Abstract
To accelerate the implementation of network functions/middle boxes and reduce the deployment cost, recently the concept of Network Function Virtualization (NFV) has emerged and became a topic of much interest attracting the attention of researchers from both industry and academia. Unlike the traditional implementation of network functions, a software-oriented approach for network functions create more flexible and dynamic network services to meet a more diversified demand. In this paper, we study the Virtual Network Function (VNF) chaining scheduling problem with limited network resources. We consider VNF transmission and processing delays, and formulate the VNFs chaining scheduling as a new Mixed Integer Linear Programming (MILP) problem. Our objective is to minimize the latency of the overall VNFs' schedule. Reducing the scheduling latency enables cloud operators to service (and admit) more customers, thereby increasing operators' revenues. Owing to the complexity of the problem, we develop a Genetic Algorithm (GA) based method for solving the problem efficiently. Finally, the effectiveness of our heuristic algorithm is verified through numerical results.
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