Network Capacity Planning via Failure Scenario Modeling
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Solution Overview
Problem
Current methods for managing network capacity in data centers often lead to inefficiencies due to long lead times and significant capital investments, resulting in overbuilding or underestimation of network equipment needs, which can cause inefficiencies and inefficiencies in capacity planning.
Innovation Solution
A network capacity management system that models network data link loads and failure scenarios using point-to-point network paths, generating estimates and processing results to facilitate accurate capacity planning and contingency planning, allowing for more precise capacity projections and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If advanced investments in network equipment are made, then network capacity is improved, but capital investment increases and lead time increases
Solution Approach 1:
The system performs preliminary capacity planning by modeling various failure scenarios and calculating required network capacity in advance. This allows organizations to proactively allocate resources based on predicted needs rather than reacting to actual failures, optimizing capital investment timing and amount.
Solution Approach 2:
The system uses actual network performance data and failure history as feedback to continuously refine capacity models and predictions. This feedback loop enables more accurate forecasting of future capacity needs, reducing both overinvestment and underinvestment in network equipment.
2Quantity of substance
If manual estimation of network capacity is used, then capital investment is reduced, but measurement precision deteriorates
Solution Approach 1:
The system introduces computational models and simulation tools as intermediaries between manual estimation and actual network performance. These models process historical data, failure scenarios, and traffic patterns to generate more accurate capacity predictions than pure manual methods, reducing both investment and improving precision.
Solution Approach 2:
The system replaces manual capacity estimation processes with automated computational modeling. This substitution eliminates human error and bias in predictions, providing more precise capacity forecasts without requiring proportionally higher investment, as the modeling uses existing network data.
3Reliability
If overbuilding of network capacity is done, then reliability is improved, but loss of substance increases
Solution Approach 1:
The system implements dynamic capacity planning that adapts to changing network conditions, failure patterns, and traffic demands. Rather than static overbuilding, the model continuously adjusts capacity recommendations based on actual performance data, ensuring reliability while minimizing resource waste from excessive provisioning.
Solution Approach 2:
The system changes key parameters in capacity planning by incorporating failure scenario probabilities, traffic growth rates, and equipment lifecycle data into the models. This parameter-driven approach replaces conservative overbuilding with precision planning, maintaining reliability while reducing resource inefficiency through optimized capacity allocation.
Data Source
AI summary
Systems, methods and interfaces are provided for the modeling of network data capacity for a network corresponding to a set of nodes interconnected via point-to-point network paths. A network capacity processing system obtains demand estimates for the nodes and network paths of the network. The network capacity processing system then identifies a set of failure scenarios for the network nodes and network paths. The network capacity processing system then generates of a set of processing results corresponding to load estimates for the network paths of the network and based on applying the set of failure scenarios to the model of network data capacity. Utilizing data capacity models, failure scenarios and set of processing results, the network capacity processing system can provide for network capacity planning or contingency planning.