Distributed Node Resource Allocation Using Predictive ML Scaling
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Solution Overview
Problem
Conventional distributed systems are slow to scale resource allotment among nodes, often relying on generic rules that are non-specific to the type of processing tasks, leading to inaccurate and reactive resource allocation, especially during sudden peaks in workload.
Innovation Solution
Utilizing a machine learning model trained on diverse data inputs to predict resource allocations that meet specific performance thresholds, enabling preemptive and accurate scaling of resources based on the characteristics of the data and workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If generic rule-based systems are used for resource allocation, then the system is simple to implement, but the resource allocation is inaccurate and reactive to performance
Solution Approach 1:
The patent replaces the mechanical rule-based resource allocation system with a machine learning-based system. The ML model processes features related to data characteristics, workflow types, and historical performance to predict optimal resource allocations, eliminating the need for rigid thresholds and generic rules while achieving accurate and proactive resource management
Solution Approach 2:
The system changes the parameters for resource allocation from static, generic thresholds to dynamic, task-specific predictions. By inputting features such as data size, complexity, workflow type, and historical performance metrics, the system generates optimized resource allocation parameters tailored to each specific task rather than applying uniform rules
2Ease of operation
If conventional systems estimate maximum resource usage based on expected input data, then the system is easy to operate, but it fails to account for unexpected samples and sudden peaks
Solution Approach 1:
The system performs preliminary resource allocation predictions before processing begins by analyzing features of incoming data and workflows. The ML model predicts required resources in advance based on data characteristics, workflow type, and historical patterns, enabling proactive resource scaling before peak loads occur rather than reacting after performance degradation starts
Solution Approach 2:
The system incorporates feedback loops where actual performance metrics and resource usage patterns are fed back into the ML model to continuously improve predictions. This feedback mechanism enables the system to learn from actual peak load experiences and refine its ability to predict and handle future peak conditions
3Device complexity
If reactive resource scaling is used, then the system is simple to implement, but performance degradation and downtime occur during peak loads
Solution Approach 1:
The system performs preliminary resource allocation predictions before processing begins by analyzing features of incoming data and workflows. The ML model predicts required resources in advance based on data characteristics, workflow type, and historical patterns, enabling proactive resource scaling before peak loads occur rather than reacting after performance degradation starts
Solution Approach 2:
The system transitions from static, fixed resource allocation to dynamic, adaptive resource scaling. The ML model continuously adjusts resource predictions based on real-time features and historical performance data, enabling flexible and responsive resource management that adapts to changing workload conditions and maintains service continuity during peak loads
Data Source
AI summary
Methods and systems for dynamically allocating resources for a distributed node network. A system may receive a workflow comprising computer program code configured to perform one or more processes of the workflow when executed. The system may generate a set of data inputs, each data input being representative of a (a) resource allocation for allocating compute resources to the one or more nodes during performance of the one or more processes and (b) sample data on which to perform the process(es). The system may determine a performance metric value for each data input by executing at least a portion of the workflow to perform the process(es) on the sample data using the specified resource allocation. Using the generated set of data inputs, a machine learning model may be trained to identify a required resource allocation for a given set of data inputs for meeting the target performance value.