Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Kademlia: A Peer-to-Peer Information System Based on the XOR Metric
IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
An analytical model for multi-tier internet services and its applications
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Unity: Experiences with a Prototype Autonomic Computing System
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Dynamic Provisioning of Multi-tier Internet Applications
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Utility-Function-Driven Resource Allocation in Autonomic Systems
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Dynamic placement for clustered web applications
Proceedings of the 15th international conference on World Wide Web
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
IEEE Internet Computing
Trade-Off analysis of elasticity approaches for cloud-based business applications
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
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In this paper, we propose and implement a control mechanism that interfaces with Infrastructure as a Service (IaaS) or cloud providers to provision resources and manage instances of web applications in response to volatile and complex request patterns. We use reinforcement learning to orchestrate control actions such as provisioning servers and application placement to meet performance requirements and minimize ongoing costs. The mechanism is incorporated in a distributed, elastic hosting architecture that is evaluated using actual web applications running on resources from Amazon EC2.