VCONF: a reinforcement learning approach to virtual machines auto-configuration
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
CoTuner: a framework for coordinated auto-configuration of virtualized resources and appliances
Proceedings of the 7th international conference on Autonomic computing
Adapting and evaluating distributed real-time and embedded systems in dynamic environments
Proceedings of the First International Workshop on Data Dissemination for Large Scale Complex Critical Infrastructures
Journal of Network and Computer Applications
PERFUME: power and performance guarantee with fuzzy MIMO control in virtualized servers
Proceedings of the Nineteenth International Workshop on Quality of Service
Adapting distributed real-time and embedded pub/sub middleware for cloud computing environments
Proceedings of the ACM/IFIP/USENIX 11th International Conference on Middleware
URL: A unified reinforcement learning approach for autonomic cloud management
Journal of Parallel and Distributed Computing
Regression-based resource provisioning for session slowdown guarantee in multi-tier Internet servers
Journal of Parallel and Distributed Computing
AROMA: automated resource allocation and configuration of mapreduce environment in the cloud
Proceedings of the 9th international conference on Autonomic computing
Model-driven network emulation with virtual time machine
Proceedings of the Winter Simulation Conference
Interference and locality-aware task scheduling for MapReduce applications in virtual clusters
Proceedings of the 22nd international symposium on High-performance parallel and distributed computing
Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for Percentile-Based Delay Guarantee
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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In a web system, configuration is crucial to the performance and service availability. It is a challenge, not only because of the dynamics of Internet traffic, but also the dynamic virtual machine environment the system tends to be run on. In this paper, we propose a reinforcement learning approach for autonomic configuration and reconfiguration of multi-tier web systems. It is able to adapt performance parameter settings not only to the change of workload, but also to the change of virtual machine configurations. The RL approach is enhanced with an efficient initialization policy to reduce the learning time for online decision. The approach is evaluated using TPC-W benchmark on a three-tier website hosted on a Xen-based virtual machine environment. Experiment results demonstrate that the approach can autoconfigure the web system dynamically in response to the change in both workload and VM resource. It can drive the system into a near-optimal configuration setting in less than 25 trial-and-error iterations.