Backbone Network Capacity Planning System
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Solution Overview
Problem
Traditional long-term strategic planning for backbone networks is inefficient due to limited input based on current network demands, leading to potential failures in meeting future demands.
Innovation Solution
A network capacity planning system that evaluates supply, demand, and cost data to generate a capacity-provisioning plan, considering future projections and potential failures, to optimize capacity provisioning and minimize costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional long-term strategic planning is used, then current network demands are considered, but future network demands cannot be met
Solution Approach 1:
The system performs preliminary analysis of future network demands, supply availability, and cost projections before making capacity planning decisions. By evaluating multiple future time periods and scenarios in advance, the system prepares comprehensive capacity provisioning plans that anticipate future needs rather than reacting to current conditions only.
Solution Approach 2:
The system incorporates feedback loops that continuously evaluate network performance, demand patterns, and supply availability. The optimization process uses feedback from evaluated scenarios and failure data to refine capacity provisioning plans, ensuring that future demands are adequately anticipated and planned for.
2Reliability
If capacity is increased to meet future demands, then network reliability improves, but costs increase
Solution Approach 1:
The system changes key parameters including capacity provisioning levels, timing of capacity additions, and allocation across different network paths. By optimizing these parameters across multiple future time periods and scenarios, the system finds the most cost-effective capacity provisioning strategy that maintains adequate network reliability.
Solution Approach 2:
The system evaluates different levels of capacity provisioning, including partial provisioning in the short term with plans for additional capacity in the future. This allows the network to meet current demands while preparing for future needs, avoiding excessive capacity provisioning that would increase costs unnecessarily.
3Measurement precision
If comprehensive scenario evaluation is performed, then planning accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex planning problem into distinct components: demand projections, supply availability, cost data, and failure scenarios. Each component is evaluated separately and then integrated through optimization algorithms, making the overall system more manageable while maintaining comprehensive analysis.
Solution Approach 2:
The system uses an optimization engine as an intermediary that processes complex scenario evaluations and translates them into actionable capacity provisioning plans. This intermediary layer handles the computational complexity of evaluating multiple scenarios and failure data, presenting simplified recommendations to planners.
Data Source
AI summary
A system and method for fault-tolerant and long-term network capacity planning is disclosed. The system receives projected data, such as available network supply and network demand, characterizing a backbone network for a set of time periods. The system also receives failure data describing different failure scenarios that may occur. Based on the received network characterization data the system generates a capacity provisioning plan, describing how capacity is added to the backbone network over time, that satisfies the network demand of each time period while providing fault-tolerance under any of the failure scenarios described in the failure data. The capacity provisioning plan is also optimized, based on cost data associated with the backbone network, to minimize total costs.