Network Planning System with Availability Guarantees
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Solution Overview
Problem
Traditional network planning tools often result in either over-designed or under-designed backbone networks due to inadequate evaluation of data communication requirements, leading to inefficient capacity allocation and potential network failures.
Innovation Solution
A network planning with guarantees system that evaluates network component, connectivity, and flow data to generate a capacity provisioning plan that ensures required availability levels by simulating various failure scenarios and verifying network models over an extensive period, balancing modeling effort and validation to provide a robust network plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network planning tools are used, then network development can be guided based on forecasted needs, but the network may be over-designed or under-designed due to inadequate evaluation of data communication requirements
Solution Approach 1:
The system performs preliminary evaluation of network requirements by simulating various failure scenarios before finalizing the network design. This allows the network plan to be optimized in advance to meet availability guarantees while avoiding both over-design and under-design issues that plague traditional planning approaches
Solution Approach 2:
The system incorporates feedback loops where network plans are evaluated against simulated failure scenarios, and the results feed back into refining the capacity provisioning plan. This iterative process ensures the final design meets reliability targets without unnecessary complexity
2Reliability
If comprehensive evaluation of data communication requirements is performed, then network availability can be guaranteed, but the planning process becomes more complex and time-consuming
Solution Approach 1:
The system focuses evaluation efforts on the most critical failure scenarios and data communication requirements rather than attempting to evaluate every possible condition. By concentrating resources on the most impactful factors, the system achieves reliable network plans without the prohibitive time cost of exhaustive analysis
Solution Approach 2:
The comprehensive evaluation process is broken down into discrete, manageable components such as individual failure scenarios, flow requirements, and capacity assessments. This segmentation allows the planning process to be executed systematically and efficiently while still achieving thorough evaluation
3Reliability
If network capacity is increased to ensure availability, then reliability improves, but unnecessary capacity is provisioned leading to inefficiency
Solution Approach 1:
The system dynamically adjusts capacity provisioning parameters based on simulated failure scenarios and actual network requirements. By optimizing these parameters through rigorous evaluation, the system ensures capacity is increased only where and when necessary to meet availability guarantees, avoiding the inefficiency of blanket capacity increases
Data Source
AI summary
A system and method for network planning with certain guarantees is disclosed. The system receives data characterizing various aspects of a backbone network, such as the nodes of the backbone network, how the nodes are connected by network links, the maximum available capacities of the network assets, network costs, and network asset reliability information. The system also receives data characterizing the requirements of different data communications, or flows, within the backbone network. For example, the backbone network may need to provide a flow a minimum amount of bandwidth or throughput, and the flow may have a minimum required uptime or availability. Based on the network data and flow data, the system generates a network plan that describes how capacity should be provided by different components of the network in a manner that guarantees satisfying flow requirements while balancing other considerations, such as network costs.