Intelligent Application Scheduling via Vector Similarity Search
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Solution Overview
Problem
Conventional distributed systems rely on heuristics or predefined rules for scheduling applications, which do not adapt well to changing workloads and application characteristics, leading to suboptimal task allocation and performance degradation.
Innovation Solution
A computer-implemented method that leverages historical application data and machine learning models to extract features, convert them into feature vectors, perform vector similarity searches, and schedule incoming applications on nodes based on matching vectors, utilizing a vector database and clustering analysis for intelligent application scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If heuristics or predefined rules are used for scheduling applications, then the scheduling process is simple and fast, but the system cannot adapt well to changing workloads and application characteristics, leading to suboptimal task allocation
Solution Approach 1:
The system performs preliminary actions by collecting historical application data and pre-training machine learning models offline before actual scheduling operations. Feature extractors are pre-configured with multiple application-specific features, and the vector database is pre-populated with historical data. This allows the system to adapt quickly to new workloads without complex real-time processing, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The patent introduces several intermediary components: feature extractors that translate diverse application characteristics into standardized feature vectors, vector databases that mediate between historical data and scheduling decisions, and machine learning models that act as intermediaries between raw data and scheduling logic. These intermediaries enable adaptability without requiring complex direct processing of all possible workload variations.
2Productivity
If machine learning models and vector similarity searches are used for intelligent scheduling, then resource utilization and system performance improve, but the computational complexity and processing time increase
Solution Approach 1:
The system extracts only the most relevant features from application data using specialized feature extractors, converting complex application characteristics into compact feature vectors. This extraction process removes unnecessary computational complexity while retaining the essential information needed for effective scheduling decisions, thereby maintaining high resource utilization without excessive computational burden.
Solution Approach 2:
The patent transforms scheduling decisions from direct complex rule-based logic into parameter-based vector similarity comparisons. By changing the parameter representation from detailed application rules to compressed feature vectors, the system achieves efficient similarity searches that improve productivity without proportional increases in computational complexity.
3Measurement precision
If multiple application-specific features are extracted and compared using vector similarity search, then scheduling accuracy improves, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary feature extraction and vector computation during application submission and historical data processing, rather than waiting until scheduling time. Feature extractors pre-compute and store feature vectors in the vector database, so that during actual scheduling, the system only needs to perform similarity searches on pre-processed data. This preliminary action maintains high scheduling accuracy while minimizing real-time processing time.
Data Source
AI summary
Embodiments receive historical application data from at least one historical application, receive incoming application data about at least one incoming application, extract a first set of features from the historical application data and a second set of features from the incoming application data using at least one machine learning model, convert the first set of features to a first set of feature vectors and the second set of features to a second set of feature vectors, perform a vector similarity search by comparing the first set of feature vectors to the second set of feature vectors, determine a matching vector based on the vector similarity search, and schedule execution of the at least one incoming application using a node based on the matching vector.