VScaler:Autonomic Virtual Machine Scaling

Research output: Contribution to conferencesPaperContributedpeer-review

Contributors

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.

Details

Original languageEnglish
Pages212-219
Number of pages8
Publication statusPublished - 2013
Peer-reviewedYes

External IDs

Scopus 84897723736

Keywords

Research priority areas of TU Dresden

DFG Classification of Subject Areas according to Review Boards

Keywords

  • Resourche Management, learning (artificial intelligence), Random access memory, Cloud computing, Adaption models, Prediction algorithms, scalability, performance, measurement