Predicting Behavior Patterns Using Adaptive Workload Models

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 dynamics in workload 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. User is allowed to define relevant information for better management as substrings. The adaptive workload model, called the SSR1 model, 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 SSR model through web traffic generation and ATM bandwidth management. We use genetic algorithms as the vehicle to address the learning aspects of the model.