Intelligent Capacity Planning for High Load Variance
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Solution Overview
Problem
Network capacity planning is challenging due to high load variance, making it difficult to determine appropriate resource allocation and prevent chokepoints, especially during peak hours when resources are at a premium.
Innovation Solution
A system utilizing a resource interception subsystem, machine learning (ML) subsystem, and reporting subsystem to dynamically assess current and future network resource capacity, intercept requests during peak times, and generate dashboard reports to guide users in scheduling requests during lower load periods, thereby preventing chokepoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network resources are allocated to handle peak load requests, then service availability is improved, but resource utilization efficiency deteriorates due to high load variance and idle capacity during low load periods
Solution Approach 1:
The system dynamically adjusts resource allocation based on predicted load capacity rather than maintaining static over-provisioning. The ML subsystem continuously forecasts network resource capacity, and the resource interception subsystem adapts request handling in real-time, allowing resources to be optimized for both peak availability and off-peak efficiency.
Solution Approach 2:
The system performs preliminary capacity planning by predicting future network resource capacity before requests arrive. The ML subsystem forecasts capacity in advance, and users are notified of predicted availability windows, allowing requests to be scheduled proactively during optimal times rather than reactively during peak loads.
2Reliability
If network resources are over-provisioned to handle peak demand, then service reliability is improved, but infrastructure cost increases
Solution Approach 1:
The system changes the operational parameters of network resources dynamically based on predicted capacity and actual demand. Rather than maintaining fixed resource levels, the system adjusts resource allocation parameters in response to ML predictions and observed load patterns, achieving reliable service with optimized resource quantities.
3Speed
If requests are processed during peak load times, then user access speed is improved, but network performance deteriorates due to chokepoints
Solution Approach 1:
The system implements feedback loops where the ML subsystem continuously monitors actual network capacity and compares it against predictions. The resource interception subsystem uses this feedback to adjust request handling strategies, notifying users of optimal access times based on real-time capacity assessment and historical patterns.
Solution Approach 2:
The system performs preliminary assessment of network capacity before users attempt access. By predicting capacity windows in advance and notifying users of optimal access times, the system enables users to access at appropriate moments without experiencing congestion, achieving both speed and reliability.
Data Source
AI summary
Systems, computer program products, and methods are described herein for intelligent capacity planning for resources with high load variance. The present invention is configured to receive, from a user input device, an input to process a request at a first time; determine network resources required to process the request; determine a current capacity of the network resources at the first time; retrieve, from an internal repository, a first predefined threshold associated with the network resources, wherein the first predefined threshold is associated with the current capacity; retrieve, from the request, a resource requirement associated with processing the request; determine that the resource requirement is greater than the first predefined threshold; and in response, generate a dashboard report, wherein the dashboard report indicates that the network resources are at a peak load capacity at the first time; and display the dashboard report to the user input device.