VScaler:Autonomic Virtual Machine Scaling
Publikation: Beitrag zu Konferenzen › Paper › Beigetragen › Begutachtung
Beitragende
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
Originalsprache | Englisch |
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Seiten | 212-219 |
Seitenumfang | 8 |
Publikationsstatus | Veröffentlicht - 2013 |
Peer-Review-Status | Ja |
Externe IDs
Scopus | 84897723736 |
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Schlagworte
Forschungsprofillinien der TU Dresden
DFG-Fachsystematik nach Fachkollegium
Schlagwörter
- Resourche Management, learning (artificial intelligence), Random access memory, Cloud computing, Adaption models, Prediction algorithms, scalability, performance, measurement