Energy-aware partitioning for multiprocessor real-time systems

Aydın, Hakan; Yang, Qi

doi:10.1109/ipdps.2003.1213225

Cited by 237 publications

(226 citation statements)

References 16 publications

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“…Aydin and Yang [5] compared the behavior of four wellknown heuristics (First-Fit, Next-Fit, Best-Fit, Worst-Fit) for homogeneous multicore systems and periodic independent tasks. Their work stated that Worst-Fit Decreasing (WFD), which aims at balancing the workload among the cores, is the most effective for reducing the energy consumption while considering cubic power functions.…”

Section: A Related Workmentioning

confidence: 99%

On the energy-aware partitioning of real-time tasks on homogeneous multi-processor systems

Bambagini

Lelli

Buttazzo

et al. 2013

2013 4th Annual International Conference on Energy Aware Computing Systems and Applications (ICEAC)

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Abstract-In high-performance computing systems, efficient energy management is a key feature for keeping energy bills low and avoiding thermal dissipation problems, as well as for controlling the application performance. This paper considers the problem of partitioning and scheduling a set of real-time tasks on a realistic hardware platform consisting of a number of homogeneous processors. Several well-known heuristics are compared to identify the approach that better reduces the overall energy consumption of the entire system. Despite the actual state of art, the approach which minimizes the number of active cores is the most energy efficient.

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Section: A Related Workmentioning

confidence: 99%

On the energy-aware partitioning of real-time tasks on homogeneous multi-processor systems

Bambagini

Lelli

Buttazzo

et al. 2013

2013 4th Annual International Conference on Energy Aware Computing Systems and Applications (ICEAC)

View full text Add to dashboard Cite

show abstract

“…Acting in the partitioner, recent works have addressed the energy-aware task allocation problem [19,2,1]. For instance, Wei et al [19] reduce energy consumption by exploiting parallelism of multimedia tasks on a multicore platform combining DVS with switching-off cores.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, Wei et al [19] reduce energy consumption by exploiting parallelism of multimedia tasks on a multicore platform combining DVS with switching-off cores. Aydin et al [2] present a new algorithm that reserves a subset of processors for the execution of tasks with utilization not exceeding a threshold. Unlike our work, none of these techniques use task migration among cores.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Power-Aware Partitioner with Task Migration for Multicore Embedded Systems

March

Sahuquillo

Petit

et al. 2011

Euro-Par 2011 Parallel Processing

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Abstract. Nowadays, a key design issue in embedded systems is how to reduce the power consumption, since batteries have a limited energy budget. For this purpose, several techniques such as Dynamic Voltage Scaling (DVS) or task migration can be used. DVS allows reducing power by selecting the optimal voltage supply, while task migration achieves this effect by balancing the workload among cores. This paper first analyzes the impact on energy due to task migration in multicore embedded systems with DVS capability and using the well-known Worst Fit (WF) partitioning heuristic. To reduce overhead, migrations are only performed at the time that a task arrives to and/or leaves the system and, in such a case, only one migration is allowed. The huge potential on energy saving due to task migration, leads us to propose a new dynamic partitioner, namely DP, that migrates tasks in a more efficient way than typical partitioners. Unlike WF, the proposed algorithm examines which is the optimal target core before allowing a migration. Experimental results show that DP can improve energy consumption in a factor up to 2.74 over the typical WF algorithm.

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“…In other words, this amounts to minimizing the total dissipated power, which is proportional to the sum of the cubes of the processor speeds (a model commonly used, e.g. in [6], [7], [8], [9]). …”

Section: Propositionmentioning

confidence: 99%

On the Performance of Greedy Algorithms for Power Consumption Minimization

Benoît

Renaud-Goud

Robert

2011

2011 International Conference on Parallel Processing

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Abstract-We revisit the well-known greedy algorithm for scheduling independent jobs on parallel processors, with the objective of minimizing the power consumption. We assess the performance of the online version, as well as the performance of the offline version, which sorts the jobs by non-increasing size before execution. We derive new approximation factors, as well as examples that show that these factors cannot be improved, thereby completely characterizing the performance of the algorithms.

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Energy-aware partitioning for multiprocessor real-time systems

Abstract: Abstract

Cited by 237 publications

References 16 publications

On the energy-aware partitioning of real-time tasks on homogeneous multi-processor systems

On the energy-aware partitioning of real-time tasks on homogeneous multi-processor systems

A Dynamic Power-Aware Partitioner with Task Migration for Multicore Embedded Systems

On the Performance of Greedy Algorithms for Power Consumption Minimization

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