Predicting behavior patterns using adaptive workload models [computer networks]

Workload characteristics in a modern networking environment are very dynamic. In order to maximize performance continuously, it is natural to explore the possibility of intelligent systems which can take cognizance of the workload dynamics and adapt themselves for future control applications. In this paper, we propose a mechanism which represents the previous state of the system as a string. The user is allowed to define relevant information for better management as substrings. The adaptive workload model, which is called the SVR model (named after the first author's initials), predicts the short-term future as a string in which the information content (conveyed as a substring) reflects the future. We illustrate the applicability of the SVR model through Web traffic generation and ATM bandwidth management. We use genetic algorithms as the vehicle to address the learning aspects of the model.