Multi-layer Resource Prediction Model for Cloud Workloads
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Solution Overview
Problem
Current resource management schemes in complex computing environments, such as cloud, local, and edge computing, face challenges in predicting resource utilization effectively, leading to inefficient resource allocation and high operational costs due to the complexity of managing different system architectures and the reactive management approach, which relies heavily on experienced IT professionals and often results in system downtime during sudden workload increases.
Innovation Solution
A method for establishing a system resource requirement prediction and resource management model through multi-layer correlations, which involves regularly collecting workloads and resource usage data, using time series and machine-learning algorithms to predict future resource needs, and dynamically adjusting resource allocation across main and sub-applications based on correlation analysis to pre-deploy resources and meet user demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If a reactive management scheme is used to handle system problems only when they occur, then operational costs are reduced in the short term, but system downtime and productivity loss increase significantly during sudden workload increases
Solution Approach 1:
The patent applies preliminary action by predicting future resource demands using time series models and machine learning algorithms before actual system failures occur. The system analyzes historical workload and resource usage data to forecast future needs, allowing proactive resource allocation and scaling decisions that prevent system downtime while avoiding unnecessary resource provisioning.
2Reliability
If experienced IT system managers are hired to implement proactive management schemes, then system reliability improves through early problem detection, but labor costs and operational complexity increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically monitor, analyze, and predict its own resource needs without human intervention. The machine learning models continuously learn from historical data and autonomously generate predictions for resource allocation, replacing the need for experienced human managers while maintaining high system reliability through automated anomaly detection and forecasting.
3Productivity
If resource allocation is adjusted dynamically based on real-time workload, then system performance improves, but computational overhead and complexity of resource management increase
Solution Approach 1:
The patent reduces computational overhead by performing resource allocation predictions in advance using historical data patterns rather than making complex real-time decisions. The time series models and machine learning algorithms pre-process and learn from historical workload patterns, enabling faster and simpler real-time resource allocation decisions based on predicted future states rather than reacting to current fluctuations.
Data Source
AI summary
A method for establishing system resource prediction and resource management model through multi-layer correlations is provided. The method builds an estimation model by analyzing the relationship between a main application workload, resource usage of the main application, and resource usage of sub-application resources and prepares in advance the specific resources to meet future requirements. This multi-layer analysis, prediction, and management method is different from the prior arts, which only focus on single-level estimation and resource deployment. The present invention can utilize more interactive relationships at different layers to effectively perform predictions, thereby achieving the advantage of reducing hidden resource management costs when operating application services.