Web server workload forecasting in fuzzy environment - linguistic approach

Web server workload forecasting is one of the essential considerations in Web server management and network upgrading. Due to variability of server workload distribution originated from unpredictable users' surfing behavior, the measurement of Web server performance metrics are characterized and modeled in fuzzy manner. A fuzzy inference system is formed using four Web server performance metrics and a server utilization index are derived to determine the servers' utilization states for every time period. A fuzzy Markov model is proposed to illustrate the state transitions of server resource utilization based on expert linguistic evaluation of stationary transition probability. A steady state algorithm is applied to explore the convergence of server resource utilization after n transition periods.