Power optimization for dynamic configuration in heterogeneous web server clusters

Bertini, Luciano; Leite, Julius; Mossé, Daniel

doi:10.1016/j.jss.2009.10.040

Cited by 45 publications

(39 citation statements)

References 18 publications

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“…It takes into account task affinity and processor diversity to direct task scheduling between CPU and GPU. In [4] feedback controlling was combined with mixed integer programming, and the energy consumption controlling model of Web server cluster was constructed. In [5] model predictive controlling strategy was introduced from global perspective.…”

Section: Related Researchesmentioning

confidence: 99%

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A GPU Heterogeneous Cluster Scheduling Model for Preventing Temperature Heat Island

Cao

Wang

2017

ITM Web Conf.

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Abstract. With the development of GPU general-purpose computing, GPU heterogeneous cluster has become a widely used parallel data processing solution in modern data center. Temperature management and controlling has become a new research hotspot in big data continuous computing. Temperature heat island in cluster has important influence on computing reliability and energy efficiency. In order to prevent the occurrence of GPU cluster temperature heat island, a big data task scheduling model for preventing temperature heat island was proposed. In this model, temperature, reliability and computing performance are taken into account to reduce node performance difference and improve throughput per unit time in cluster. Temperature heat islands caused by slow nodes are prevented by optimizing scheduling. The experimental results show that the proposed scheme can control node temperature and prevent the occurrence of temperature heat island under the premise of guaranteeing computing performance and reliability.

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Section: Related Researchesmentioning

confidence: 99%

“…For a certain node, C i , Q 1 , Q 2 , Q 3 , Q 4 and Q 5 can be determined experimentally, and then the approximate value of k 1 , k 2 , k 3 , k 4 and k 5 can be determined by regression method.…”

Section: Estimating Current Computing Capabilitymentioning

confidence: 99%

A GPU Heterogeneous Cluster Scheduling Model for Preventing Temperature Heat Island

Cao

Wang

2017

ITM Web Conf.

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“…Sources for estimating power consumption on a device or component level are vendor-supplied information, including equipment faceplates and site planning guides. Other sources for determining energy usage are power meters and analyzers, as well as power distribution devices that can also measure and report power usage [29].…”

Section: Power Measuring Problems In Data Centersmentioning

confidence: 99%

Power Usage Effectiveness Metrics to Measure Efficiency and Performance of Data Centers

Uddin

Alsaqour

Shah

et al. 2014

Appl. Math. Inf. Sci.

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Abstract:Intensifying computation demand from enterprises has driven the growth of large, multifaceted data centers to manage current Internet, financial, commercial, and business applications. A data center comprises thousands of servers and other equipment that require substantial amounts of power to operate. This condition results in numerous challenges for the data center industry, such as massive energy consumption, underutilization of installed equipment, emission of greenhouse gases, and effect on global warming. This paper highlights the significance of identifying metrics to determine the performance and efficiency of a data center, which can help such a facility achieve operational cost savings through proper implementation of performance-measuring metrics. This paper discusses the implementation of Power Usage Effectiveness metrics in a tier-level data center in Pakistan. The results show that the overall performance value of the facility is 3.3, which indicates poor and inefficient operations.

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“…Although, they do not directly address the concept of energy savings, methods given in this paper can be applied for energy consumption reduction. In [27] the management of energy consumption in data centers is studied through optimization problems taking into account: frequency scaling, servers booting times and hysteresis. We also assume these issues but we make slightly different assumptions to discover appropriate settings for each workload.…”

Section: Related Workmentioning

confidence: 99%

“…We also assume these issues but we make slightly different assumptions to discover appropriate settings for each workload. For example, to accommodate an increased workload in the platform, in [27] the plan could dictate switch on or off the machines. In our case, with increasing workload if you have to select a new configuration of hardware, we decided to not reduce the number of servers.…”

Section: Related Workmentioning

confidence: 99%

QoS and energy management with Petri nets: A self-adaptive framework

Pérez-Palacín

Mirandola

Merseguer

2012

Journal of Systems and Software

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Energy use is becoming a key design consideration in computing infrastructures and services. In this paper we focus on service-based applications and we propose an adaptation framework that can be used to reduce power consumption according to the observed workload. The adaptation guarantees a trade-off between energy consumption and system performance. The approach is based on the principle of proportional energy consumption obtained by scaling down energy for unused resources, considering both the number of servers switched on and their operating frequencies. Stochastic Petri Nets are proposed for the modeling of the framework concerns, their analyses give results about the trade-offs. The application of the approach to a simple case study shows its usefulness and practical applicability. Finally, different types of workloads are analyzed with validation purposes.

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Power optimization for dynamic configuration in heterogeneous web server clusters

Cited by 45 publications

References 18 publications

A GPU Heterogeneous Cluster Scheduling Model for Preventing Temperature Heat Island

A GPU Heterogeneous Cluster Scheduling Model for Preventing Temperature Heat Island

Power Usage Effectiveness Metrics to Measure Efficiency and Performance of Data Centers

QoS and energy management with Petri nets: A self-adaptive framework

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