United States Data Center Energy Usage Report

Shehabi, Arman; Smith, Sarah; Sartor, Dale; Brown, Richard E.; Herrlin, Magnus K.; Koomey, Jonathan; Masanet, Eric; Horner, Nathaniel; Azevedo, Inês L.; Lintner, William

doi:10.2172/1372902

Cited by 615 publications

(396 citation statements)

References 23 publications

Supporting

Mentioning

358

Contrasting

Unclassified

Order By: Relevance

“…Beginning in 2013, the US government initiated a carbon-tax on IT organizations to encourage major CSPs to pursue green energy opportunities for their datacenters operations [73]. US datacenters are projected to consume approximately 73 billion kWh by 2020 [73] with a corresponding increase greenhouse gases.…”

Section: Management and Budget Issued A Federal Data Center Optimizatmentioning

confidence: 99%

“…US datacenters are projected to consume approximately 73 billion kWh by 2020 [73] with a corresponding increase greenhouse gases. Green energy generation growth is expected to triple by 2040 [65].…”

Section: Management and Budget Issued A Federal Data Center Optimizatmentioning

confidence: 99%

“…We used server utilization and power metrics from [62,73] to design the prototype. Most of these sources we considered have limited utilization rates and server utilization distribution.…”

Section: On-demand Streaming and Cdnmentioning

confidence: 99%

See 2 more Smart Citations

Cloud Computing Cost and Energy Optimization through Federated Cloud SoS

2017

View full text Add to dashboard Cite

CLOUD COMPUTING COST AND ENERGY OPTIMIZATION THROUGH FEDERATED CLOUD SOSThe two most significant differentiators amongst contemporary Cloud Computing service providers have increased green energy use and datacenter resource utilization. This work addresses these two issues from a system's architectural optimization viewpoint. The proposed approach herein, allows multiple cloud providers to utilize their individual computing resources in three ways by:(1) cutting the number of datacenters needed, (2) scheduling available datacenter grid energy via aggregators to reduce costs and power outages, and lastly by (3) utilizing, where appropriate, more renewable and carbon-free energy sources. Altogether our proposed approach creates an alternative paradigm for a Federated Cloud SoS approach. The proposed paradigm employs a novel control methodology that is tuned to obtain both financial and environmental advantages. It also supports dynamic expansion and contraction of computing capabilities for handling sudden variations in service demand as well as for maximizing usage of time varying green energy supplies. Herein we analyze the core SoS requirements, concept synthesis, and functional architecture with an eye on avoiding inadvertent cascading conditions. We suggest a physical architecture that diminishes unwanted outcomes while encouraging desirable results. Finally, in our approach, the constituent cloud services retain their independent ownership, objectives, funding, and sustainability means.This work analyzes the core SoS requirements, concept synthesis, and functional architecture.It suggests a physical structure that simulates the primary SoS emergent behavior to diminish unwanted outcomes while encouraging desirable results. The report will analyze optimal computing generation methods, optimal energy utilization for computing generation as well as a procedure for building optimal datacenters using a unique hardware computing system design based on the openCompute community as an illustrative collaboration platform. Finally, the research concludes with ii security features cloud federation requires to support to protect its constituents, its constituents tenants and itself from security risks.iii ACKNOWLEDGEMENTS

show abstract

Section: Management and Budget Issued A Federal Data Center Optimizatmentioning

confidence: 99%

Section: Management and Budget Issued A Federal Data Center Optimizatmentioning

confidence: 99%

See 1 more Smart Citation

Cloud Computing Cost and Energy Optimization through Federated Cloud SoS

2017

View full text Add to dashboard Cite

show abstract

“…According to several studies, average server utilization levels usually vary between 10 and 50 percent [9], [10], [14]. In June 2016, the United States Data Center Energy Usage Report forecasted the average utilization of active volume servers to be approximately 15 % for internal data centers and 25 % for data centers of service providers in 2020 [15]. According to this report, volume servers represent by far the largest share of total servers in data centers and show an adverse power proportionality in the effect of consuming about 50 percent of their maximum power usage at low utilization levels of around 10 percent.…”

Section: Introductionmentioning

confidence: 99%

Capacity Management as a Service for Enterprise Standard Software

Müller

Bosse

Turowski

2017

CSIMQ

View full text Add to dashboard Cite

Abstract. Capacity management approaches optimize component utilization from a strong technical perspective. In fact, the quality of involved services is considered implicitly by linking it to resource capacity values. This practice hinders to evaluate design alternatives with respect to given service levels that are expressed in user-centric metrics such as the mean response time for a business transaction. We argue that utilized historical workload traces often contain a variety of performance-related information that allows for the integration of performance prediction techniques through machine learning. Since enterprise applications excessively make use of standard software that is shipped by large software vendors to a wide range of customers, standardized prediction models can be trained and provisioned as part of a capacity management service which we propose in this article. Therefore, we integrate knowledge discovery activities into well-known capacity planning steps, which we adapt to the special characteristics of enterprise applications. Using a realworld example, we demonstrate how prediction models that were trained on a large scale of monitoring data enable cost-efficient measurement-based prediction techniques to be used in early design and redesign phases of planned or running applications. Finally, based on the trained model, we demonstrate how to simulate and analyze future workload scenarios. Using a Pareto approach, we were able to identify cost-effective design alternatives for an enterprise application whose capacity is being managed.

show abstract

“…Major cloud service providers devour many megawatts of power to operate such data centers, with the related annual power bills amounting to millions of dollars. According to current trend estimates, the United States' data centers alone are expected to consume about 73 billion kWh per year by 2020 [2]. In fact, all the data centers around the world devour more power than most countries in the world, except for four countries [3].…”

Section: Introductionmentioning

confidence: 99%

EH-GC: An Efficient and Secure Architecture of Energy Harvesting Green Cloud Infrastructure

et al. 2017

View full text Add to dashboard Cite

Nowadays, the high power consumption of data centers is the biggest challenge to making cloud computing greener. Many researchers are still seeking effective solutions to reduce or harvest the energy produced at data centers. To address this challenge, we propose a green cloud infrastructure which provides security and efficiency based on energy harvesting (EH-GC). The EH-GC is basically focused on harvesting the heat energy produced by data centers in the Infrastructure-as-a-Service (IaaS) infrastructure. A pyroelectric material is used to generate the electric current from heat using the Olsen cycle. In order to achieve efficient green cloud computing, the architecture utilizes a genetic algorithm for proper virtual machine allocation, taking into consideration less Service Level Agreement (SLA) violations. The architecture utilizes Multivariate Correlation Analysis (MCA) correlation analysis based on a triangular map area generation to detect Denial of Service (DoS) attacks in the data center layer of the IaaS. Finally, the experimental analysis is explained based on the energy parameter, which proves that our model is efficient and secure, and that it efficiently reuses the energy emitted from the data center.

show abstract

United States Data Center Energy Usage Report

Cited by 615 publications

References 23 publications

Cloud Computing Cost and Energy Optimization through Federated Cloud SoS

Cloud Computing Cost and Energy Optimization through Federated Cloud SoS

Capacity Management as a Service for Enterprise Standard Software

EH-GC: An Efficient and Secure Architecture of Energy Harvesting Green Cloud Infrastructure

Contact Info

Product

Resources

About