Multi-Objective Resource Provisioning in Network Function Virtualization Infrastructures
Network function virtualization (NFV) and software-dened networking (SDN) are two recent networking paradigms that strive to increase manageability, scalability, pro- grammability and dynamism. The former decouples network functions and hosting devices, while the latter de...
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| Format: | Others |
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Scholar Commons
2018
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| Online Access: | http://scholarcommons.usf.edu/etd/7206 http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=8403&context=etd |
| Summary: | Network function virtualization (NFV) and software-dened networking (SDN) are two recent networking paradigms that strive to increase manageability, scalability, pro- grammability and dynamism. The former decouples network functions and hosting devices, while the latter decouples the data and control planes. As more and more service providers adopt these new paradigms, there is a growing need to address multi-failure conditions, particularly those arising from large-scale disaster events. Overall, addressing the virtual network function (VNF) placement and routing problem is crucial to deploy NFV surviv- ability. In particular, many studies have inspected non-survivable VNF provisioning, however no known work have proposed survivable/resilient solutions for multi-failure scenarios. In light of the above, this work proposes and deploys a survivable multi-objective provisioning solution for NFV infrastructures. Overall, this study initially proposes multi- objective solutions to eciently solve the VNF mapping/placement and routing problem. In particular, a integer linear programming (ILP) optimization and a greedy heuristic meth- ods try to maximize the requests acceptance rate while minimizing costs and implementing trac engineering (TE) load-balancing. Next, these schemes are expanded to perform \risk- aware" virtual function mapping and trac routing in order to improve the reliability of user services. Furthermore, additionally to the ILP optimization and greedy heuristic schemes, a metaheuristic genetic algorithm (GA) is also introduced, which is more suitable for large- scale networks. Overall, these solutions are then tested in idealistic and realistic stressor scenarios in order to evaluate their performance, accuracy and reliability. |
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