Automated IP Address Capacity Analytics and Management
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Solution Overview
Problem
Current network IP address range allocations and de-allocations are manual, time-consuming, and involve multiple teams, resulting in lengthy wait times and inefficiencies, with the entire process taking 30-40 days and consuming over 120 business days annually.
Innovation Solution
A system that provides network IP address capacity analytics and management through a repository, interactive interface, and capacity management processor, using historical data analysis and machine learning to predict future capacity needs, automating IP subnet allocations and de-allocations, and integrating with DNS and network configuration services to streamline the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for IP address allocation and de-allocation involving multiple teams, then centralized control and coordination are maintained, but the process time increases to 30-40 days and productivity decreases
Solution Approach 1:
The system performs preliminary actions by pre-allocating and pre-configuring IP address ranges before they are actually needed. The network capacity team proactively monitors usage patterns and prepares IP address pools in advance, so when IaaS or PaaS teams need addresses, they are already available for immediate assignment, eliminating the 30-40 day wait time.
Solution Approach 2:
The system enables self-service by allowing IaaS and PaaS teams to automatically request and receive IP address allocations through integrated workflows without manual intervention from network teams. The automation platform handles the entire allocation process, configuration, and de-allocation automatically based on actual usage, freeing up network teams from routine tasks while maintaining centralized control.
2Ease of operation
If multiple teams work in silos with manual coordination, then specialized expertise is maintained in each team, but device complexity increases and ease of operation decreases
Solution Approach 1:
The system merges previously separate manual processes into a single automated platform that handles IP address allocation, configuration, monitoring, and de-allocation. By consolidating these functions into one integrated system, the complexity of coordinating between multiple teams is eliminated, and users can manage IP addresses through a single interface, dramatically improving ease of operation.
Solution Approach 2:
The automation platform acts as an intermediary between network teams and IaaS/PaaS teams, translating requirements into automated actions. It receives requests from business teams, coordinates with network capacity management, automatically allocates addresses, and handles configuration tasks, thereby simplifying the interaction between specialized teams while maintaining their expertise.
3Productivity
If manual requests and scanning are performed for each IP address allocation, then accurate tracking of available addresses is maintained, but loss of time increases and productivity decreases
Solution Approach 1:
The system maintains continuous monitoring and tracking of IP address usage across the network. Rather than periodically scanning for available addresses, the automation platform continuously updates the status of IP addresses as they are allocated, used, and released, ensuring real-time accuracy of available address information while enabling rapid response to allocation requests.
Solution Approach 2:
The system implements feedback mechanisms where the automation platform continuously receives information about IP address usage from network devices and IaaS/PaaS teams, automatically updates availability records, and provides real-time feedback on allocation status. This closed-loop feedback ensures accurate tracking without manual scanning while enabling fast processing of new requests based on current availability data.
Data Source
AI summary
An embodiment of the present invention is directed to analyzing historical network capacity allocations, using machine learning to predict future capacity needs and automating network capacity management activities such as allocations and de-allocations.