Network Capacity Forecasting System for Dynamic Resource Adjustment
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Solution Overview
Problem
Managers of communications networks face challenges in predicting and managing network capacity to meet changing user demands, balancing response times, and avoiding over-provisioning, which leads to resource waste and increased costs.
Innovation Solution
A system and method that forecast network capacity requirements by evaluating present capacity, user demand, and utilization, allowing for timely adjustments to increase or decrease network capacity to maintain optimal utilization between minimum and maximum acceptable levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If network capacity is increased to achieve faster application response time, then user response time is improved, but network resources are wasted and costs increase due to over-provisioning
Solution Approach 1:
The system performs preliminary forecasting of network capacity requirements by analyzing historical data and predicting future demand patterns. This allows network capacity to be adjusted proactively before actual demand changes occur, rather than reactively increasing capacity in advance, thereby avoiding over-provisioning while ensuring adequate response time when needed.
Solution Approach 2:
The system continuously monitors actual network utilization and compares it with forecasted demand, then uses this feedback to dynamically adjust network capacity. This closed-loop control ensures capacity matches actual needs, preventing both over-provisioning (wasted capital) and under-provisioning (poor response time).
2Reliability
If network capacity is increased to meet future user demand, then network adequacy is improved, but costs increase due to premature expansion
Solution Approach 1:
The system performs preliminary forecasting of network capacity requirements by analyzing historical data and predicting future demand patterns. This allows network capacity to be adjusted proactively before actual demand changes occur, rather than reactively increasing capacity in advance, thereby avoiding over-provisioning while ensuring adequate response time when needed.
Solution Approach 2:
The system dynamically changes network capacity parameters based on forecasted demand and actual utilization patterns. Rather than maintaining fixed or uniformly increased capacity, the system adjusts capacity parameters (bandwidth, routing, resource allocation) to match predicted needs, ensuring network adequacy without unnecessary capacity expansion.
3Loss of energy
If network capacity is reduced to avoid wasting working capital, then costs are decreased, but application response time deteriorates
Solution Approach 1:
The system continuously monitors actual network utilization and compares it with forecasted demand, then uses this feedback to dynamically adjust network capacity. This closed-loop control ensures capacity matches actual needs, preventing both over-provisioning (wasted capital) and under-provisioning (poor response time).
Solution Approach 2:
The system makes network capacity dynamic rather than static, allowing capacity to be increased or decreased based on real-time conditions and forecasts. This dynamic adjustment ensures adequate response time during high-demand periods while reducing capacity during low-demand periods to avoid wasting working capital.
4Reliability
If network capacity is expanded in advance to accommodate future demand, then future network adequacy is ensured, but costs increase due to premature provisioning
Solution Approach 1:
The system performs preliminary forecasting of network capacity requirements by analyzing historical data and predicting future demand patterns. This allows network capacity to be adjusted proactively before actual demand changes occur, rather than reactively increasing capacity in advance, thereby avoiding over-provisioning while ensuring adequate response time when needed.
Solution Approach 2:
The forecasting system automatically identifies when capacity changes are needed and triggers appropriate provisioning actions without requiring manual intervention or conservative over-provisioning. The system serves itself by continuously monitoring, predicting, and initiating capacity adjustments only when forecasted demand warrants them, eliminating the need for premature provisioning.
Data Source
AI summary
A system and method for maintaining capacity of a network. Instructions are adapted to define future times at which a capacity of the network is evaluated. In addition, instructions are adapted to determine a total capacity of the network (TNC) and a total demand of users (TUD) for the network at each of the future times. As a function of the total capacity of the network (TNC) and the total demand of users (TUD), instructions are adapted to determine a predicted utilization (PU) of the network at each of the future times. By comparing the predicted utilization (PU) to a defined acceptable utilization of the network at each of the future times, instructions are adapted to determine a change in network capacity (DCNC) to be applied to the network at each of the future times in order to increase or decrease the capacity of the network.