Save Watts in Your Grid: Green Strategies for Energy-Aware Framework in Large Scale Distributed Systems

Orgerie, Anne‐Cécile; Lefèvre, Laurent; Gelas, Jean-Patrick

doi:10.1109/icpads.2008.97

Cited by 80 publications

(77 citation statements)

References 11 publications

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“…Our proposal differs from research works [6,[19][20][21][22][23] in two main aspects. We propose optimizing makespan and energy consumption at the level of the scheduler.…”

Section: Related Workmentioning

confidence: 43%

“…Chae et al [20] illustrate a method of determining the aggregate system load and the minimal set of computational resources that can process the workload. Orgerie et al in [21] present a three-step strategy based on a framework able to control the computing requirements by switching the unused nodes off, predicting their usage to switch on them again and finally aggregating some reservations to avoid frequent on/off cycles. Christobel et al in [6] proposed an energy-aware scheduling approach for scientific workflows based on Particle Swarm Optimization and Dynamic Voltage Scaling.…”

Section: Related Workmentioning

confidence: 99%

“…Two primary methods are commonly used: switching off underutilized resources [19][20][21] or using voltage and frequency scaling (VFS) techniques [6,[22][23][24]. Cocaña et al [19] presented a software tool that predicts the future node requirements using a machine-learning approach, and then stopping those that will not be required in the near future.…”

Section: Related Workmentioning

confidence: 99%

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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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“…Our proposal differs from research works [6,[19][20][21][22][23] in two main aspects. We propose optimizing makespan and energy consumption at the level of the scheduler.…”

Section: Related Workmentioning

confidence: 43%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

et al. 2016

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“…A prediction algorithm was proposed by Orgerie et al in [101] which aggregates the workload and switches off the un-utilized CPUs thereby reducing the processing power consumption in large Grids such as Grid5000 [101]. In [38] the authors pointed out that just focusing on processor power consumption is not always sufficient.…”

Section: Energy -Aware Resource Allocationmentioning

confidence: 99%

Survey on Grid Resource Allocation Mechanisms

et al. 2014

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Grid is a distributed high performance computing paradigm that offers various types of resources (like computing, storage, communication) to resource-intensive user tasks. These tasks are scheduled to allocate available Grid resources efficiently to achieve high system throughput and to satisfy A. Y. Zomaya University of Sydney, Sydney, Australia user requirements. The task scheduling problem has become more complex with the ever increasing size of Grid systems. Even though selecting an efficient resource allocation strategy for a particular task helps in obtaining a desired level of service, researchers still face difficulties in choosing a suitable technique from a plethora of existing methods in literature. In this paper, we explore and discuss existing resource allocation mechanisms for resource allocation problems employed in Grid systems. The work comprehensively surveys Gird resource allocation mechanisms for different architectures (centralized, distributed, static or dynamic). The paper also compares these resource allocation mechanisms based on their common features such as time complexity, searching mechanism, allocation strategy, optimality, operational environment and objective function they adopt for solving computing-and data-intensive applications. The comprehensive analysis of cutting-edge research in the Grid domain presented in this work provides readers with an understanding of essential concepts of resource allocation mechanisms in Grid systems and helps them identify important and outstanding issues for further investigation. It also helps readers to choose the most appropriate mechanism for a given system/application.

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“…A different form of virtualization is grid computing. An example of grid computed used as a way to save power is presented in [10].…”

Section: Related Workmentioning

confidence: 99%

Power efficiency of thin clients

Vereecken

Deboosere

Simoens

et al. 2010

Trans Emerging Tel Tech

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Abstract-Thin client computing trades local processing for network bandwidth consumption by offloading application logic to remote servers. User input and display updates are exchanged between client and server through a thin client protocol. This thin client protocol traffic can lead to a significantly higher power consumption of the radio interface of the wireless device. In this contribution, we present a cross-layer algorithm that exploits thin client protocol layer information to determine intervals where no traffic from the server is expected. During these intervals, the wireless network interface card (WNIC) is instructed to enter the energy conserving sleep mode. Using this algorithm for a remote text editor, WNIC energy consumption reductions of 21-52 % can be achieved.

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Save Watts in Your Grid: Green Strategies for Energy-Aware Framework in Large Scale Distributed Systems

Cited by 80 publications

References 11 publications

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

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

Survey on Grid Resource Allocation Mechanisms

Power efficiency of thin clients

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