DT-Tuning: A Method for Self-Parameterization of Metaheuristics in Cloud Resource Management

Given modern clouds' dynamic and scalable environment, cloud resource management is a complex and multifaceted task involving several challenges. In this context, self-parameterization of metaheuristics is an advanced technique that can optimize cloud resource management. Motivated by the need to tune the parameters of metaheuristics to improve their efficiency, we propose a self-parameterization method based on knowledge representation and inference that can be applied to optimize resource allocation, scalability, and load balancing in cloud environments. This method, which we call DT-Tuning, uses a Decision Tree to dynamically adjust the value ranges of the parameters of a metaheuristic. A broad set of experiments validated the functioning of DT-Tuning in the self-parameterization of the hybrid metaheuristic GRASP with Path-Relinking (GRASP+PR) in the data clustering problem. We compared DT-Tuning with manually parameterized GRASP+PR and the I/F-Race and BRKGA auto parameterization algorithms about solution quality and execution time. The results highlight the potential of DT-Tuning in improving solutions, reducing execution time, and improving the quality of clusters compared to manually parameterized metaheuristics.