A Heuristic Energy-aware Scheduling Algorithm for Heterogeneous Clusters

Liu, Yu; Liu, Yi; Qian, Depei

doi:10.1109/icpads.2009.33

Cited by 34 publications

(19 citation statements)

References 14 publications

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“…However, the authors did not consider much about the effect of scheduling algorithms. The authors in [5] introduced an online dynamic power management strategy with multiple power-saving states. Then, they proposed an energy-aware scheduling algorithm to reduce energy consumption in heterogeneous computing systems.…”

Section: Related Workmentioning

confidence: 99%

“…For each task the information on its processing time and the voltage rate of the processor to execute one unit of time is sufficient to measure the energy consumption for that task. In this context, we additionally promote the heterogeneity capability of the computing system to efficiently use the energy of the system [3][4][5]. The main idea is to match each task with the best resource to execute it, that is, the resource that optimizes the completion time of the task and executes it fastest with minimum energy.…”

Section: Introductionmentioning

confidence: 99%

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Energy-aware fast scheduling heuristics in heterogeneous computing systems

Díaz

Guzek

Pecero

et al. 2011

2011 International Conference on High Performance Computing &Amp; Simulation

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In heterogeneous computing systems it is crucial to schedule tasks in a manner that exploits the heterogeneity of the resources and applications to optimize systems performance. Moreover, the energy efficiency in these systems is of a great interest due to different concerns such as operational costs and environmental issues associated to carbon emissions. In this paper, we present a series of original low complexity energy efficient algorithms for scheduling. The main idea is to map a task to the machine that executes it fastest while the energy consumption is minimum. On the practical side, the set of experimental results showed that the proposed heuristics perform as efficiently as related approaches, demonstrating their applicability for the considered problem and its good scalability.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-aware fast scheduling heuristics in heterogeneous computing systems

Díaz

Guzek

Pecero

et al. 2011

2011 International Conference on High Performance Computing &Amp; Simulation

View full text Add to dashboard Cite

show abstract

“…There have been numerous works in the area of power and energy optimization for such systems [3], [4], [5], [6], [7], [8], [9], [10], [11]. Another dimension of this work is energy conservation, there has been a lot of work of a reason on the afore mentioned topic, which are covered by surveys such as [8], [9], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

“…Another dimension of this work is energy conservation, there has been a lot of work of a reason on the afore mentioned topic, which are covered by surveys such as [8], [9], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Scalable and Energy-Efficient Scheduling Techniques for Large-Scale Systems

Díaz

Guzek

Pecero

et al. 2011

2011 IEEE 11th International Conference on Computer and Information Technology

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Abstract-The scalability of a computing system can be identified by at least three components: (a) size, (b) geographical distribution, and (c) administrative constraints. Newer paradigms, such as clouds, grids, and clusters bring in more parameters to the aforementioned list, namely heterogeneity, energy consumption, and transparency. To optimize the performance of a computing system, it is manner that exploits heterogeneity and is scalable. Moreover, newer systems also demand energy efficiency as an integral part of schedulers. In this paper, we evaluate the behavior of low complexity energyefficient algorithms for scheduling. The set of experimental results showed that the evaluated heuristics perform as efficiently as related approaches; demonstrating their applicability and scalability for the considered problem.

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“…-The CBS heuristic was proposed by Jones et al [29] and tries to allocate as much as possible the tasks of the first job in the queue to a single cluster in an attempt to avoid inter-cluster link saturation. -Min-Min is a heuristic presented by Li et al on [30] that is able to consider a set of jobs with the aim of minimizing energy consumption. -JPR is a variant of the Naik et al heuristic [10], where the jobs are matched with the best available resources depending on just only one of the criteria selected, makespan or energy.…”

Section: Moga Performance Comparisonmentioning

confidence: 99%

Blacklist muti-objective genetic algorithm for energy saving in heterogeneous environments

et al. 2016

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Reducing energy consumption in large-scale computing facilities has become a major concern in recent years. Most of the techniques have focused on determining the computing requirements based on load predictions and thus turning unnecessary nodes on and off. Nevertheless, once the available resources have been configured, new opportunities arise for reducing energy consumption by providing optimal matching of parallel applications to the available computing nodes. Current research in scheduling has concentrated on not only optimizing the energy consumed by the processors but also optimizing the makespan, i.e., job completion time. The large number of heterogeneous computing nodes and variability of application-tasks are factors that make the scheduling an NP-Hard problem. Our aim in this paper is a multi-objective genetic algorithm based on a weighted blacklist able to generate scheduling decisions that globally optimizes the energy consumption and the makespan.

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A Heuristic Energy-aware Scheduling Algorithm for Heterogeneous Clusters

Cited by 34 publications

References 14 publications

Energy-aware fast scheduling heuristics in heterogeneous computing systems

Energy-aware fast scheduling heuristics in heterogeneous computing systems

Scalable and Energy-Efficient Scheduling Techniques for Large-Scale Systems

Blacklist muti-objective genetic algorithm for energy saving in heterogeneous environments

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