Review of performance metrics for green data centers: a taxonomy study

Wang, Lizhe; Khan, Samee U.

doi:10.1007/s11227-011-0704-3

Cited by 157 publications

(74 citation statements)

References 20 publications

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“…Another method to predict resource temperatures is using artificial intelligence techniques, such as support vector machine, neural network, generic algorithm [54], [56]. [52] develop a taxonomy study of performance metrics for green data centers.…”

Section: B Thermal-aware Methodsmentioning

confidence: 99%

Energy-Aware High Performance Computing: A Taxonomy Study

Cai

Wang

Khan

et al. 2011

2011 IEEE 17th International Conference on Parallel and Distributed Systems

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Abstract-To reduce the energy consumption and build a sustainable computer infrastructure now becomes a major goal of the high performance community. A number of research projects have been carried out in the field of energy-aware high performance computing. This paper is devoted to categorize energy-aware computing methods for the high-end computing infrastructures, such as servers, clusters, data centers, and Grids/Clouds. Based on a taxonomy of methods and system scales, this paper reviews the current status of energy-aware HPC research and summarizes open questions and research directions of software architecture for future energy-aware HPC studies.

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Section: B Thermal-aware Methodsmentioning

confidence: 99%

Energy-Aware High Performance Computing: A Taxonomy Study

Cai

Wang

Khan

et al. 2011

2011 IEEE 17th International Conference on Parallel and Distributed Systems

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“…Several research works have used similar models and approaches, that have addressed various research problems related to large-scale computing systems, such as energy proportionality [17,20], memory-aware computations, data intensive computations, energy-efficient, and grid scheduling [22,39]. A lot of interesting examples of recently developed static and dynamic power and energy management techniques in the distributed computing environments are presented in the following surveys [5,40,43,44]. [28] Although a significant volume of the research has been provided in energy effective scheduling and resource allocation in large-scale computing systems, still not so large family of energy-aware genetic-based grid and cloud schedulers have been developed.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical genetic-based grid scheduling with energy optimization

et al. 2012

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An optimization of power and energy consumptions is the important concern for a design of modern-day and future computing and communication systems. Various techniques and high performance technologies have been investigated and developed for an efficient management of such systems. All these technologies should be able to provide good performance and to cope under an increased workload demand in the dynamic environments such as Computational Grids (CGs), clusters and clouds. In this paper we approach the independent batch scheduling in CG as a bi-objective minimization problem with makespan and energy consumption as the scheduling criteria. We use the Dynamic Voltage Scaling (DVS) methodology for scaling and possible reduction of cumulative power energy utilized by the system resources. We develop two implementations of Hierarchical Genetic Strategy-based grid scheduler (Green-HGS-Sched) with elitist and struggle replacement mechanisms. The proposed algorithms were empirically evaluated versus single-population Genetic Algorithms (GAs) and Island GA models for four CG size scenarios in static and dynamic modes. The simulation results show that proposed scheduling methodologies fairly reduce the energy usage and can be easily adapted to the dynamically changing grid states and various scheduling scenarios.

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“…The efficiency of a data center can be defined in terms of the performance delivered per watt, which may be quantified by the following two metrics: (a) Power Usage Effectiveness (PUE) and (b) Data Center Infrastructure Efficiency (DCiE) [15,16]. Both PUE and DCiE describe which portion of the totally consumed energy gets delivered to the computing servers.…”

Section: Energy Efficiencymentioning

confidence: 99%

Simulation and Performance Analysis of Data Intensive and Workload Intensive Cloud Computing Data Centers

2012

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Data centers are becoming increasingly popular for the provisioning of computing resources. The cost and operational expenses of data centers have skyrocketed with the increase in computing capacity [1]. Energy consumption is a growing concern for data center operators. It is becoming one of the main entries on a data center operational expenses (OPEX) bill [2,3]. The Gartner Group estimates energy consumptions to account for up to 10% of the current OPEX, and this estimate is projected to rise to 50% in the next few years [4]. However, computing-based energy consumption is not the only power-related portion of the OPEX bill. High power consumption generates heat and requires an accompanying cooling system that costs in a range of $2-$5 million per year for classical data centers [5]. Failure to keep data center temperatures within operational ranges drastically decreases hardware reliability and may potentially violate the service level agreement (SLA) with the customers.From the perspective of energy efficiency, a cloud computing data center can be defined as a pool of computing and communication resources organized in the way to transform the received power into computing or data transfer work to satisfy user demands. The first power saving solutions focused on making the data center hardware components power efficient. Technologies, such as dynamic voltage and frequency scaling (DVFS), and dynamic power management (DPM) [6], were

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Review of performance metrics for green data centers: a taxonomy study

Cited by 157 publications

References 20 publications

Energy-Aware High Performance Computing: A Taxonomy Study

Energy-Aware High Performance Computing: A Taxonomy Study

Hierarchical genetic-based grid scheduling with energy optimization

Simulation and Performance Analysis of Data Intensive and Workload Intensive Cloud Computing Data Centers

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