy efficiency. Moreover, our scheduler is able to efficiently handle execution with very limited resources by avoiding scheduling tasks that are expected to miss their deadlines and do not have an impact on future deadlines. We validate our approach against state-of-the-art solutions. In our first set of experiments, our results with the H.264 video decoder demonstrate that the proposed low-complexity solution for the general DAG model reduces the energy consumption by up to 15% compared to an existing sophisticated and complex scheduler that was specifically built for the H.264 video decoder application. In our second set of experiments, our results with different configurations of synthetic DAGs demonstrate that our proposed solution is able to reduce the energy consumption by up to 55% and the deadline miss rates by up to 99% compared to a second existing scheduling solution. Finally, we show that our DAG flow manager and scheduler have low complexities on a real mobile platform and we show that our solution is resilient to workload prediction errors by using different estimator accuracies.
Year
DOI
Venue
2014
10.1109/TCAD.2014.2316094
IEEE Trans. on CAD of Integrated Circuits and Systems
Keywords
DocType
Volume
mobile platform,processor scheduling,online energy-efficient task-graph scheduling,Adaptive,computerised instrumentation,DAG schedulers,online,task analysis,energy-efficient online scheduling,directed acyclic graph,H.264 video decoder application,data compression,low-power electronics,offline profiling data,workload prediction errors,video coding,multicore platforms,directed graphs,estimator accuracies,multimedia embedded systems,task dependencies,energy-efficient scheduler,video codecs
Journal
33
Issue
ISSN
Citations 
8
0278-0070
3
PageRank 
References 
Authors
0.39
16
4
Name
Order
Citations
PageRank
Karim Kanoun1172.27
Nicholas Mastronarde224026.93
David Atienza32219149.60
Mihaela Van Der Schaar43968352.59