Network Capacity Engineering System for Bottleneck Resolution
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Solution Overview
Problem
Network operators face challenges in efficiently managing network capacities due to changing traffic patterns and underutilization or overutilization of network nodes and links, leading to suboptimal resource allocation and high maintenance costs.
Innovation Solution
A system comprising a processor with a data collection module, capacity analysis module, simulation module, and network management action module that collects capacity utilization data, produces actual and projected capacity utilization maps, simulates management actions, and suggests ranked recommendations for optimizing network resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If network nodes and links are expanded to handle increasing traffic demands, then network capacity is improved, but investment cost and operational complexity increase
Solution Approach 1:
The system implements automated capacity engineering that performs self-assessment, self-planning, and self-optimization of network resources. The automated system collects capacity utilization data, generates capacity maps, identifies bottlenecks, and recommends optimization actions without requiring specialized engineering staff intervention, thereby reducing operational complexity while maintaining improved network capacity
Solution Approach 2:
The system dynamically changes network operating parameters by adjusting traffic routing, load balancing configurations, and resource allocation based on real-time capacity utilization data. This allows the network to adapt to changing traffic patterns and maintain optimal performance without physical expansion, avoiding the complexity associated with adding new nodes and links
2Quantity of substance
If network nodes and links are expanded to handle increasing traffic demands, then network capacity is improved, but investment cost increases
Solution Approach 1:
The system performs preliminary capacity planning and optimization by identifying future bottlenecks before they occur. By proactively adjusting traffic routing and load balancing in advance, the system prevents capacity shortages without requiring emergency network expansion, thereby avoiding substantial investment costs while maintaining adequate network capacity
Solution Approach 2:
The system creates virtual copies of network capacity through software-defined networking and virtualization techniques. Instead of physically expanding the network infrastructure, the system uses software-based solutions to replicate and allocate network resources dynamically, significantly reducing investment costs while maintaining improved network capacity
3Adaptability or versatility
If manual monitoring and adjustment of network capacity is performed, then adaptability to traffic changes is improved, but productivity and operational efficiency deteriorate
Solution Approach 1:
The system implements automated feedback loops that continuously monitor capacity utilization data, compare actual performance against targets, and automatically adjust network configurations in response to traffic changes. This closed-loop control system maintains high adaptability to traffic patterns while eliminating manual intervention, thereby improving operational efficiency and productivity
Solution Approach 2:
The system dynamically adjusts network parameters in real-time based on changing traffic conditions. Automated algorithms continuously optimize traffic routing, load balancing, and resource allocation without manual intervention, maintaining high adaptability to traffic changes while significantly improving operational efficiency by eliminating manual monitoring and adjustment processes
4Reliability
If specialized engineering staff are dedicated to operating and maintaining network nodes, then network reliability is improved, but operational cost increases
Solution Approach 1:
The system implements automated self-diagnosis, self-monitoring, and self-optimization capabilities that maintain network reliability without requiring specialized engineering staff. The automated capacity engineering system continuously monitors network health, identifies issues, and performs optimizations, thereby maintaining high reliability while eliminating the operational costs associated with dedicated engineering teams
Solution Approach 2:
The system replaces manual mechanical processes of network monitoring and management with automated software-based solutions. Automated algorithms perform capacity planning, traffic optimization, and fault detection that previously required human engineers, maintaining network reliability while significantly reducing operational costs by eliminating the need for specialized staff
Data Source
AI summary
A system for managing network capacities is disclosed. The system comprises a processor implementing a data collection module configured to collect a plurality of capacity utilization data. The processor also implements a capacity analysis module configured to produce an actual capacity utilization map using the plurality of capacity utilization data. The capacity analysis module is also configured to produce a projected capacity utilization map using a plurality of designed node and link capacities, and a plurality of capacity engineering constraints. The processor also implements a simulation module configured to simulate a network management action and to produce simulated capacity utilization maps. The processor also implements a network management action module configured to suggest network management actions based on the actual capacity utilization map and the projected capacity utilization map and to rank the one or more network management actions based on the one or more simulated capacity utilization maps.