Reconfiguration Aware Orchestration for Network Function Virtualization with Time-Varied Workload in Virtualized Datacenters

Network function virtualization (NFV) is an emerging paradigm that has revolutionized the deployment and provisioning of telecommunication network services (NS). In NFV, a network service is implemented as a chain of network functions (NFs). NFs are virtualized and packaged into virtual network functions (VNF), so that they can be decoupled from the physical middleboxes and dynamically deployed on standard virtualization platforms in cloud or datacenters. Therefore, NFV has the potential to provide significant cost reductions in operating expenses (OPEX) and capital expenses (CAPEX) and increased agility for network service deployments. However, to build and manage a network function virtualization infrastructure (NFVI) is challenging. One of the key problems is to determine the mapping between underlying physical resources and VNFs in order to minimize energy consumption without violating service-level agreements (SLA). Many previous studies use embedding solutions to solve the problem by only considering the resource demand of NS and the resource capacity of physical network at a given time instance. But in practice, the resource demands of NS can vary over time, hence the embedding decision must be reconfigured accordingly. Many techniques can be used for reconfiguration, including resource scaling, instance migration and traffic re-direction. But each of these operation is associated with some costs, such as service interruptions, and additional resource or energy consumption. In this work, we investigate various reconfiguration strategies, formulate their costs by designing a model in the NFV placement problem, and propose Integer Linear Programming (ILP) and heuristic solutions, called REAP. In our evaluation, we observed 60% and 25% cost reduction for energy and reconfiguration costs, and we analyze the cost reduction from each of the reconfiguration methods to validate the design of our heuristic algorithm.