Online Algorithm For Migration Aware Virtualized Network Function Placing And Routing In Dynamic 5g Networks

The goal of Network Function Virtualization (NFV) is to build agile and service-aware networks by building a new paradigm of provisioning network services where physical network functions are deployed as Virtualized Network Functions (VNFs). Due to the advantages of NFV, 5G networks have adopted NFV as one of its key enabling technologies. One of the most important challenges for realizing NFV-enable 5G Networks is VNF placing and routing. However, most existing studies neglected flow rate fluctuation of service requests, which could cause service migration and fluctuation of queues (buffers) in the networks. In this paper, we study the cost-minimizing problem of VNF placing and routing in this dynamic environment while considering service migration and queue backlog stability. We first formulate the problem as a stochastic optimization problem. Then we propose an online algorithm, named MACRO, to solve the problem. In order to bound the worst-case delay encountered by requests we propose WEB-MACRO. MACRO and WEB-MACRO base on Lyapunov optimization and make decisions without knowing future information. The theoretical analysis suggests that MACRO and WEB-MACRO achieve an optimality gap of O(1/nu), where nu is a tunable parameter that controls the tradeoff between cost and backlogs. In addition, the queue backlogs maintained by MACRO are bounded by O(nu) and the queue backlogs maintained by WEB-MACRO are upper bounded which accordingly bounds the worst-case delay encountered by service requests. The experiment results suggest that MACRO and WEB-MACRO achieve queue stability and reduce the total cost by 36.49% when nu = 2 and 45.84% when nu = 6, compared with the state-of-the-art method.