System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
The Vision of Autonomic Computing
Computer
ControlWare: A Middleware Architecture for Feedback Control of Software Performance
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Feedback Control of Computing Systems
Feedback Control of Computing Systems
Research challenges of autonomic computing
Proceedings of the 27th international conference on Software engineering
Automated and Adaptive Threshold Setting: Enabling Technology for Autonomy and Self-Management
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Towards Self-Configuring Hardware for Distributed Computer Systems
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Feedback Control Architecture and Design Methodology for Service Delay Guarantees in Web Servers
IEEE Transactions on Parallel and Distributed Systems
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
IEEE Internet Computing
Feedback Control-Based QoS Guarantees in Web Application Servers
HPCC '08 Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications
The PARSEC benchmark suite: characterization and architectural implications
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Proceedings of the 41st annual IEEE/ACM International Symposium on Microarchitecture
Applying genetic algorithms to decision making in autonomic computing systems
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Review: Intrusion detection by machine learning: A review
Expert Systems with Applications: An International Journal
ACM SIGMETRICS Performance Evaluation Review
Why feedback implementations fail: the importance of systematic testing
Proceedings of the Fifth International Workshop on Feedback Control Implementation and Design in Computing Systems and Networks
Proceedings of the 7th international conference on Autonomic computing
Statistical machine learning makes automatic control practical for internet datacenters
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
Decision making in autonomic computing systems: comparison of approaches and techniques
Proceedings of the 8th ACM international conference on Autonomic computing
Benchmarking modern multiprocessors
Benchmarking modern multiprocessors
Language and compiler support for auto-tuning variable-accuracy algorithms
CGO '11 Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization
Proceedings of the first ACM workshop on Optimization techniques for resources management in clouds
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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Autonomic computing systems are capable of adapting their behavior and resources thousands of times a second to automatically decide the best way to accomplish a given goal despite changing environmental conditions and demands. Different decision mechanisms are considered in the literature, but in the vast majority of the cases a single technique is applied to a given instance of the problem. This article proposes a comparison of some state of the art approaches for decision making, applied to a self-optimizing autonomic system that allocates resources to a software application. A variety of decision mechanisms, from heuristics to control-theory and machine learning, are investigated. The results obtained with these solutions are compared by means of case studies using standard benchmarks. Our results indicate that the most suitable decision mechanism can vary depending on the specific test case but adaptive and model predictive control systems tend to produce good performance and may work best in a priori unknown situations.