Dynamic Server Resource Scaling via ML Demand Prediction
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Solution Overview
Problem
Existing systems face challenges in predicting and efficiently allocating computing resources in server environments due to varying user demand, leading to issues such as slow task execution, processing errors, and unnecessary resource consumption.
Innovation Solution
A machine learning engine analyzes past computing resource usage to predict future demands using models like linear regression and gradient boosting machines, enabling intelligent allocation of resources based on predicted requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more computing power and resources are provided to handle peak demand, then system availability and processing speed are improved, but resource utilization efficiency deteriorates due to idling and unused capacity during low demand periods
Solution Approach 1:
The patent implements dynamic resource allocation by using machine learning models to predict future computing demands and adjusting resource provisioning in real-time. The system transitions from static over-provisioning to dynamic scaling, where computing resources are allocated based on predicted workload patterns, thereby maintaining system availability during peak demand while reducing resource idling during low demand periods.
Solution Approach 2:
The system performs preliminary analysis of historical usage data and training of machine learning models to predict future computing demands before peak loads occur. By proactively predicting demand patterns and pre-positioning appropriate resource levels, the system avoids both over-provisioning and under-provisioning, optimizing the balance between availability and efficiency.
2Device complexity
If conventional single-metric analysis is used for resource allocation, then system complexity is reduced, but prediction accuracy of future requirements deteriorates
Solution Approach 1:
The patent combines multiple metrics including historical usage data, current system state, and predictive analytics into a unified resource allocation framework. By merging these diverse data sources and analysis methods, the system achieves high prediction accuracy for future computing demands while managing complexity through integrated processing rather than separate systems.
Solution Approach 2:
The machine learning models automatically analyze historical patterns and make predictions without requiring complex manual configuration or intervention. The system self-optimizes by continuously learning from usage data and adjusting predictions, reducing operational complexity while maintaining high prediction accuracy through automated adaptive processes.
Data Source
AI summary
There are provided systems and methods for dynamic autoscaling of server resources using intelligent demand analytic systems. A service provider, such as an electronic transaction processor for digital transactions, may utilize different computing resource to provide computing resources to users. During use of such computing resources by end users and their computing devices, different demand and needs may be required by such users and devices. The service provider may utilize an intelligent machine learning system to predict computing resource needs and demands at different future time periods based on past usages over similar time periods, computing requests and demands, and network communications. The machine learning engines may identify one or more usages curves, which may be of one or more degrees of curvature, to determine potential future usage. Using these past analytics, the service provider may dynamically scale automatic provision of computing resources.