Paper Info
Title
Towards Economic Energy Trading in Cloud Environments
Abstract
Especially in times of heavy loads, cloud providers often have to outsource tasks to external clouds to fulfill service level agreements. Nevertheless, a cloud provider maximizes the company's benefit while running as many jobs as possible on the own hardware without going below a specific workload of the running processors. Since cloud providers will have to estimate the required energy in advance due to energy trading, they should aim for estimating maturely the optimal number of necessary processors for a future date and time. This paper presents a method for anticipating the optimal number of active processors and corresponding energy. In particular, this work analyzes the potential of Gaussian processes to estimate future jobs by considering statistical data. Based on the job number estimate, a second Gaussian process approximates the optimal number of processors for a future date allowing for economical energy trading. Finally, the paper optimizes the computing resources in clouds by applying earliest deadline first strategy.
Year
DOI
Venue
2011
10.1109/CloudCom.2011.70
Cloud Computing Technology and Science
Keywords
Field
DocType
economical energy trading,cloud provider,future job,future date,required energy,optimal number,external cloud,towards economic energy trading,job number estimate,energy trading,cloud environments,corresponding energy,estimation,kernel,regression,earliest deadline first,gaussian processes,statistical analysis,optimization,resource allocation,data models,data model,cloud computing,gaussian process
Data modeling,Service level,Computer science,Workload,Outsourcing,Real-time computing,Resource allocation,Gaussian process,Earliest deadline first scheduling,Cloud computing,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-4673-0090-2
0
0.34
References 
Authors
2
2
Name
Order
Citations
PageRank
Andreas Zinnen121512.41
Thomas Engel253859.08