Multi-objective efficiency enhancement using workload spreading in an operational data center

Khalaj, Ali Habibi; Scherer, Thomas; Siriwardana, Jayantha; Halgamuge, Saman K.

doi:10.1016/j.apenergy.2014.10.083

Cited by 24 publications

(3 citation statements)

References 32 publications

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“…Several simulations are performed in order to form a Pareto front to demonstrate a set of non-dominated design points. In this research, this algorithm (Habibi Khalaj et al, 2015) is implemented in MATLAB in order to increase efficiency as well as reducing the programming runtime. First, using the previously generated objective function, an initial population—consisting of 1000 initial random design points, including their position, velocity, and cost value—is generated.…”

Section: Optimization Proceduresmentioning

confidence: 99%

Optimization of a piezoelectric energy harvester considering electrical fatigue

Niasar

Dahmardeh

Googarchin

2020

Journal of Intelligent Material Systems and Structures

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The application of piezoelectric materials in energy harvesting devices has become an interesting topic for designers in recent years. Most of the reported works consider electrical power as a major parameter throughout the design procedure. What is less discussed is considering other design factors like the fatigue life of the harvester as well as the financial costs of the device. In this research, a methodology is introduced to design an optimum harvester, taking into account the aforementioned design factors. The finite element model of a piezoelectric harvester used in roadways is developed and validated. The fatigue test constants of the piezoelectric material are extracted via a novel approach. A parametric study is conducted on the model to generate a dataset containing electrical and mechanical characteristics, where it is used as an input for training an artificial neural network to model the behavior of the harvester. In order to evaluate the level of importance of the optimization objectives, two methods are employed: the Shannon entropy method and equal weighting factors. Results show the effectiveness of the model, where considering the electromechanical characteristics of the module is important in terms of overall performance, efficiency, durability, and financial costs.

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Section: Optimization Proceduresmentioning

confidence: 99%

Optimization of a piezoelectric energy harvester considering electrical fatigue

Niasar

Dahmardeh

Googarchin

2020

Journal of Intelligent Material Systems and Structures

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“…Based on the heat recirculation model, the supply temperature of cooling system is maximized for a given workload. Khalaj et al presented a reduce order model to describe the heat flow in data center [13]. On the base of this reduced-order model, they proposed a multi-objective optimization problem that minimizes the impact of hot spots by optimizing workload distribution.…”

Section: Relate Workmentioning

confidence: 99%

Comparative Study of Energy Performance between Chip and Inlet Temperature-Aware Workload Allocation in Air-Cooled Data Center

Yan

2018

Energies

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Improving the energy efficiency of data center has become a research focus in recent years. Previous works commonly adopted the inlet temperature constraint to optimize the thermal environment in the data center. However, the inlet temperature-aware method cannot prevent the servers from over-cooling. To cope with this issue, we propose a thermal-aware workload allocation strategy with respect to the chip temperature constraint. In this paper, we conducted a comparative evaluation of the performance between the chip and inlet temperature-aware workload allocation strategies. The workload allocation strategies adopt a POD-based heat recirculation model to characterize the thermal environment in data center. The contribution of the temperature-dependent leakage power to server power consumption is also considered. We adopted a sample data center under constant-flow and variable-flow cooling air supply to evaluate the performance of these two different workload allocation strategies. The comparison results show that the chip temperature-aware workload allocation strategy prevents the servers from over-cooling and significantly improves the energy efficiency of data center, especially for the case of variable-flow cooling air supply.

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“…Fang et al [14] combined a two-time-scale control algorithm with Tang's model to optimize the workload allocation, the cooling supply and the IT equipment operating state; thus the total power of the cooling system and IT equipment was minimized. In addition to this, Khalaj et al [15] proposed another reduced-order model to predict the temperature distribution in data centers. On the basis of the prediction, a particle swarm algorithm was employed to find the best load allocation strategy for a given total workload.…”

Section: Related Workmentioning

confidence: 99%

Chip Temperature-Based Workload Allocation for Holistic Power Minimization in Air-Cooled Data Center

Yan

2017

Energies

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Minimizing the energy consumption is a dominant problem in data center design and operation. To cope with this issue, the common approach is to optimize the data center layout and the workload distribution among servers. Previous works have mainly adopted the temperature at the server inlet as the optimization constraint. However, the inlet temperature does not properly characterize the server's thermal state. In this paper, a chip temperature-based workload allocation strategy (CTWA-MTP) is proposed to reduce the holistic power consumption in data centers. Our method adopts an abstract heat-flow model to describe the thermal environment in data centers and uses a thermal resistance model to describe the convective heat transfer of the server. The core optimizes the workload allocation with respect to the chip temperature threshold. In addition, the temperature-dependent leakage power of the server has been considered in our model. The proposed method is described as a constrained nonlinear optimization problem to find the optimal solution by a genetic algorithm (GA). We applied the method to a sample data center constructed with computational fluid dynamics (CFD) software. By comparing the simulation results with other different workload allocation strategies, the proposed method prevents the servers from overcooling and achieves a substantial energy saving by optimizing the workload allocation in an air-cooled data center.

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Multi-objective efficiency enhancement using workload spreading in an operational data center

Cited by 24 publications

References 32 publications

Optimization of a piezoelectric energy harvester considering electrical fatigue

Optimization of a piezoelectric energy harvester considering electrical fatigue

Comparative Study of Energy Performance between Chip and Inlet Temperature-Aware Workload Allocation in Air-Cooled Data Center

Chip Temperature-Based Workload Allocation for Holistic Power Minimization in Air-Cooled Data Center

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