Virtual Network Control via Two-Stage Robust Optimization
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Solution Overview
Problem
Existing methods for virtual network function (VNF) allocation and path determination in network function virtualization (NFV) environments do not adequately consider uncertainties in traffic volume and renewable energy, leading to potential communication performance deterioration, congestion, increased costs, and environmental impact due to insufficient power utilization.
Innovation Solution
A control apparatus that calculates an optimal solution for VNF allocation and path determination using prediction values for traffic volume and renewable energy, employing a two-stage robust optimization problem to embed virtual networks within physical networks, accounting for uncertainties in traffic volume and renewable energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If VNF allocation and path determination are performed without considering traffic volume uncertainty, then calculation complexity is reduced, but communication performance deteriorates and congestion occurs
Solution Approach 1:
The patent applies preliminary action by acquiring prediction values for traffic volumes before performing VNF allocation and path determination. The prediction value acquisition unit obtains predicted traffic data in advance, which is then used as input for the optimization calculation. This allows the system to prepare for uncertain future conditions proactively, improving communication performance while maintaining manageable calculation complexity through structured prediction models.
2Reliability
If renewable energy usage is not considered in VNF allocation, then power supply stability is improved, but environmental load increases and costs rise
Solution Approach 1:
The patent applies parameter changes by incorporating renewable energy prediction values as dynamic parameters in the two-stage robust optimization problem. The objective function includes terms that minimize power costs and maximize renewable energy utilization, allowing the system to adapt VNF allocation decisions based on predicted renewable energy availability. This balances power supply stability with environmental considerations through mathematical optimization.
3Reliability
If robust optimization considering both traffic volume uncertainty and renewable energy uncertainty is performed, then virtual network control robustness is improved, but calculation complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the robust optimization problem into two distinct stages: (1) VNF allocation and path determination based on prediction values, and (2) traffic engineering and power consumption optimization. This two-stage structure breaks down the complex simultaneous optimization into manageable sequential steps, reducing calculation complexity while maintaining robustness against uncertainties in both traffic volume and renewable energy availability.
Data Source
AI summary
A control apparatus for embedding, in a physical network, a virtual network that implements provision of a service is provided. The control apparatus includes a memory; and a processor configured to acquire a prediction value of a traffic volume of the service and a prediction value of electric power including renewable energy that is usable by each physical node that constitutes the physical network; acquire information on the physical network; calculate an optimal solution of a two-stage robust optimization problem related to allocation of virtual nodes constituting the virtual network to physical nodes and path determination between the virtual nodes, based on the prediction value of the traffic volume, the prediction value of the electric power, and the information on the physical network; and control the virtual network embedded in the physical network, based on the allocation of the virtual nodes and the path determination represented by the optimal solution.