Offline and Real-Time Policy-based Management for Virtualized Services: Conflict and Redundancy Detection, and Automated Resolution

Network Function Virtualization (NFV) is a new technology that allows service providers to improve the cost efficiency of network service provisioning. This is accomplished by decoupling the network functions from the physical environment within which they are deployed and converting them into software components that run on top of commodity hardware. Despite its importance, NFV encounters many challenges at the placement, resource management, and adaptation levels. For example, any placement strategy must take into account the minimization of several factors, including those of hardware resource utilization, network bandwidth and latency. Moreover, Virtual Network Functions (VNFs) should continuously be adjusted to keep up with the changes that occur at both the data center and user levels. Over the past few years several efforts have been made to come up with innovative placement, resource management, and readjustment policies. However, a problem arises when these policies exhibit some conflicts and/or redundancies with one another, since the policies are proposed by multiple sources (e.g., service providers, network administrators, NFV-orchestrators and customers). This constitutes a serious problem for the network service as a whole and has several negative impacts such as Service-Level Agreement (SLA) violations and performance degradation. Besides, as conflicts may occur among a set of policies, pairwise detection will not adequate. In this paper, we tackle this problem by defining a conflict and redundancy detection and an automated resolution mechanisms to identify and solve the issues within and between NFV policies. Finally, we integrate a real-time detection component into our solution to provide continuous and comprehensive conflict and redundancy resolution, as new policies are introduced. The experimental results show that the proposed policy detection and resolution tools could rapidly identify, detect and solve conflicts and redundancies among NFV policies and extremely fast than other frameworks. Furthermore, the results show that our solution is efficient even in scenarios that consist of more than 2000 policies. Moreover, our proposed detection mechanisms can detect and solve the conflicts and redundancies for various types of policies such as placement, scaling and migration.