Neural Network API Load Forecasting for SLA Resource Planning
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Solution Overview
Problem
Existing systems struggle to predict and manage the future load on Application Programming Interfaces (APIs) effectively, leading to inefficient resource allocation and difficulty in maintaining Service-Level Agreements (SLAs) due to varying API calls based on promotional and environmental factors.
Innovation Solution
A system and method using a neural network module to forecast API execution time by analyzing historical logs and parameters such as CPU, GPU utilization, data size, and calendar events, allowing for optimized resource allocation and SLA maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resource allocation is done statically without prediction, then system simplicity is maintained, but resource efficiency deteriorates due to inability to optimize for varying API load patterns
Solution Approach 1:
The system performs preliminary actions by predicting future API load patterns before actual requests occur. The neural network model analyzes historical data and calendar events to forecast future load, enabling proactive resource allocation rather than reactive adjustments. This allows the system to prepare resource configurations in advance, improving efficiency without significant complexity increase.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual API execution data and comparing it with predictions. Historical execution logs are fed back into the neural network model to refine future predictions. This feedback loop enables the system to learn from past performance and improve its forecasting accuracy over time, optimizing resource allocation dynamically.
2Productivity
If same resources are allocated every day, then allocation simplicity is maintained, but cost increases due to inability to optimize for varying load patterns
Solution Approach 1:
The system transitions from static to dynamic resource allocation by continuously adjusting resource assignments based on predicted load patterns. The neural network model processes varying factors such as promotional events, calendar events, and historical execution data to generate dynamic forecasts. This enables the system to allocate resources flexibly according to actual demand patterns, reducing costs while maintaining operational simplicity through automated decision-making.
3Reliability
If future load is not predicted, then system simplicity is maintained, but SLA maintenance becomes difficult due to inability to optimize execution time
Solution Approach 1:
The system performs preliminary predictions of future API load and execution time before actual requests occur. The neural network model analyzes historical execution logs and calendar events to forecast future performance metrics. This enables the system to proactively adjust resource allocation and execution parameters to ensure SLA compliance, moving from reactive to proactive SLA management.
Solution Approach 2:
The system replaces manual SLA monitoring and adjustment mechanisms with an automated neural network-based prediction system. Instead of mechanically checking SLA compliance after the fact, the system uses machine learning models to predict future performance and automatically adjust resources. This substitution of mechanical processes with intelligent prediction enables more reliable SLA maintenance with appropriate system complexity.
Data Source
AI summary
The present invention provides a robust and effective solution to an entity or an organization by enabling maximization of the utilization of machine resources by predicting future load on API based on different calendar events. The results obtained can be used by an organization for optimization and smooth allocation of the resources accordingly. The method further enables prediction of API execution time with respect to input data size and aiding in better planning of resources to keep up SLAs for APIs.