Today, in telecommunication networks, much shift is ongoing with the emergence of softwarization and cloudification. Among these technologies, NFV (Network Function Virtualization) is the network architecture that decouples network functions from hardware devices (middleboxes) with the help of a virtual component known as VNF (Virtual Network Function). VNF has shifted the network technological paradigm. Before: Network Function was performed by physical equipment, and service providers acquired its property for the lifetime of the relying hardware (instead counted in years). Today, Network functions are software that service providers develop or acquire purchasing licenses. A license defines software's Right to Use (RTU).
Therefore, if licensing in NFV is not appropriately managed, service providers might (1) be exposed to counterfeiting and risk heavy financial penalties due to non-compliance; (2) might overbuy licenses to cover poorly estimated usages. Thus, mastering network function license through implementing Software Asset Management and FinOps (Finance and DevOps) is essential to control costs. In this research, our primary problem is to minimize the TCO (Total Cost of Ownership) of software cost (VNF), providing Quality of Services (QoS) to a specific amount of users. Software costs include various costs, from development to maintenance, integration to release management, and professional services. Our research focuses on proprietary software (developed by a publisher and sold via a paid license). We considered that TCO consists of the software license cost, the resources necessary to execute and operate SW, and the energy consumed by this execution. In this research, first, we have identified the need for a standardized VNF licensing model, which is highly dependent on the VNF provider's creativity; This lack of standards places CSPs (Communication Service Providers) at risk of having to delegate the management of rights to suppliers. Hence, we proposed a licensing model based on the metrics, which help to quantify the usage of the VNF. After estimating the license of VNF, we estimated the license cost. Afterward, we presented several ways to minimize the license cost depending upon the different use cases, which depend on the user's scenario and needs. Then after, with the help of industrial knowledge, we found that reducing resource consumption to minimize the TCO providing QoS affects the deployment of the VNF directly or indirectly, which impacts the licensing. Thus, the licenses and resources are interdependent. We used these costs to construct the software's total cost. After that, we proposed several ways to reduce the software's total cost by fulfilling the client's requirements. Then after, we considered the energy and its associated cost of VNF. The energy consumption of the VNF is dependent on resource consumption, and resources usages impact the license. Thus, we can see that these three costs are interdependent: license, resources, and energy cost of VNF. Hence, we consider these costs and constructed TCO. Minimizing TCO fulfilling the client's requirements is challenging since it is a multi-parameter. Therefore, we proposed several heuristical algorithms based on resource sharing and consolidation to reduce the TCO depending on the license, resource preference, and the client's scenarios.
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