Workload Step Reallocation Using ML Influence Factors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to efficiently allocate system resources for processing database workloads, leading to inefficiencies and suboptimal use of resources.
Innovation Solution
A computer-implemented method that involves receiving a workload, collecting historical and real-time data, generating a feature map, dividing the workload into steps, assigning steps to groups, calculating system resources, training machine learning models to determine influence factors, and reallocating workload steps to processing nodes based on calculated resources and influence factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource allocation methods are used for database workloads, then system simplicity is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The workload is divided into multiple workload steps, and the system is segmented into processing nodes with specialized functions. Each node can be independently managed and optimized, allowing efficient resource allocation without requiring complete system redesign.
Solution Approach 2:
The system performs preliminary analysis by generating feature maps from historical and real-time data before actual workload execution. Machine learning models are trained in advance to predict resource requirements, enabling proactive resource allocation decisions that improve efficiency without adding operational complexity.
2Measurement precision
If machine learning models are trained to determine influence factors for resource allocation, then resource allocation accuracy is improved, but computational complexity increases
Solution Approach 1:
Machine learning models are trained in advance using historical workload data and system performance metrics. This preliminary training phase allows the models to learn complex patterns and relationships, so that during actual operation, resource allocation decisions can be made quickly and accurately without real-time computational burden.
Solution Approach 2:
The system creates feature maps that are simplified representations or copies of the actual workload characteristics. These feature maps serve as inputs to machine learning models, allowing complex resource allocation decisions to be based on processed, standardized data structures rather than raw, unprocessed information.
3Adaptability or versatility
If workload steps are reallocated dynamically based on influence factors, then system adaptability is improved, but control complexity increases
Solution Approach 1:
The system continuously collects real-time data on workload performance and system resource usage, feeding this information back to the resource allocation mechanism. Influence factors are dynamically adjusted based on this feedback, allowing the system to adapt to changing conditions automatically without requiring complex manual control mechanisms.
Solution Approach 2:
The resource allocation system operates autonomously by automatically analyzing workload characteristics, determining influence factors, and reallocating workload steps to appropriate processing nodes without human intervention. This self-service capability improves adaptability while avoiding the complexity of manual control systems.
4Productivity
If historical data and real-time data are collected and analyzed, then resource allocation decisions are improved, but data processing time increases
Solution Approach 1:
Historical workload data and system performance data are collected and analyzed in advance to train machine learning models and establish baseline metrics. This preliminary data processing creates reusable knowledge structures that enable rapid real-time decision-making without requiring complete re-analysis of all historical data during each allocation cycle.
Solution Approach 2:
The system extracts only the most relevant features and characteristics from large volumes of historical and real-time data to create condensed feature maps. By taking out only the essential information needed for resource allocation decisions, the system maintains high allocation quality while reducing data processing time and computational overhead.
Data Source
AI summary
A computer-implemented method for receiving, by a processor set, a workload for processing. The processor set may further collect historical data and real-time data related to processing the received workload and generate a feature map by investigating the received workload, the historical data, and the real-time data. The processor set may further divide the workload into workload steps and assign each workload step to one of a plurality of groups. System resources may also be calculated for each of the workload steps. The processor set trains a machine learning model to determine influence factors for each of the plurality of groups, and then, the processor set reallocates the workload steps based on the calculated system resources and the influence factors.