PREDICTION ACCURACY STUDY OF AN ADAPTIVE ALGORITHM IN CLUSTER-BASED NETWORK SERVERS WITH RESOURCE ALLOCATION

Prediction is one of the most important factors that prevents a Web system from collapsing when high intensity of transactional traffic arrives to it. It is necessary to ensure the quality of prediction is good enough in order to maintain under control the correct utilization of the Web system by allocating its resources among different types of incoming traffic that can reach the system and by controlling bursty transaction arrivals to the system. We have developed an algorithm that resides in a Web switch and distributes the workload among a set of servers following a resource allocation policy based on quality of service (QoS) attributes. The algorithm includes a reward scheme to control the accuracy of the predictions on real time.