Technical Note: \cal Q-Learning
Machine Learning
QualProbes: middleware QoS profiling services for configuring adaptive applications
IFIP/ACM International Conference on Distributed systems platforms
Hierarchial architecture for real-time adaptive resource management
IFIP/ACM International Conference on Distributed systems platforms
QoS Negotiation in Real-Time Systems and Its Application to Automated Flight Control
IEEE Transactions on Computers
Performance Guarantees for Web Server End-Systems: A Control-Theoretical Approach
IEEE Transactions on Parallel and Distributed Systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Vision of Autonomic Computing
Computer
Extending a Best-Effort Operating System to Provide QoS Processor Management
IWQoS '01 Proceedings of the 9th International Workshop on Quality of Service
On the Relationship between Learning Capability and the Boltzmann-Formula
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Supporting Adaptive Multimedia Applications through Open Bindings
CDS '98 Proceedings of the International Conference on Configurable Distributed Systems
Automatic Configuration and Run-time Adaptation of Distributed Applications
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
On Quality of Service Optimization with Discrete QoS Options
RTAS '99 Proceedings of the Fifth IEEE Real-Time Technology and Applications Symposium
A resource allocation model for QoS management
RTSS '97 Proceedings of the 18th IEEE Real-Time Systems Symposium
A Dynamic Quality of Service Middleware Agent for Mediating Application Resource Usage
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
Qos adaptation in real-time systems
Qos adaptation in real-time systems
Towards Autonomic Computing Middleware via Reflection
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Towards Autonomic Computing: Adaptive Network Routing and Scheduling
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Introduction to self-adaptive software: applications
IWSAS'01 Proceedings of the 2nd international conference on Self-adaptive software: applications
Modeling distributed applications for qos management
SEM'04 Proceedings of the 4th international conference on Software Engineering and Middleware
Automated Generation of Knowledge Plane Components for Multimedia Access Networks
MACE '08 Proceedings of the 3rd IEEE international workshop on Modelling Autonomic Communications Environments
Using machine learning to maintain pub/sub system QoS in dynamic environments
Proceedings of the 8th International Workshop on Adaptive and Reflective MIddleware
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
Adapting distributed real-time and embedded pub/sub middleware for cloud computing environments
Proceedings of the ACM/IFIP/USENIX 11th International Conference on Middleware
Software—Practice & Experience
Timely Autonomic Adaptation of Publish/Subscribe Middleware in Dynamic Environments
International Journal of Adaptive, Resilient and Autonomic Systems
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In any system, applications compete for a limited amount of resources. As long as there are enough resources, resource sharing based on a best effort policy is satisfactory. When resources become scarce, the system gives rise to uncontrol-lable degradations. From a global view of the system and according to the degrees of freedom of applications, Quality of Service (QoS) managers aim to adapt application behavior to deal with overload effects.This paper proposes a middleware for autonomic QoS management of a system in a dynamic environment. It associates a reinforcement learning technique with a control mechanism to improve and adapt the QoS management policy in an execution context that changes unexpectedly. The simulation of the QoS management of a set of heterogeneous applications illustrates our results.