An Energy-driven Network Function Virtualization for Multi-domain\n Software Defined Networks

Network Functions Virtualization (NFV) in Software Defined Networks (SDN)\nemerged as a new technology for creating virtual instances for smooth execution\nof multiple applications. Their amalgamation provides flexible and programmable\nplatforms to utilize the network resources for providing Quality of Service\n(QoS) to various applications. In SDN-enabled NFV setups, the underlying\nnetwork services can be viewed as a series of virtual network functions (VNFs)\nand their optimal deployment on physical/virtual nodes is considered a\nchallenging task to perform. However, SDNs have evolved from single-domain to\nmulti-domain setups in the recent era. Thus, the complexity of the underlying\nVNF deployment problem in multi-domain setups has increased manifold. Moreover,\nthe energy utilization aspect is relatively unexplored with respect to an\noptimal mapping of VNFs across multiple SDN domains. Hence, in this work, the\nVNF deployment problem in multi-domain SDN setup has been addressed with a\nprimary emphasis on reducing the overall energy consumption for deploying the\nmaximum number of VNFs with guaranteed QoS. The problem in hand is initially\nformulated as a "Multi-objective Optimization Problem" based on Integer Linear\nProgramming (ILP) to obtain an optimal solution. However, the formulated ILP\nbecomes complex to solve with an increasing number of decision variables and\nconstraints with an increase in the size of the network. Thus, we leverage the\nbenefits of the popular evolutionary optimization algorithms to solve the\nproblem under consideration. In order to deduce the most appropriate\nevolutionary optimization algorithm to solve the considered problem, it is\nsubjected to different variants of evolutionary algorithms on the widely used\nMOEA framework (an open source java framework based on multi-objective\nevolutionary algorithms).\n