Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS

Wang, Lizhe; Laszewski, Gregor von; Dayal, Jay; Wang, Fugang

doi:10.1109/ccgrid.2010.19

Cited by 184 publications

(109 citation statements)

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“…In [13] the authors present a scheduling heuristic for reducing power consumption of precedence-constrained parallel tasks in a cluster with DVFS, the model is proposed for applications that have slack time for non-critical jobs while they scale down supply voltage for reducing energy consumption and extend execution time of jobs. The model considers homogeneous nodes as processing elements (PEs) with the same processing speed and uses green SLA negotiation in order to accept a tolerable performance loss.…”

Section: Related Workmentioning

confidence: 99%

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

Juarez

Ejarque

Badía

2018

Future Generation Computer Systems

176

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h i g h l i g h t s• Energy-aware run-time scheduler for task-based applications.• Model for estimating the application Energy consumption.• Methodology to automatically generate the required power consumption profile.• Multi-heuristic resource allocation algorithm to get solutions in polynomial time.• Energy saving/performance trade-off evaluation for different scenarios. a r t i c l e i n f o b s t r a c tGreen Computing is a recent trend in computer science, which tries to reduce the energy consumption and carbon footprint produced by computers on distributed platforms such as clusters, grids, and clouds. Traditional scheduling solutions attempt to minimize processing times without taking into account the energetic cost. One of the methods for reducing energy consumption is providing scheduling policies in order to allocate tasks on specific resources that impact over the processing times and energy consumption. In this paper, we propose a real-time dynamic scheduling system to execute efficiently taskbased applications on distributed computing platforms in order to minimize the energy consumption. Scheduling tasks on multiprocessors is a well known NP-hard problem and optimal solution of these problems is not feasible, we present a polynomial-time algorithm that combines a set of heuristic rules and a resource allocation technique in order to get good solutions on an affordable time scale. The proposed algorithm minimizes a multi-objective function which combines the energy-consumption and execution time according to the energy-performance importance factor provided by the resource provider or user, also taking into account sequence-dependent setup times between tasks, setup times and down times for virtual machines (VM) and energy profiles for different architectures. A prototype implementation of the scheduler has been tested with different kinds of DAG generated at random as well as on real task-based COMPSs applications. We have tested the system with different size instances and importance factors, and we have evaluated which combination provides a better solution and energy savings. Moreover, we have also evaluated the introduced overhead by measuring the time for getting the scheduling solutions for a different number of tasks, kinds of DAG, and resources, concluding that our method is suitable for run-time scheduling.

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Section: Related Workmentioning

confidence: 99%

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

Juarez

Ejarque

Badía

2018

Future Generation Computer Systems

176

View full text Add to dashboard Cite

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“…However, energy consumption due to idle time is not considered in the model. Wang et al [4] proposed a best-effort scheduling algorithm to optimize energy. DVS is applied to no-critical tasks.…”

Section: Related Workmentioning

confidence: 99%

“…In the independent mode, which is considered as a twophases optimization technique, a best-effort scheduling algorithm is combined with a slack reclamation technique [4]- [6]. The slack reclamation algorithm is used as a second pass to minimize the energy consumption of tasks in a schedule generated by the best-effort scheduling algorithm.…”

Section: Introductionmentioning

confidence: 99%

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On the energy optimization for precedence constrained applications using local search algorithms

Pecero

Huacuja

Bouvry

et al. 2012

2012 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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Abstract-We investigate the problem of scheduling precedence constrained applications on a distributed heterogeneous computing system with the aim of minimizing schedule length and reducing energy consumption. We present a scheduling algorithm based on the best-effort idea that promotes local search algorithms and dynamic voltage scaling to reduce energy consumption. The final goal is to maintain a given performance while minimizing energy use. The proposed approach first uses a list-based scheduling algorithm to find near-optimal solutions for schedule length, then local search algorithms with dynamic voltage scaling are applied to reduce energy consumption. However the algorithm it's not allowed to deteriorate the schedule length computed by the best-effort algorithm. We discuss simulation results obtained with sets of real-world applications that emphasize the interest of the approach.

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“…In [14] authors developed a particular kind of Service Level Agreement (SLA) called Green SLA. Green SLA uses best effort scheduling, which minimizes task execution time and energy-performance trade off.…”

Section: Introductionmentioning

confidence: 99%

A power efficient genetic algorithm for resource allocation in cloud computing data centers

Portaluri

Giordano

Kliazovich

et al. 2014

2014 IEEE 3rd International Conference on Cloud Networking (CloudNet)

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Abstract-One of the main challenges in cloud computing is to increase the availability of computational resources, while minimizing system power consumption and operational expenses. This article introduces a power efficient resource allocation algorithm for tasks in cloud computing data centers. The developed approach is based on genetic algorithms which ensure performance and scalability to millions of tasks. Resource allocation is performed taking into account computational and networking requirements of tasks and optimizes task completion time and data center power consumption. The evaluation results, obtained using a dedicated open source genetic multi-objective framework called jMetal show that the developed approach is able to perform the static allocation of a large number of independent tasks on homogeneous single-core servers within the same data center with a quadratic time complexity.

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Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS

Cited by 184 publications

References 39 publications

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

On the energy optimization for precedence constrained applications using local search algorithms

A power efficient genetic algorithm for resource allocation in cloud computing data centers

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