VScaler:Autonomic Virtual Machine Scaling

Publikation: Beitrag zu KonferenzenPaperBeigetragenBegutachtung

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

OriginalspracheEnglisch
Seiten212-219
Seitenumfang8
PublikationsstatusVeröffentlicht - 2013
Peer-Review-StatusJa

Externe IDs

Scopus 84897723736

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