Paper Info
Title
Green web services: improving energy efficiency in data centers via workload predictions
Abstract
Improving energy efficiency of data centers is an important research challenge. Web services are an important part of data centers' workload, and a large contributor to their energy footprint. This paper contributes an approach that, leveraging statistical data over web services usage patterns, dynamically predicts the resources required by the web service application. Our framework, SOPRA, uses these predictions to constantly adapt the allocation of resources to minimize the energy utilization of the data center. We demonstrate the viability of our approach by executing SOPRA over a synthetic workload. We compare the energy savings achieved by SOPRA with the traditional over allocation strategy and with the saving achievable by using a static predictor. Furthermore, we show how different service level agreements (SLA) influence the ability to save energy. The results of our experiments show that, with our workload, we can save up to 52.49% of energy over the over-allocation approach while a static prediction can only achieve a 44.78% saving. Moreover, our results show that the SLA has a high impact on energy savings. Using a more demanding SLA, the energy saving SOPRA was able to achieve was only 28.29%.
Year
DOI
Venue
2013
10.1109/GREENS.2013.6606416
GREENS
Keywords
Field
DocType
data centers,proactive resource adaptation,energy efficiency,web services,service level agreements,sla,statistical analysis,predictive models,servers,resource management,green computing
Green computing,Service level,Efficient energy use,Workload,Computer science,Resource allocation,Footprint,Web service,Data center,Database
Conference
ISBN
Citations 
PageRank 
978-1-4673-6267-2
5
0.40
References 
Authors
14
5
Name
Order
Citations
PageRank
Massimiliano Menarini19710.22
Filippo Seracini2121.92
Xiang Zhang350.74
Tajana Simunic43198266.23
Ingolf Krüger576863.23