Virtual Network Embedding Under Traffic and Renewable Power Uncertainty
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Solution Overview
Problem
Existing methods for VNF allocation and route determination in NFV environments do not adequately address the durability of virtual networks against sudden changes in traffic and renewable energy uncertainties, leading to potential communication performance deterioration, congestion, increased costs, and environmental impact.
Innovation Solution
A control device that performs 2-stage robust optimization to minimize link and node congestion rates by calculating optimal VNF allocation and route determination based on predicted traffic and renewable energy values, using a Column-and-constraint generation method to handle uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional VNF allocation and route determination methods are used, then the system can operate with simple control logic, but the virtual network durability against sudden changes in traffic and renewable energy uncertainty deteriorates
Solution Approach 1:
The patent applies preliminary action by performing robust optimization calculation in advance to determine VNF allocation and route determination that minimize congestion rates. The control apparatus calculates optimal allocation before actual traffic and renewable energy variations occur, preparing a resilient network configuration that can withstand uncertain future conditions. This is embodied in the solution calculation unit that computes optimal solutions based on predicted traffic and power values before real-time operations begin.
Solution Approach 2:
The patent implements feedback by continuously monitoring actual traffic amounts and renewable energy power amounts, comparing them with predicted values, and using this information to adjust and update VNF allocation and route determination. The control apparatus acquires actual values, compares them with predictions, and recalculates optimization solutions when deviations occur, creating a closed-loop control system that adapts to changing conditions while maintaining network durability.
2Reliability
If robust optimization is performed to improve durability, then the virtual network becomes resilient to uncertainties, but the calculation complexity and processing time increase
Solution Approach 1:
The patent performs robust optimization calculation in advance before real-time operations begin, using predicted traffic and renewable energy values to determine optimal VNF allocation and routing. This preliminary calculation establishes a resilient network configuration that can withstand anticipated uncertainties, eliminating the need for complex real-time optimization during actual operations and thus reducing processing time requirements.
Solution Approach 2:
The patent applies partial action by performing full robust optimization only when necessary (e.g., when actual values deviate significantly from predictions or when reconfiguration is triggered), rather than continuously recalculating. This selective optimization approach reduces unnecessary computation while maintaining network durability, balancing calculation complexity with operational needs.
3Productivity
If VNF allocation is optimized for minimum congestion, then link and node utilization improve, but the system becomes more sensitive to prediction errors in traffic and renewable energy
Solution Approach 1:
The patent implements feedback by continuously monitoring actual traffic and renewable energy values, comparing them with predicted values used in optimization calculations. When significant deviations are detected, the system recalculates optimal VNF allocation and routing to adapt to the new reality, thereby maintaining both high utilization and robustness against prediction errors through continuous adjustment.
Solution Approach 2:
The patent applies dynamics by making the VNF allocation and route determination adaptive rather than static. The control apparatus dynamically adjusts the network configuration based on the ratio between actual and predicted values, transitioning between different operational states. This dynamic approach allows the system to maintain optimal utilization while remaining robust to prediction errors through flexible reconfiguration.
Data Source
AI summary
A control apparatus according to an embodiment is a control apparatus that embeds a virtual network for implementing provision of a service on a physical network, the control apparatus including: a first acquisition unit that acquires a predicted value of an amount of traffic of the service and a predicted value of an amount of power including renewable energy usable by each of physical nodes constituting the physical network; a second acquisition unit that acquires information regarding the physical network; a solution calculation unit that calculates an optimal solution of a 2-stage robust optimization problem regarding allocation of virtual nodes constituting the virtual network to the physical nodes and route determination between the virtual nodes to minimize a link congestion rate representing a maximum value of a utilization rate of a link constituting the physical network and a node power congestion rate representing a maximum value of a power consumption rate of a node constituting the physical network on the basis of the predicted value of the amount of traffic, the predicted value of the amount of power, and the information regarding the physical network; and a control unit that controls the virtual network embedded in the physical network on the basis of the allocation of the virtual nodes and the route determination that are represented by the optimal solution.