Task Processing Topology Matching for Resource Allocation
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Solution Overview
Problem
In big data computing engines, efficiently allocating resources for task processing is challenging due to the complexity of task execution and the need for reasonable resource configuration to improve execution efficiency, especially in scenarios with data skewness and ad hoc tasks lacking historical information for predictive analysis.
Innovation Solution
A task processing method that generates a topological graph for a target task, matches it with historical tasks, sifts similar tasks based on attribute conditions, and determines execution parameter recommendations to optimize resource allocation and shuffle performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resource allocation is increased for task processing, then task execution efficiency is improved, but resource waste occurs when tasks lack historical information for predictive analysis
Solution Approach 1:
The system performs preliminary actions by building topological graphs for historical tasks in advance and storing them for future reference. When a new task arrives, the system quickly matches it against pre-built historical topological graphs to predict resource needs, avoiding the need to allocate excessive resources during the initial task execution phase.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing actual task execution results with predictions based on historical data. The feedback loop refines the matching algorithm and topological graph construction over time, improving resource allocation accuracy and reducing waste while maintaining high execution efficiency.
2Measurement precision
If detailed task analysis is performed to improve resource allocation accuracy, then resource configuration is optimized, but system complexity increases
Solution Approach 1:
The system segments the complex task analysis process into manageable components: task parameter extraction, topological graph construction, historical task matching, and resource prediction. Each component handles a specific aspect of the analysis, making the overall system more manageable and maintainable while achieving high allocation accuracy.
Solution Approach 2:
The topological graph serves as an intermediary structure that bridges raw task data and resource allocation decisions. By transforming task information into standardized topological representations, the system simplifies the matching process and reduces complexity in comparing historical tasks with new tasks.
3Productivity
If historical tasks are matched to provide execution parameter recommendations, then resource allocation is optimized, but time is consumed in the matching and sifting process
Solution Approach 1:
The system applies partial action by performing matching on the most critical task parameters first, rather than analyzing all possible attributes. The topological graph matching focuses on key structural features and execution patterns, providing sufficient accuracy for resource allocation without consuming excessive time on minor details.
Solution Approach 2:
The system changes parameters by transforming task characteristics into standardized topological graph representations with specific attributes like node count, edge density, and execution depth. This parameter transformation enables efficient comparison and matching operations while maintaining the essential characteristics needed for accurate resource prediction.
Data Source
AI summary
A task processing method and apparatus, an electronic device, a storage medium and a program product are provided. The method includes: receiving a remote shuffling service request of a target task, and generating a topological graph of the target task according to execution flow information of the target task in response to a remote system service request of the target task, the topological graph including a plurality of target nodes, and each target node corresponding to at least one subtask of the target task; matching information of the target node with information of at least one historical node to obtain a first matching result; sifting the target historical task based on a preset attribute condition; and determining, based on a sifting result, execution parameter recommendation information for the target task as a processing result of the remote shuffling service request.