Energy-efficient auto-scaling of virtualized network function instances based on resource execution pattern. (December 2020) - Mehmood, Asif; Muhammad, Afaq; Ahmed Khan, Talha; Diaz Rivera, Javier Jose; Iqbal, Javed; Ul Islam, Ihtesham; Song, Wang-Cheol
Abstract: To scale virtualized network functions (VNF), information about computing resource utilization plays an important role. With the usage of both virtualized and physical resources, introduced new challenges that need to be solved by virtual network functions manager (VNFM), which are responsible for managing the lifecycle of VNFs. These challenges demand an auto-scaling mechanism that relies on proper factors. The proposed system considers the execution time, thresholds of a VNF, and weight factor reflecting virtualization overhead as the practical factors. An auto-scaling application, along with the monitoring application, is developed. These two applications communicate with each other via application programming interfaces (API) to generate the auto-scaling configurations of resources. In the light of mentioned factors, mechanism, and later provided results, the proposed system proves to be useful in solving the challenges related to energy and allocation of resources for both virtual and physical infrastructures. Graphical abstract: Highlights: Current auto-scaling of compute resources are not energy efficient in MANO. Context aware virtualized network function scaling is required in SDN/NFV environment. Resource allocation needs to be fair whether it be a compute or network resource. Trade off needed between service quality and resource usage optimization. Responsiveness maintained while designing the system components via REST interfaces.