VScaler:Autonomic Virtual Machine Scaling
Research output: Contribution to conferences › Paper › Contributed › peer-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 language | English |
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Pages | 212-219 |
Number of pages | 8 |
Publication status | Published - 2013 |
Peer-reviewed | Yes |
External IDs
Scopus | 84897723736 |
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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