Predictive Distributed Resource Scaling via MACD Trend Analysis
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Solution Overview
Problem
Existing distributed computing systems face inefficiencies in scaling resources due to reliance on average system load, leading to unnecessary resource addition or premature removal, as the load average can be deceiving and fail to account for trends in system load.
Innovation Solution
The implementation of a predictive automatic scaling technology that considers both the current system load and a trend factor, using indicators like the Moving Average Convergence Divergence (MACD) to determine whether to add or remove resources, ensuring more intelligent and accurate scalability decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If resources are scaled based on average system load utilization, then resource allocation can be automated, but resources are unnecessarily added or prematurely deleted
Solution Approach 1:
The patent applies preliminary action by evaluating the trend factor before making resource scaling decisions. The system predicts future load conditions using historical data and trend analysis (MACD indicators) to anticipate whether resources will be needed, allowing proactive resource allocation rather than reactive responses to current load averages alone
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring system load and comparing actual performance against predicted trends. The scaling decision system uses feedback from load average measurements combined with trend factor analysis to adjust resource allocation dynamically, correcting for the deceptive nature of load average alone
2Extent of automation
If resources are scaled based on average system load utilization, then resource allocation can be automated, but resources are prematurely removed
Solution Approach 1:
The system performs preliminary trend analysis using MACD indicators and historical load data before removing resources. This allows the system to predict whether load will increase soon, preventing premature resource removal that would compromise reliability while maintaining automated decision-making
Solution Approach 2:
The feedback loop continuously monitors both current load and trend factors, providing early warning signals when load is increasing even if current average appears low. This feedback mechanism prevents premature resource removal by alerting the system to upcoming load increases before they manifest in the load average
3Measurement precision
If trend factor analysis is added to resource scaling decisions, then resource allocation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces MACD indicators as an intermediary layer between raw load average data and resource scaling decisions. These indicators simplify the complexity of trend analysis by providing standardized, easily computable signals (buy/sell/hold equivalents) that translate complex temporal patterns into actionable thresholds without requiring sophisticated algorithms
Data Source
AI summary
The described technology is generally directed towards automatically scaling distributed computing resources of a distributed computing system based on a system load measurement and a trend factor indicative of whether the system load is increasing or decreasing. If a computing resource load value is above a resource addition threshold value and the trend factor indicates that the computing resource load is increasing, a corresponding computing resource is added to the distributed computing system. If a computing resource load value is below a resource removal threshold value and the trend factor indicates that the computing resource load is decreasing, a corresponding computing resource is removed from the distributed computing system. The trend factor can be obtained using a moving average convergence divergence (MACD) direction indicator.