Mercury and freon

Heath, Taliver; Centeno, Ana Paula; George, Pradeep; Ramos, Luiz Armando Vidal; Jaluria, Yogesh; Bianchini, Ricardo

doi:10.1145/1168857.1168872

Cited by 137 publications

(9 citation statements)

References 19 publications

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“…Several authors have proposed various power models [71], either to be used in simulation as a tool in designing energy-efficient data centers [16], [25], [28], [32], [36], [42], [48], [72], [73], or for server-level optimization [15], [17], [36], [37], [43], [45], [48], [49], [59], [72]- [74], [20]- [25], [27], [32]. The works on server-level optimization can be divided into two categories: 1) hardware-based on the server's die temperature, fan speed, heat dissipation, voltage, current, capacitance, motherboard components, and resistance [8]- [14], and 2) software-based, reflecting the server's user-level utilization metrics such as CPU, memory, disk, and network [15], [16], [25]- [34], [17], [35]- [44], [18], [45]- [49], [19]- [24]. The software-based models can be divided into linear [15], [16], [25]- [34], [17], …”

Section: B Power Modeling For Energy Efficiency In Data Centersmentioning

confidence: 99%

“…In the various energy-efficient algorithms, a power model is used to predict the power consumption of an application and/or to estimate the energy consumed by a server after the execution of an algorithm to assess the effectiveness of the algorithm in achieving energy efficiency. Researchers proposed different power models for servers, based either on a server's hardware such as die temperature, fan speed, heat dissipation, voltage, current, capacitance, motherboard components, and resistance [8]- [14] or its user-level utilization metrics (CPU, memory, disk, and network) [15], [16], [25]- [34], [17], [35]- [44], [18], [45]- [49], [19]- [24]. The hardware-based power models require sensors to measure different variables on a server's hardware.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in this paper, we focus on the software-based power models. These models are divided into 2 categories: 1) linear [15], [16], [25]- [34], [17], [35]- [38], [43]- [48], [18], [49], [19]- [24], [50] and 2) non-linear [17], [26], [39]- [42], [48]. A linear model has a complexity of O(n) [51] compared to O(n 2 ) for a non-linear model [52].…”

Section: Introductionmentioning

confidence: 99%

“…Most research on linear power models and energy optimizations for servers propose models based on the lowest-and highest-power data points (referred to here as the ''Power Endpoints Model'' or simply PEM) [15], [16], [27], [38], [43]- [49], [53], [17]- [24], or models based on Simple Linear Regression (SLR) [54], [25], [26], [28]- [31], to predict the amount of power consumption. These models use CPU utilization as an independent variable for power prediction.…”

Section: Introductionmentioning

confidence: 99%

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Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and Evaluation

Ismail

Abed

2019

IEEE Access

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Cloud computing is a novel paradigm, where the limitations of ubiquitous connected devices in terms of computing, data access, networking and storage are solved through the use of cloud infrastructure. The pervasive adoption of cloud computing results in a rising carbon footprint due to the high energy consumption of computing servers. This negatively affects the environment and entails an associated increase in electricity costs and consequently operational costs. Many works proposed scheduling algorithms using software-centric power models in order to predict electric power consumption in underlying data centers and to schedule cloud tasks so as to reduce energy consumption. Linear models which are based on the lowestand highest-power data points (referred to here as the ''Power Endpoints Model'' -PEM) and the simple linear regression (SLR) model are the most used in the literature. However, these models have traditionally been evaluated using different environments, experimental setups, workloads, and error calculation formulas. In this paper, a unified classification and evaluation for these linear power models is presented, under unified setup, benchmarking applications, and error formula with the main goal being to achieve an objective comparison. A new power model is proposed, named Locally Corrected Multiple Linear Regression (LC-MLR), in order to increase prediction accuracy. A simulation framework for a cloud energy-aware scheduler is introduced. The framework combines the Energy-Aware Task Scheduling on Cloud Virtual Machines (EATSVM) with the LC-MLR power model, and facilitates performance measurement for cloud data centers. The scheduler with the new power model increases energy efficiency without degrading the qualities of service of the system. The workloads used for performance evaluation and comparisons in this work are generated using a diverse set of applications. The results show that LC-MLR outperforms the most-used models for simulation of power consumption of cloud data centers. The detailed performance analysis is elaborated in the paper.

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Section: B Power Modeling For Energy Efficiency In Data Centersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and Evaluation

Ismail

Abed

2019

IEEE Access

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“…Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permissions@acm.org. c 2014 ACM 1049-3301/2014/01-ART3 $15.00 DOI: http://dx.doi.org/10.1145/2553083 cost of running a data center is avoidable through an intelligent understanding and management of the cyber-physical [Lee 2008;Tang et al 2012; interactions within them. The predominant way that data center owners deal with idle power usage is to turn the equipment off when it is not needed.…”

Section: Introductionmentioning

confidence: 99%

GDCSim

Gupta

Banerjee

Abbasi

et al. 2014

ACM Trans. Model. Comput. Simul.

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ROSE ROBIN GILBERT, IO Data Centers TRIDIB MUKHERJEE, XeroxEnergy-efficient data center design and management has been a challenge of increasing importance in the past decade due to its potential to save billions of dollars in energy costs. However, the state of the art in design and evaluation of data centers require designers to be expertly familiar with a prohibitively large number of domain-specific design tools that necessitate user intervention in each step of the design process. This is due to the lack of a holistic data center design tool. To fill this gap, this article presents an iterative green data center design framework, the Green Data Center Simulator (GDCSim), for the design and development of energy-efficient data centers.

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Green Cloud Computing and Environmental Sustainability

Kumar

Buyya

2012

Harnessing Green It

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Cloud computing is a highly scalable and cost-effective infrastructure for running HPC, enterprise and Web applications. However, the growing demand of Cloud infrastructure has drastically increased the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high operational cost, which reduces the profit margin of Cloud providers, but also leads to high carbon emissions which is not environmentally friendly. Hence, energy-efficient solutions are required to minimize the impact of Cloud computing on the environment. In order to design such solutions, deep analysis of Cloud is required with respect to their power efficiency. Thus, in this chapter, we discuss various elements of Clouds which contribute to the total energy consumption and how it is addressed in the literature. We also discuss the implication of these solutions for future research directions to enable green Cloud computing. The chapter also explains the role of Cloud users in achieving this goal. and generally include specific component for developing applications, advanced services for application monitoring, management, and reporting.

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Mercury and freon

Cited by 137 publications

References 19 publications

Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and Evaluation

Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and Evaluation

GDCSim

Green Cloud Computing and Environmental Sustainability

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