Software Component Graph Forecasting Resource Requirements
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Solution Overview
Problem
Current technologies face challenges in accurately forecasting resource requirements for software application components, especially when dealing with a large number of transactions exceeding historical data, leading to potential resource shortages and inefficiencies in computing infrastructures.
Innovation Solution
A system constructs a component graph to estimate workloads and resource requirements by traversing the graph, using machine learning and deep learning models, including Generative Additive Models and Long Short-Term Memory models, to predict resource usage metrics for future durations, enabling effective resource allocation and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional forecasting methods are used for resource requirements, then the system works with available historical data, but the forecasting accuracy deteriorates when transaction volumes significantly exceed historical data ranges
Solution Approach 1:
The patent segments the forecasting problem by constructing a component graph that breaks down the software application into individual components and their invocation relationships. This segmentation allows the system to analyze workload distribution across components separately, enabling accurate forecasting even when total transaction volumes exceed historical data ranges, as each component's resource requirements can be independently predicted based on its specific invocation patterns
Solution Approach 2:
The patent introduces a new dimension by incorporating component invocation graphs and workload distribution metrics alongside traditional time-series data. This multi-dimensional approach adds structural information about component relationships and invocation patterns, allowing the forecasting system to accurately predict resource requirements for components even when overall transaction volumes significantly exceed historical ranges
2Productivity
If resource allocation is based on historical data only, then the allocation process is simple, but resource shortages occur when transaction volumes exceed historical ranges
Solution Approach 1:
The patent performs preliminary actions by constructing component graphs and analyzing workload distributions before the actual forecasting process. This preparatory work establishes the structural relationships and invocation patterns that enable accurate resource requirement predictions even for future transaction volumes exceeding historical data, ensuring adequate resource allocation is planned in advance
Solution Approach 2:
The patent changes the parameters used for forecasting by incorporating component-specific workload metrics, invocation frequencies, and graph-based structural parameters alongside traditional time-series data. This parameter expansion allows the system to accurately predict resource requirements for components under varying transaction volumes, improving both resource utilization efficiency and allocation adequacy
3Measurement precision
If detailed component-level analysis is performed, then forecasting precision for individual components improves, but the system complexity increases
Solution Approach 1:
The patent applies universality by using a unified component graph framework that serves multiple functions: representing component relationships, tracking workload distributions, and enabling forecasting predictions. This multi-functional approach allows detailed component-level analysis to improve forecasting precision without proportionally increasing system complexity, as the same structural representation supports multiple analytical purposes
Data Source
AI summary
An aspect of the present disclosure is directed to forecasting resource requirements for components of software applications. In one embodiment, a system constructs a component graph of components deployed in a computing environment, the component graph indicating for each component, a corresponding subset of components that are invoked by the component and a corresponding distribution of component workloads received at the component to the subset of components. Upon receiving data indicating an entry workload expected to be received in a future duration at one or more entry components, the system estimates by traversing the component graph, a component workload, corresponding to the entry workload, expected to be received in the future duration at a first component and determines resource requirements for the first component based on the estimated component workload.