Title : 
VScaler: Autonomic Virtual Machine Scaling
         
        
            Author : 
Yazdanov, Lenar ; Fetzer, Christof
         
        
            Author_Institution : 
Fac. of Comput. Sci., Tech. Univ. Dresden, Dresden, Germany
         
        
        
            fDate : 
June 28 2013-July 3 2013
         
        
        
        
            Abstract : 
Recent research results in cloud community found that cloud users increasingly force providers to shift from fixed bundle instance types(e.g. Amazon instances) to flexible bundles and shrinked billing cycles. This means that cloud applications can dynamically provision the used amount of resources in a more fine-grained fashion. This observation calls for approaches which are able to automatically implement fine granular VM resource allocation with respect to user-provided SLAs. In this work we propose VScaler, a framework which implements autonomic resource allocation using a novel approach to reinforcement learning.
         
        
            Keywords : 
cloud computing; contracts; learning (artificial intelligence); resource allocation; user interfaces; virtual machines; VScaler; autonomic virtual machine scaling; billing cycles; cloud community; fine-grained fashion; reinforcement learning; resource allocation; user-provided SLA; Adaptation models; Cloud computing; History; Learning (artificial intelligence); Prediction algorithms; Random access memory; Resource management; measurement; performance; scalability;
         
        
        
        
            Conference_Titel : 
Cloud Computing (CLOUD), 2013 IEEE Sixth International Conference on
         
        
            Conference_Location : 
Santa Clara, CA
         
        
            Print_ISBN : 
978-0-7695-5028-2
         
        
        
            DOI : 
10.1109/CLOUD.2013.142