Bayllocator: A Proactive System to Predict Server Utilization and Dynamically Allocate Memory Resources Using Bayesian Networks and Ballooning

With the advent of virtualization and cloud computing, virtualized systems can be found from small companies to service providers and big data centers. All of them use this technology because of the many benefits it has to offer, such as a greener ICT, cost reduction, improved profitability, uptime, flexibility in management, maintenance, disaster recovery, provisioning and more. The main reason for all of these benefits is server consolidation which can be even further improved through dynamic resource allocation techniques. Out of the resources to be allocated, memory is one of the most difficult and requires proper planning, good predictions and proactivity. Many attempts have been made to approach this problem, but most of them are using traditional statistical mathematical methods. In this paper, the application of discrete Bayesian networks is evaluated, to offer probabilistic predictions on system utilization with focus on memory. The tool Bayllocator is built to provide proactive dynamic memory allocation based on the Bayesian predictions, for a set of virtual machines running in a single hypervisor. The results show that Bayesian networks are capable of providing good predictions for system load with proper tuning, and increase performance and consolidation of a single hypervisor. The modularity of the tool gives a great freedom for experimentation and even results to deal with the reactivity of the system can be provided. A survey of the current state-of-the-art in dynamic memory allocation for virtual machines is included in order to provide an overview.