Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Systems That Know What They're Doing
IEEE Intelligent Systems
The Vision of Autonomic Computing
Computer
A knowledge plane for the internet
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Self-Optimization module for Scheduling using Case-based Reasoning
Applied Soft Computing
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Computer systems are rapidly becoming so complex that maintaining them with human support staffs will be prohibitively expensive and inefficient. In response, visionaries have begun proposing that computer systems be imbued with the ability to configure themselves, diagnose failures, and ultimately repair themselves in response to these failures. However, despite convincing arguments that such a shift would be desirable, as of yet there has been little concrete progress made towards this goal. We view these problems as fundamentally machine learning challenges. Hence, this article presents a new network simulator designed to study the application of machine learning methods from a system-wide perspective. We also introduce learning-based methods for addressing the problems of job routing and scheduling in the networks we simulate. Our experimental results verify that methods using machine learning outperform heuristic and hand-coded approaches on an example network designed to capture many of the complexities that exist in real systems.