A predictive framework for load balancing clustered web servers

Self-adaptation is the mechanism that is used automatically by some clustered web servers for resolving issues such as server bottleneck and overload. Such mechanisms are usually reactive meaning that they will be used when some issue occurs. However, taking an adaptation mechanism after arising a bottleneck or overload may be late and can cause some problems. Such problems could be resolved if we are able to predict future behavior of servers. To this end, based on our previous experiences, we presented a framework by which an adaptation strategy is selected based on a learning-based predictive method. Through the prediction, we are provided with a number of decision-making parameters for adaptation strategy selection. To show the effectiveness of our framework, we applied it for design and implementation of a differentiated cluster-based web server system and showed results. In such systems, each cluster is considered to serve a specific service. By comparison of simulation results between our predictive method and the reactive one, we found: (1) increase of the number of replied requests by servers, (2) decrease of average response time, and (3) increase of resource utilization of the system twofold to threefold.