Designing efficient control mechanisms to meet strict performance requirements with respect tochanging workload demands without sacrificing resource efficiency remains a challenge in cloudinfrastructures. A popular approach is fine-grained resource provisioning via auto-scaling mechanisms that rely on either threshold-based adaptation rules or sophisticated queuing/control-theoretic models. While it is difficult at design time to specify optimal threshold rules, it is even more challenging inferring precise performance models for the multitude of services. Recently, reinforcement learning have been applied to address this challenge. However, such approaches require many learning trials to stabilize at the beginning and when operational conditions vary thereby limiting their application under dynamic workloads. To this end, we extend the standard reinforcement learning approach in two ways: a) we formulate the system state as a fuzzy space and b) exploit a set of cooperative agents to explore multiple fuzzy states in parallel to speed up learning. Through multiple experiments on a real virtualized testbed, we demonstrate that our approach converges quickly, meets performance targets at high efficiency without explicit service models. ; Cloud Control
Adaptive Service Performance Control using Cooperative Fuzzy Reinforcement Learning in Virtualized Environments
2017-01-01
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC: | 629 |
Adaptive fuzzy control of satellite attitude by reinforcement learning
Tema Archiv | 1998
|Taylor & Francis Verlag | 2021
|An Integrated Cloud Service of Application Delivery in Virtualized Environments
BASE | 2016
|