Paper Info
Title
Adaptive Service Performance Control Using Cooperative Fuzzy Reinforcement Learning In Virtualized Environments
Abstract
Designing efficient control mechanisms to meet strict performance requirements with respect to changing workload demands without sacrificing resource efficiency remains a challenge in cloud infrastructures. 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.
Year
DOI
Venue
2017
10.1145/3147213.3147225
PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC' 17)
Keywords
Field
DocType
Performance control, Resource allocation, Quality of service, Reinforcement learning, Autoscaling, Autonomic computing
Autonomic computing,Computer science,Fuzzy logic,Quality of service,Provisioning,Resource allocation,Autoscaling,Distributed computing,Reinforcement learning,Cloud computing
Conference
Citations 
PageRank 
References 
2
0.44
13
Authors
4
Name
Order
Citations
PageRank
Olumuyiwa Ibidunmoye1382.66
Mahshid Helali Moghadam264.25
Ewnetu Bayuh Lakew3818.83
Erik Elmroth41675149.84