INTELLIGENT ADAPTIVE MULTI-PARAMETER MIGRATION MODEL FOR LOAD BALANCING VIRTUALIZED CLUSTER OF SERVERS

Original scientific paper The most important benefit of virtualization is to get a load balanced environment through Virtual Machine (VM) migration. Performance of clustered services such as Average Response Time is reduced through intelligent VM migration decision. Migration depends on a variety of criteria like resource usage (CPU usage, RAM usage, Network Usage, etc.) and demand of machines (Physical (PM) and Virtual (VM)). This is a multi-criteria migration problem that evaluates, compares and sorts a set of PMs and VMs on the basis of parameters affected on migration process. But, which parameter(s) has dominant role over cluster performance in each time window? How can we determine weight of parameters over oncoming time slots? Current migration algorithms do not consider time-dependent variable weights of parameters. These studies assume fixed weight for each parameter over a wide range of time intervals. This approach leads to imprecise prediction of recourse demand of each server. Our paper presents a new Intelligent and Adaptive Multi Parameter migration-based resource manager (IAMP) for virtualized data centres and clusters with a novel Artificial Neural Network (ANN)-based weighting analysis named Error Number of Parameter Omission (ENPO). In each time slot, weight of parameters is recalculated and non-important ones will be attenuated in ranking process. We characterized the parameters affecting cluster performance and used hot migration with emphasis on cluster of servers in XEN virtualization platform. The experimental results based on workloads composed of real applications, indicate that IAMP management framework is feasible to improve the performance of the virtualized cluster system up to 23 % compared to current algorithms. Moreover, it reacts mor