Dynamic Network Device Capacity Assessment Using Feature-Specific Resource Data
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Solution Overview
Problem
Colocation facility providers face challenges in accurately assessing network device resource capacity, leading to either over-deployment of resources or under-capacity, due to conventional methods based on static data points like available ports and forwarding bandwidth, which can result in waste or network failure.
Innovation Solution
A dynamic capacity assessment method using a multi-dimensional capacity model that combines feature usage data and resource utilization data for network devices, allowing for real-time evaluation of resource availability and demand, enabling precise determination of necessary resource allocations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional capacity assessment methods based on static data points (available ports and forwarding bandwidth) are used, then the assessment process is simple and quick, but the accuracy of capacity assessment deteriorates leading to over-deployment or under-capacity
Solution Approach 1:
The patent segments the capacity assessment into multiple dimensions by evaluating individual network device features (routing tables, ACLs, NAT sessions, VLANs, etc.) separately using vendor-provided resource utilization data, then combines these segmented assessments to form a comprehensive multi-dimensional capacity model. This segmentation approach improves measurement precision by considering each feature's specific resource consumption patterns while maintaining manageable complexity through systematic organization.
Solution Approach 2:
The patent transitions from conventional two-dimensional assessment (ports and bandwidth) to multi-dimensional assessment by incorporating numerous additional network device features as separate assessment dimensions. Each dimension represents a specific feature's resource utilization characteristics, enabling comprehensive capacity evaluation that accurately reflects the complex, multi-faceted nature of network device resource consumption.
2Reliability
If conservative estimate of resource consumption is made, then network device reliability is improved by avoiding overutilization, but network resource waste increases due to unnecessary deployment of additional devices
Solution Approach 1:
The patent changes the assessment parameters from static, conservative assumptions to dynamic, feature-specific resource utilization data provided by network device vendors. By using actual measured data for each network feature (routing tables, ACLs, NAT sessions, etc.), the system determines accurate capacity thresholds that maintain reliability without requiring excessive conservative margins, thereby preventing unnecessary resource deployment.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring actual network device feature usage and comparing it against capacity thresholds derived from vendor-provided resource utilization data. This feedback loop enables dynamic capacity assessment that adjusts to actual usage patterns, ensuring reliability is maintained while avoiding over-provisioning by basing decisions on real-world performance data rather than static conservative estimates.
3Productivity
If aggressive estimate of resource consumption is made, then network resource utilization is maximized, but network device reliability deteriorates by pushing utilization close to scale limits
Solution Approach 1:
The patent changes capacity assessment from static aggressive estimates to dynamic evaluations based on vendor-provided resource utilization data for each network feature. By incorporating actual measured consumption patterns for routing tables, ACLs, NAT sessions, and other features, the system determines scientifically-based capacity thresholds that maximize resource utilization while maintaining reliability through data-driven safety margins.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor actual network device feature usage and compare it against capacity thresholds derived from vendor-provided data. This feedback enables the system to maximize resource utilization by operating close to true capacity limits while maintaining reliability through real-time monitoring and alerting, replacing aggressive estimates with evidence-based utilization management.
4Measurement precision
If multi-dimensional capacity model with feature-specific resource utilization data is implemented, then capacity assessment accuracy is improved, but data collection and processing complexity increases
Solution Approach 1:
The patent introduces network device vendors as intermediaries who provide pre-measured resource utilization data for various network features. These vendors act as mediators between the complex task of measuring feature-specific resource consumption and the capacity assessment system, supplying standardized, vendor-validated data that improves measurement precision while reducing the burden on colocation facility providers to collect and measure this complex data themselves.
Solution Approach 2:
The patent applies preliminary action by having network device vendors pre-calculate and provide resource utilization data for various network features before deployment. This preliminary measurement and organization of feature-specific consumption data eliminates the need for colocation facility providers to perform complex real-time measurements, significantly reducing data collection difficulty while maintaining high assessment accuracy.
Data Source
AI summary
In some examples, a computing device comprises at least one computer processor; and a memory comprising instructions that when executed by the at least one computer processor cause the at least one computer processor to: receive feature usage data indicating respective usages of a plurality of network device features configured for the network device; receive resource utilization data indicating resource utilization of the network device resource by each of the network device features at different usages; determine, based on the features usage data and the resource utilization data, respective resource utilizations of the network device resource by the plurality of network device features; combine the respective resource utilizations of the network device resource to determine a total network device resource utilization for the network device resource; and output an indication of the total network device resource utilization for the network device resource.