Self-adaptive workload classification and forecasting for proactive resource provisioning-Reference-Cited by-同舟云学术

Self-adaptive workload classification and forecasting for proactive resource provisioning

Published:2014-03-10 Issue:12 Volume:26 Page:2053-2078
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat.: Pract. Exper.

Author:

Herbst Nikolas Roman¹,Huber Nikolaus¹,Kounev Samuel¹,Amrehn Erich²

Affiliation:

1. Karlsruhe Institute of Technology, Am Fasanengarten 5; 76131 Karlsruhe Germany

2. IBM Research and Development, Schoenaicher Str. 220; 71032 Boeblingen Germany

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Reference39 articles.

1. Abdelsalam HS Maly K Mukkamala R Zubair M Kaminsky D Analysis of energy efficiency in clouds Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, 2009. COMPUTATIONWORLD '09 2009 416 421

2. Designing and evaluating an energy efficient cloud;Lefvre;The Journal of Supercomputing,2010

3. Gmach D Rolia J Cherkasova L Kemper A Workload analysis and demand prediction of enterprise data center applications Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization 2007 171 180 http://dx.doi.org/10.1109/IISWC.2007.4362193

4. Hedwig M Malkowski S Bodenstein C Neumann D Towards autonomic cost-aware allocation of cloud resources Proceedings of the International Conference on Information Systems ICIS 2010 2010 http://aisel.aisnet.org/icis2010_submissions/180/

5. Bobroff N Kochut A Beaty K Dynamic placement of virtual machines for managing sla violations 10th IFIP/IEEE International Symposium on Integrated Network Management, 2007. IM '07 2007 119 128

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