Data Center Energy Consumption Modeling: A Survey

Dayarathna, Miyuru; Wen, Yonggang; Fan, Rui

doi:10.1109/comst.2015.2481183

Cited by 761 publications

(339 citation statements)

References 322 publications

Supporting

Mentioning

333

Contrasting

Unclassified

Order By: Relevance

“…As stated in [43], some facilities even have power densities more than 100 times that of a typical office building, so that the electricity costs are a dominant operating expense for those facilities and occupy over 10% of the total cost of ownership (TCO) of a datacenter [44]. The cost of power for the global datacenter had already exceeded the cost of the original capital investment by 2012 [45].…”

Section: Power Consumption Trendmentioning

confidence: 99%

Sustainable and Resilient Network Infrastructure Design for Cloud Data Centers

Liu

Gutiérrez

et al. 2017

Engineering and Management of Data Centers

View full text Add to dashboard Cite

Section: Power Consumption Trendmentioning

confidence: 99%

Sustainable and Resilient Network Infrastructure Design for Cloud Data Centers

Liu

Gutiérrez

et al. 2017

Engineering and Management of Data Centers

View full text Add to dashboard Cite

“…They can be categorized into two main spheres: models focused on hardware and models focused on software [1]. For an in-depth discussion of the different models for energy saving systems, one can turn to the review in [1]. The present study focuses on the infrastructure monitoring stage present in all the proposals in the literature, which constitutes our main objective.…”

Section: Related Workmentioning

confidence: 99%

“…As a result of this research activity, many different approaches have been proposed to save energy. They can be categorized into two main spheres: models focused on hardware and models focused on software [1]. For an in-depth discussion of the different models for energy saving systems, one can turn to the review in [1].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Smart Monitoring Embedded Service for Energy-Efficient and Sustainable Management in Data Centers

et al. 2016

View full text Add to dashboard Cite

Information technologies (IT) currently represent 2% of CO 2 emissions. In recent years, a wide variety of IT solutions have been proposed, focused on increasing the energy efficiency of network data centers. Monitoring is one of the fundamental pillars of these systems, providing the information necessary for adequate decision making. However, today's monitoring systems (MSs) are partial, specific and highly coupled solutions. This study proposes a model for monitoring data centers that serves as a basis for energy saving systems, offered as a value-added service embedded in a device with low cost and power consumption. The proposal is general in nature, comprehensive, scalable and focused on heterogeneous environments, and it allows quick adaptation to the needs of changing and dynamic environments. Further, a prototype of the system has been implemented in several devices, which has allowed validation of the proposal in addition to identification of the minimum hardware profile required to support the model.

show abstract

“…A comprehensive survey in [15] reviewed most of the available PC models to date, including virtualised and non virtualised servers, data centres as well as single server. Generally, the available power models can be classified into hardware, software intrusive and machine learning.…”

Section: Related Workmentioning

confidence: 99%

Maximizing the Energy Efficiency of Virtualized C-RAN via Optimizing the Number of Virtual Machines

Alhumaima

Ahmed

Al-Raweshidy

2018

IEEE Trans. on Green Commun. Netw.

View full text Add to dashboard Cite

Abstract-In cloud radio access networks (C-RAN), more accurate prediction of the number of virtual machines (VMs) one server can support would improve network capacity and energy efficiency (EE). In this paper, the problem of allocating an optimal number of VMs to the cloud server is introduced. Monte Carlo based evolutionary algorithm (PSO, QPSO or GA) are used to find the suboptimal number of VMs that optimises the energy efficiency (EE) of C-RAN. To enable such evaluation, a power model is proposed to evaluate the power consumption (PC) of each unit within a virtualised server. This evaluation occurs under the circumstances of increased number of hosted VMs, and processed resource blocks (RBs) at each VM. Moreover, power allocation methods are proposed to transmit the power from base band unit (BBU) pool to the remote radio heads (RRHs), and from RRHs to the users (UEs). This allocation is based on the combination of one or more of RRH distance, RRH channel gain, UE distance, UE channel gain, and UE path loss. The EE problem was constrained to crucial quality of service (QoS) indicators, including minimum UE data rate, number of allocated RBs, and latency imposed due to virtualisation.

show abstract

Data Center Energy Consumption Modeling: A Survey

Cited by 761 publications

References 322 publications

Sustainable and Resilient Network Infrastructure Design for Cloud Data Centers

Sustainable and Resilient Network Infrastructure Design for Cloud Data Centers

Smart Monitoring Embedded Service for Energy-Efficient and Sustainable Management in Data Centers

Maximizing the Energy Efficiency of Virtualized C-RAN via Optimizing the Number of Virtual Machines

Contact Info

Product

Resources

About