LawNFO: A decision framework for optimal location-aware network function outsourcing

By decoupling the network function from the underlying dedicated hardware, enterprises can reap the benefit of outsourcing network functions to the cloud. Due to a lack of analytical tools, one cannot simply justify the worthiness of outsourcing network functions. This paper proposes a decision support framework for network function outsourcing, called Location-aware Network Function Outsourcing (LawNFO), aiming to identify which network function is worth to be outsourced to the cloud. LawNFO constructs a network-function graph and maps the cost of network function processing units and the data transmission between any network-function pairs onto the graph. The network function outsourcing problem is formulated as an optimization problem targeting on the minimum network cost. To solve the optimization problem based on the graph input, we develop an efficient and fast heuristic algorithm, called Node-weighted Contraction (NowCont), that encompasses the graph partitioning and graph augmentation techniques. This paper performs simulation to evaluate the performance of LawNFO with NowCont algorithm in two aspects: 1) the closeness ratio to the optimal solutions and 2) the network cost comparing to that of all-in-house and most-in-cloud network function placement models. The simulation results illustrate that the NowCont algorithm yields the solution closest to the optimal solution. The evaluation through two use cases show that LawNFO is of benefit to the enterprises for the decision of network function outsourcing.