Workload Type Classifier for Database Resource Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current workload identification technologies are limited in their ability to accurately classify complex workloads, making it difficult for computing entities to manage resources effectively and maintain performance, especially as workload complexity increases.
Innovation Solution
A machine-implemented workload type classifier that samples various parameters, identifies significant parameters affecting response time, and classifies workloads using predefined classifications to adjust resource allocation and performance accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human detection methods are used to identify workload type, then the system is easy to operate and understand, but the detection precision deteriorates as workload complexity increases
Solution Approach 1:
The patent replaces the mechanical human detection process with an automated computational system. The workload type classifier uses machine learning algorithms to automatically analyze workload characteristics and determine workload types, eliminating the need for human administrators to manually detect and classify workloads. This substitution maintains ease of operation while significantly improving detection precision for complex workloads.
Solution Approach 2:
The patent introduces a workload type classifier as an intermediary component between the workload and the database management system. This classifier acts as a mediator that automatically analyzes workload patterns and provides classification information to the DBMS, enabling the system to adapt resource allocation without requiring direct human intervention. The intermediary handles the complexity of detection while keeping the user interface simple.
2Productivity
If the computing entity reconfigures resources based on workload classification, then the productivity improves, but the device complexity increases
Solution Approach 1:
The patent implements self-service by enabling the database management system to automatically detect workload types and reconfigure its own resources without external intervention. The workload type classifier is integrated into the DBMS, allowing it to autonomously monitor workload characteristics, classify them, and adjust resource allocation accordingly. This self-service capability improves productivity while managing complexity through automation rather than manual processes.
Solution Approach 2:
The patent establishes a feedback loop where the workload type classifier continuously monitors workload characteristics, compares them against known patterns, and provides classification feedback to the resource management system. This feedback mechanism enables dynamic resource reconfiguration based on actual workload conditions, improving productivity while the automated feedback process manages the complexity of coordination between different system components.
Data Source
AI summary
An embodiment of the invention provides an apparatus and method for classifying a workload of a computing entity. In an embodiment, the computing entity samples a plurality of values for a plurality of parameters of the workload. Based on the plurality of values of each parameter, the computing entity determines a parameter from the plurality of parameters that the computing entity's response time is dependent on. Here, the computing entity's response time is indicative of a time required by the computing entity to respond to a service request from the workload. Further, based on the identified significant parameter, the computing entity classifies the workload of the computing entity by selecting a workload classification from a plurality of predefined workload classifications.