Adaptive Scheduling on Power-Aware Managed Data-Centers Using Machine Learning
Energy-related costs have become one of the major economic factors in IT data-centers, and companies and the research community are currently working on new efficient power-aware resource management strategies, also known as "Green IT". Here the authors propose a framework for autonomic scheduling of tasks and web-services on cloud environments, optimizing the profit taking into account revenue for task execution minus penalties for service-level agreement violations, minus power consumption cost. The principal contribution is the combination of consolidation and virtualization technologies, mathematical optimization methods, and machine learning techniques. The data-center infrastructure, tasks to execute, and desired profit are casted as a mathematical programming model, which can then be solved in different ways to find good task scheduling.