Virtual Network Allocation Under Traffic and Renewable Energy Uncertainty
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Solution Overview
Problem
Existing virtual network control methods fail to address uncertainties in traffic and renewable energy while satisfying power consumption commands, leading to potential oversupply, undersupply, and economic losses.
Innovation Solution
A control apparatus that calculates optimal VNF allocation and path determination using a 2-stage robust optimization method, considering uncertainties in traffic and renewable energy, to ensure power consumption compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing virtual network control methods are used to allocate VNFs and determine paths, then the allocation and routing can be optimized based on nominal traffic and power consumption values, but the system cannot satisfy power consumption commands when uncertainties in traffic and renewable energy occur
Solution Approach 1:
The system performs preliminary actions by predicting future traffic amounts and power consumption values before actual demand occurs. The control apparatus acquires prediction values of traffic amounts and power consumption, then uses these predictions to pre-determine optimal VNF allocation and path routing that can satisfy power consumption commands even when uncertainties occur, rather than reacting after the fact
Solution Approach 2:
The system changes the approach from using fixed nominal values to using prediction values that account for uncertainties. The control apparatus adjusts allocation and routing decisions based on predicted traffic amounts and power consumption values, transforming the control parameters from static to dynamic predictions that can handle variability in real-world conditions
2Productivity
If the control method prevents oversupply by assuming uncertainty in traffic and renewable energy, then the system can handle uncertainties robustly, but it cannot generate surplus electricity to meet demand response requests or market transactions
Solution Approach 1:
The system introduces dynamics by allowing the control apparatus to adjust VNF allocation and path routing based on predicted traffic amounts and power consumption values that change over time. This dynamic adjustment enables the system to generate surplus power when needed for demand response or market transactions, while still preventing oversupply when uncertainties occur, through flexible real-time control
Solution Approach 2:
The system implements feedback mechanisms where the control apparatus continuously monitors actual traffic amounts and power consumption values, compares them with prediction values, and adjusts allocation and routing decisions accordingly. This feedback loop enables the system to respond to changing conditions and generate surplus power when required while maintaining robustness against uncertainties
3Reliability
If the control method is based on nominal values without uncertainty consideration, then the control system remains simple, but it cannot satisfy power consumption commands when actual traffic or renewable energy deviates from predictions
Solution Approach 1:
The system performs preliminary predictions of traffic amounts and power consumption values before actual demand occurs. By acquiring prediction values in advance and using these to determine optimal VNF allocation and path routing beforehand, the system can satisfy power consumption commands even when actual values deviate from nominal predictions, without requiring complex real-time optimization calculations
Data Source
AI summary
A control apparatus for allocating a virtual network to a physical network is disclosed. The control apparatus includes a processor, and a memory storing instructions, which when executed, cause the processor to execute a process including performing allocation of virtual nodes configuring the virtual network to physical nodes or determination of paths between the virtual nodes, based on a prediction value of an amount of traffic of a service, a prediction value of an amount of power consumption of a physical node, information relating to the physical network, and a command value relating to power supply-demand match.