Server Power Modeling for Run-time Energy Optimization of Cloud Computing Facilities

Arroba, Patricia; Risco‐Martín, José L.; Zapater, Marina; Moya, José Manuel Huidobro; Ayala, José L.; Olcoz, Katzalin

doi:10.1016/j.egypro.2014.12.402

Cited by 20 publications

(45 citation statements)

References 17 publications

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“…Mohammad Mehedi Hassan et al [9] proposed an inescapable method, based on Multi-Objective Particle Swarm Optimization, for the identification of capability faculty models of enterprise servers in Cloud text centers. The proper fatigue of a single data center is equivalent to the energy consumption of 25.000 households.…”

Section: A I Awad Et Al [3]mentioning

confidence: 99%

A Study for VM Placement Schemes in Cloud

Kaur

2017

IJARCSSE

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Abstract-The cloud computing can be essentially expressed as aconveyance of computing condition where distinctive assets are conveyed as a support of the client or different occupants over the web. The task scheduling basically concentrates on improving the productive use of assets and henceforth decrease in task fruition time. Task scheduling is utilized to allot certain tasks to specific assets at a specific time occurrence. A wide range of systems has been exhibited to take care of the issues of scheduling of various tasks. Task scheduling enhances the productive use of asset and yields less reaction time with the goal that the execution of submitted tasks happens inside a conceivable least time. This paper talks about the investigation of need, length and due date based task scheduling calculations utilized as a part of cloud computing.

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Section: A I Awad Et Al [3]mentioning

confidence: 99%

A Study for VM Placement Schemes in Cloud

Kaur

2017

IJARCSSE

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“…In our previous work, we have applied the benefits of Particle Swarm Optimization algorithms (PSO) to identify an analytical model that provides accurate results for power estimation [2]. PSO simplifies the power model by significantly reducing the number of predefined parameters and variables used in the analytical formulation.…”

Section: Related Workmentioning

confidence: 99%

“…The power consumption of a high-end server usually depends on several factors that affect both dynamic and static behavior [2]. Our proposed case study takes into account the following 7 variables: Power consumption is measured with a current clamp with the aim of validating our approach.…”

Section: Data Compilationmentioning

confidence: 99%

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Enhancing Regression Models for Complex Systems Using Evolutionary Techniques for Feature Engineering

et al. 2014

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This work proposes an automatic methodology for modeling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer's expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimizes error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98%. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers

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“…The proposed algorithm is based on a bin packing problem [1] where servers are represented as bins with variable sizes due to the frequency scaling. To design our optimization technique, we first characterize performance and power contributions in terms of those architectural parameters most influenced by DVFS and consolidation [2]. The obtained power model offers an accuracy of about 4.46% and allows a better understanding of how energy varies depending on frequency and utilization simultaneously.…”

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confidence: 99%

DVFS-Aware Consolidation for Energy-Efficient Clouds

Arroba

Moya

Ayala

et al. 2015

2015 International Conference on Parallel Architecture and Compilation (PACT)

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Motivation. Computational demand on data centers is increasing due to the growing popularity of Cloud applications. However, data centers are becoming unsustainable in terms of power consumption and growing energy costs so they must be placed on a more scalable curve. Recently, there has been a growing interest in developing techniques to provide power management in Clouds. Dynamic Voltage and Frequency Scaling (DVFS) helps to reduce the consumption of underutilized resources dynamically, while consolidation strategies decrease significantly the static consumption by reducing the number of active servers, thus increasing their utilization. However, DVFS is traditionally applied locally, regardless the consolidation techniques. Understanding the relationship between power, DVFS and consolidation is crucial to enable new energy-efficient strategies that combine these effective techniques. To this purpose, the dependency of power on some traditionally ignored factors like frequency or static consumption, which are increasingly influencing the consumption patterns of these infrastructures, must now be considered. Furthermore, as Cloud services are provided under strict Service Level Agreement (SLA) conditions, power consumption in data centers may be minimized, taking into account a trade-off between DVFS and performance, without violating the SLA requirements whenever it is feasible. Also, Cloud workloads vary significantly over time, difficulting the optimal allocation of resources that requires a tradeoff between consolidation and performance. Therefore, the implementation of consolidation policies that are aware of both DVFS and energy consumption while considering QoS has the potential to optimize the sustainability of Cloud data centers.Proposed Solution. In this work we aim to find an energy optimization strategy for Cloud data centers that combines DVFS and consolidation techniques. Our policy is not only aware of the utilization of the incoming workload to be assigned, but also is conscious of the impact of its allocation in terms of frequency. One of the main challenges when designing data center optimizations is to implement fast algorithms that can be evaluated during runtime. For this reason, our research is focused on the design of an optimization algorithm that is simple in terms of computational requirements, in which both decision making and its execution in a real infrastructure are fast. The proposed algorithm is based on a

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Server Power Modeling for Run-time Energy Optimization of Cloud Computing Facilities

Cited by 20 publications

References 17 publications

A Study for VM Placement Schemes in Cloud

A Study for VM Placement Schemes in Cloud

Enhancing Regression Models for Complex Systems Using Evolutionary Techniques for Feature Engineering

DVFS-Aware Consolidation for Energy-Efficient Clouds

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