Energy-cost-aware scheduling of HPC workloads

Aikema, David; Kiddle, Cameron; Simmonds, Rob

doi:10.1109/wowmom.2011.5986476

Cited by 16 publications

(13 citation statements)

References 13 publications

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“…al. [3] discussed the possibility of altering the scheduling of HPC workloads according to changes in the cost of power allowing for significant cost savings. The authors demonstrated a simulator that alters the cluster scheduler to delay the execution of lowerpriority jobs when power prices are high.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, we have witnessed a growing interest in reducing energy consumption of large computational systems using various energy conservation techniques [1][2][3][4][5][6][7]. This interest is driven by the fact that energy is the biggest expense in operating large clusters, and energy cost over the life span of such a system may exceed the system's initial capital investment [8].…”

Section: Introductionmentioning

confidence: 99%

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Toward building green computing environment for reservoir simulation

Al-Saadoon

Al-Shaikh

Baddourah

et al. 2013

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

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In this paper, we analyze the level of power consumption for running large scale hydrocarbonreservoir simulation on high performance clusters (HPC), which may impact the data center design and the overall environment for running massive simulation models. We derive energy consumption from simulation run time and the power needed to keep associated processors running. We then propose a metric that ties reservoir simulation to Power Usage Effectiveness (PUE) factor based on energy consumption per-node. This factor works as the efficiency target that can be adjusted to balance execution time and power consumption.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward building green computing environment for reservoir simulation

Al-Saadoon

Al-Shaikh

Baddourah

et al. 2013

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

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“…Elsewhere we have also examined the separation of jobs into two classes, permitting high priority jobs to suspend low priority jobs, and also suspending low priority jobs when the price of power was high. This reduced the power cost of the low priority jobs for the workloads studied by 25-50% while providing high priority users with access to large-scale resources [14].…”

Section: Solar Generationmentioning

confidence: 99%

“…Computer systems are becoming more energy proportional [14], with the most energy-proportional system now available consuming less than 25% of their peak power at idle compared to 50% just a few years ago.…”

Section: Solar Generationmentioning

confidence: 99%

Electrical cost savings and clean energy usage potential for HPC workloads

Aikema

Simmonds

2011

Proceedings of the 2011 IEEE International Symposium on Sustainable Systems and Technology

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Data centres containing high-performance computing (HPC) clusters may be able to coordinate with the operation of wind farms for mutual benefit. Large data centres consume megawatts of power, typically accounting for a majority of life cycle carbon emissions and a significant portion of the total cost of ownership.We ran simulations to explore the potential for data centres to adapt to dynamic electrical prices, variation in carbon intensity within an electrical grid, or the availability of local renewables. Using workloads from the Parallel Workloads Archive alongside real-world pricing data, we demonstrate potential savings on the cost of electricity ranging typically between 10-50%. Adaptation to the variation in the electrical grid carbon intensity was not as successful, but adaptation to the availability of local renewables showed potential to significantly increase their use. In one example the fraction of power obtained from a local wind installation increased by 10-80%.

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“…So far efforts were focused on treating DCs as isolated islands: coordinated cooling and load management to reduce energy consumption [6], energy efficiency using virtualization techniques [7] or load (re)distribution [8], and of course big players of the industry, such as Google, adopting novel techniques for their back-up storage [9]. Although such efforts do provide valuable tools and practices towards reducing energy consumption and environmental footprint, they should be considered as just the beginning of the journey since: (a) the energy demand is still on the rise, so obviously they are not enough, and (b) they are missing out positive synergistic effects that emerge from considering DCs as connection hubs within both data and energy (including both electricity and heat) networks.…”

Section: Introductionmentioning

confidence: 99%

GEYSER: Enabling Green Data Centres in Smart Cities

Anghel

Bertoncini

Cioara

et al. 2015

Energy Efficient Data Centers

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Abstract. Information Technology is a dominant player of our modern societies; Data Centres, lying at the heart of the IT landscape, have attracted attention, with their increasing energy consumption being a constant topic of concern, especially when it comes to the negative impact on the quality of their surrounding environment. Nevertheless, recent technological and societal advances are paving the way for DCs to change their role from passive energy consumers into prosumers, thus, transforming themselves into leading players within their smart district surroundings. This paper describes the innovative GEYSER approach to enabling green networked DCs to monitor, control, reuse, and optimize both their energy consumption and production, and in particular from renewable resources, towards becoming active participants within Smart Grids and Smart Cities.

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Energy-cost-aware scheduling of HPC workloads

Cited by 16 publications

References 13 publications

Toward building green computing environment for reservoir simulation

Toward building green computing environment for reservoir simulation

Electrical cost savings and clean energy usage potential for HPC workloads

GEYSER: Enabling Green Data Centres in Smart Cities

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