Scheduling Grid workloads on multicore clusters to minimize energy and maximize performance

Lammie, Michael; Thain, Douglas

doi:10.1109/grid.2009.5353071

Cited by 16 publications

(12 citation statements)

References 22 publications

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“…Lammie et al [15] analyze the effect of automated node scaling (turn on/off), CPU frequency scaling, and job assignment on power consumption and performance of grid workload on larges clusters. Orgirie et al [17], [18] propose an energyaware model to reduce the global energy consumption of a large scale experimental grid.…”

Section: Related Workmentioning

confidence: 99%

“…The power consumption of the machines was attributed based on their model and the power consumption specification provided by their manufactures. We assumed that all jobs submitted to the grid are CPU-bound, which facilitated the calculation of the energy consumption; other studies have also made a similar simplification [15]. To select machines for scenarios with less than 360 machines we did as follows.…”

Section: The Experimental Setupmentioning

confidence: 99%

“…There are some studies that deal with reduction of energy consumption in grids and clusters using sleeping strategies (or power down modes) [15], [17], [18], [23]. Lammie et al [15] analyze the effect of automated node scaling (turn on/off), CPU frequency scaling, and job assignment on power consumption and performance of grid workload on larges clusters.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, if for one side this also makes them suitable for being explored opportunistically, the lack of a background processing that "pays" for the baseline energy consumption makes their exploitation less energy-efficient than that achieved with personal computers. Moreover, previous studies show that P2P grids are rarely operated at full utilization at all times [10], and the demand for resources is typically bursty [15], [17], [18]. During bursts of resource demand, many grid resources are required, but on other times they remain idle for long periods.…”

Section: Introductionmentioning

confidence: 97%

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On the impact of energy-saving strategies in opportunistic grids

Ponciano

Brasileiro

2010

2010 11th IEEE/ACM International Conference on Grid Computing

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Opportunistic grids are distributed computing infrastructures that harvest the idle computing cycles of computing resources geographically distributed. In these grids, the demand for resources is typically bursty. During bursts of resource demand, many grid resources are required, but on other times they remain idle for long periods. If the resources are kept powered on even when they are neither processing their owners workload nor grid jobs, their exploitation is not efficient in terms of energy consumption. One way to reduce the energy consumed in these idleness periods is to place the computers that form the grid in a "sleeping" mode which consumes less energy. We evaluated two sleeping strategies, denoted: standby and hibernate. Resources that comprise an opportunistic grid are normally very heterogeneous, and differ enormously on their processing power and energy consumption. It opens the possibility of implementing scheduling strategies that take energy-efficiency into account. We consider scheduling in two different levels. Firstly, how to choose which machine should be woken up, if several options are available. Secondly, how to decide which tasks to schedule to the available machines. In summary, our results presented a significant reduction in energy consumption, surpassing 80% in a scenario when the amount of resources in the grid was high. Moreover, this comes with limited impact on the response time of the applications.

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Section: Related Workmentioning

confidence: 99%

Section: The Experimental Setupmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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On the impact of energy-saving strategies in opportunistic grids

Ponciano

Brasileiro

2010

2010 11th IEEE/ACM International Conference on Grid Computing

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“…Furthermore, it is not based on the DVFS technique. Energy-performance tradeoff by varying the number and frequency of processors available has been investigated in the job scheduling of sequential job ( [30]). …”

Section: Related Workmentioning

confidence: 99%

Linear programming based parallel job scheduling for power constrained systems

Etinski

Corbalan

Labarta

et al. 2011

2011 International Conference on High Performance Computing &Amp; Simulation

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Power has become the primary constraint in high performance computing. Traditionally, parallel job scheduling policies have been designed to improve certain job performance metrics when scheduling parallel workloads on a system with a given number of processors. The available number of processors is not anymore the only limitation in parallel job scheduling. The recent increase in processor power consumption has resulted in a new limitation: the available power. Given constraints naturally lead to an optimization problem. In this paper we propose MaxJobPerf, a new parallel job scheduling policy based on integer linear programming. Dynamic Voltage Frequency Scaling (DVFS) is a widely used technique that running applications at reduced CPU frequency/voltage trades increased execution time for power reduction. The optimization problem determines which jobs should run and at which frequency. The MaxJobPerf policy clearly outperforms the other power budgeting approaches at the parallel job scheduling level.

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Modeling and simulation of global and sleep states in ACPI‐compliant energy‐efficient cloud environments

Xavier

Rossi

Rose

et al. 2016

Concurrency and Computation

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SUMMARYThe more large-scale data centers infrastructure costs increase, the more simulation-based evaluations are needed to understand better the trade-off between energy and performance and support the development of new energy-aware resource allocation policies. Specifically, in the cloud computing field, various simulators are able to predict and measure the behavior of applications on different architectures using different resource allocation policies. Yet, only a few of them have the ability to simulate energy-saving strategies, and none of them support the complete advanced configuration and power interface (ACPI) specification. ACPI defines a terminology for all possible power states of a machine and their associated power rate. The hardware industry has relied on ACPI to provide up-to-date standard interfaces for hardware discovery, configuration, power management, and monitoring, enabling a better understanding of the energy consumption level of different hardware states, referred to as ACPI G-states, S-states, and P-states. In this paper, we improve the modeling and simulation of the ACPI G/S-states and show not only that these states offer different energy-saving levels but also that state transitions consume energy. In addition, we model the latency to transit between two states and the effects on the turnaround time when the transitions are not performed conservatively. Furthermore, the equations provide essential information to quantify the trade-off between energy consumption and performance and assist in the analysis/decision on which strategy fits better in the environment and how it could be refined. Our expanded energy model was implemented in CloudSim and validated with simulation-based experiments with a very high level of accuracy, with a standard deviation of at most 6%.

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Scheduling Grid workloads on multicore clusters to minimize energy and maximize performance

Abstract: Abstract

Cited by 16 publications

References 22 publications

On the impact of energy-saving strategies in opportunistic grids

On the impact of energy-saving strategies in opportunistic grids

Linear programming based parallel job scheduling for power constrained systems

Modeling and simulation of global and sleep states in ACPI‐compliant energy‐efficient cloud environments

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