Towards Autonomic Computing: Adaptive Job Routing and Scheduling

Abstract

Computer systems are rapidly becoming so complex that maintaining them with human support staffs will be pro-hibitively expensive and inefficient. In response, visionar-ies have begun proposing that computer systems be imbued with the ability to configure themselves, diagnose failures, and ultimately repair themselves in response to these fail-ures. 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 ver-ify 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.