Towards autonomic computing: adaptive network routing and scheduling

Abstract

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, we define and study learning-based methods for addressing the problems of packet routing and CPU scheduling in (simulated) computer networks. 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.