Demystifying energy consumption in Grids and Clouds

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

doi:10.1109/greencomp.2010.5598295

Cited by 48 publications

(35 citation statements)

References 26 publications

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“…This mode of operation causes low utilization of the cluster resources, and consequently consumes a high amount of power. Research has pointed out that using techniques of load-screw scheduling and dynamically adjusting idle servers to power-save mode can significantly reduce the energy consumption [11], [1], [2].…”

Section: Related Workmentioning

confidence: 99%

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Energy-aware hierarchical scheduling of applications in large scale data centers

Wen

Hong

et al. 2011

2011 International Conference on Cloud and Service Computing

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Abstract-With the rapid advance of cloud computing, large scale data center plays a key role in cloud computing. Energy consumption of such distributed systems has become a prominent problem and received much attention. Among existing energy-saving methods, application scheduling can reduce energy consumption by replacing and consolidating applications to decrease the number of running servers. However, most application scheduling approaches did not consider the energy cost on network devices, which is also a big portion of power consumption in large data centers.In this paper we propose a Hierarchical Scheduling Algorithm for applications, namely HSA, to minimize the energy consumption of both servers and network devices. In HSA, a Dynamic Maximum Node Sorting (DMNS) method is developed to optimize the application placement on servers connected to a common switch. Hierarchical crossing-switch adjustment is applied to further reduce the number of running servers. As a result, both the number of running servers and the amount of data transfer can be greatly reduced. The time complexity of HSA is Θ( * ( )), where n is the total number of the severs in the data center. Its stability is verified via simulations. Experiments show that the performance of HSA outperforms existing algorithms.

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Section: Related Workmentioning

confidence: 99%

“…By consolidating application on as less servers as possible and making idle servers sleep or power-off, the energy consumption can be significantly reduced [1], [2], [3], [4]. However, few of existing application scheduling approach has considered optimizing the power consumption of the network infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Energy-aware hierarchical scheduling of applications in large scale data centers

Wen

Hong

et al. 2011

2011 International Conference on Cloud and Service Computing

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“…Reaching the balance between performance, power and energy consumption has always been a difficult problem, as coding and compiling for performance do not always mean coding and compiling for power and energy [3]. When a program is executed on a computing device, it consumes energy based on how it uses the computing device's resources [4]- [7]. Each instruction inside the program contributes to the resources usage and to the total energy being consumed.…”

Section: Introductionmentioning

confidence: 99%

Performance vs. Power and Energy Consumption

Hassan¹,

Moussa²,

Farag³

2017

ijacsa

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Abstract-Reaching a balance between performance and energy consumption has always been a difficult objective to achieve for energy and power-aware applications. The work presented in this paper investigates the impact of using different coding styles to achieve a balance between performance and energy efficiency. The research also studies how different compilers may affect not only the performance of the code but also the energy consumption. The research demonstrates and concludes the process of choosing the right combination of the coding style and compiler, the combination which works best with the nature of the application and the target hardware, is necessary if the balance between performance and energy is a software design goal. The study addresses some experimental aspects of the impact of coding style and choice of the compiler on energy and performance efficiency. It also shows how different coding practices for the same problem could produce different performance and energy consumption rates.

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“…A great advantage of Graphical Processor Unit (GPU) accelerators over multicore platforms was demonstrated in terms of energy efficiency [2] with an overall energy consumption for an order of magnitude smaller for the GPUs. Inside the clusters, the many-core architectures, such as GPUs [3], as well as even more specialized data-flow computers [4] are getting an increasingly large supporting role but the main processing is still performed by the multi-core main processors because of simple programming model.In this research, we analyze the energy consumption profile of the processors on a two processor multi-core computer, while varying its workload.With the increasing number of components in large-scale computing systems the energy consumption is steeply increasing [5]. In computer systems, energy (measured in Joules) is represented as the electricity resource that can power the hardware components to do computation.…”

mentioning

confidence: 99%

“…With the increasing number of components in large-scale computing systems the energy consumption is steeply increasing [5]. In computer systems, energy (measured in Joules) is represented as the electricity resource that can power the hardware components to do computation.…”

mentioning

confidence: 99%

Energy Efficiency of Parallel Multicore Programs

Davidović¹,

Depolli²,

Lipić³

et al. 2016

SCPE

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Abstract. The increasing energy consumption of large-scale high performance resources raises technical and economical concerns. A reduction of consumed energy in multicore systems is possible to some extent with an optimized usage of computing and memory resources that is tailored to specific HPC applications. The essential step towards more sustainable consumption of energy is its reliable measurements for each component of the system and selection of optimally configured resources for specific applications. This paper briefly surveys the current approaches for measuring and profiling power consumption in large scale systems. Then, a practical case study of a real-time power measurement of multicore computing system is presented on two real HPC applications: maximum clique algorithm and numerical weather prediction model. We assume that the computing resources are allocated in a HPC cloud on a pay-per-use basis. The measurements demonstrate that the minimal energy is consumed when all available cores (up to the scaling limit for a particular application) are used on their maximal frequencies and with threads binded to the cores.Key words: Energy efficiency, high-performance, maximum clique, numerical weather prediction AMS subject classifications. 65Y05, 68U201. Introduction. Nowadays, a large number of hard problems is being solved efficiently on large-scale computing systems such as clusters, grids and clouds. The main building blocks of such systems are highperformance computing (HPC) clusters, since they are furthermore embedded both into grid computing systems as well as cloud computing systems [1]. A great advantage of Graphical Processor Unit (GPU) accelerators over multicore platforms was demonstrated in terms of energy efficiency [2] with an overall energy consumption for an order of magnitude smaller for the GPUs. Inside the clusters, the many-core architectures, such as GPUs [3], as well as even more specialized data-flow computers [4] are getting an increasingly large supporting role but the main processing is still performed by the multi-core main processors because of simple programming model.In this research, we analyze the energy consumption profile of the processors on a two processor multi-core computer, while varying its workload.With the increasing number of components in large-scale computing systems the energy consumption is steeply increasing [5]. In computer systems, energy (measured in Joules) is represented as the electricity resource that can power the hardware components to do computation. On the other hand, power is the amount of energy consumed per unit time (measured in Joules per second or Watts). The ever-increasing energy consumption of large-scale computing systems causes higher operation costs (e.g. electricity bills) and has negative environmental impacts (e.g. carbon dioxide emissions). Therefore, when designing large-scale computing systems, the focus is often shifting from performance improvements to energy efficiency.Energy can be reduced at several levels of the distrib...

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Demystifying energy consumption in Grids and Clouds

Cited by 48 publications

References 26 publications

Energy-aware hierarchical scheduling of applications in large scale data centers

Energy-aware hierarchical scheduling of applications in large scale data centers

Performance vs. Power and Energy Consumption

Energy Efficiency of Parallel Multicore Programs

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