Adaptive Parallelization for Distributed Job Execution
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Solution Overview
Problem
Large-scale computing systems face challenges in efficiently classifying and re-classifying items in extensive electronic catalogs, particularly in distributed marketplaces, due to the complexity and cost associated with continuous classification processes.
Innovation Solution
The implementation of adaptive parallelization techniques, where incoming job streams are dynamically partitioned based on an execution time metric and target throughput, allowing for dynamic adjustment of parallelism across computing resources to optimize job execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous classification and re-classification is performed on large electronic catalogs, then item classification accuracy is improved, but processing cost and system complexity increase
Solution Approach 1:
The patent divides the large electronic catalog into multiple partitions, with each partition processed by a separate computing node. This segmentation allows the classification system to handle large catalogs by distributing the workload across multiple independent units, reducing overall system complexity while maintaining classification accuracy for each segment.
Solution Approach 2:
The patent implements dynamic parallelization where the system automatically adjusts the number of computing nodes and partition assignments based on real-time workload characteristics. This dynamic adaptation allows the system to scale resources up or down as needed, reducing complexity during low-demand periods while maintaining high classification accuracy during peak loads.
2Productivity
If more computing resources are allocated to classification tasks, then processing throughput is improved, but resource cost increases
Solution Approach 1:
The system dynamically adjusts the number of computing nodes and partition assignments based on real-time workload characteristics. When workload increases, the system automatically allocates more resources to maintain throughput; when workload decreases, resources are released to reduce costs. This dynamic resource management resolves the contradiction between maintaining high throughput and reducing resource consumption.
Solution Approach 2:
The patent changes system parameters such as the number of partitions and parallel processing degree based on workload characteristics. By adjusting these parameters dynamically, the system optimizes the balance between processing throughput and resource consumption, allocating more resources only when necessary to handle increased classification demands.
3Productivity
If manual tuning of parallelization parameters is performed, then execution efficiency is improved, but operational complexity increases
Solution Approach 1:
The patent implements self-service through automatic parallelization parameter tuning. The system monitors workload characteristics and autonomously adjusts the number of partitions, thread counts, and other parallelization parameters without requiring manual intervention. This self-tuning capability maintains high execution efficiency while eliminating the operational complexity of manual parameter adjustment.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor job execution performance and workload characteristics. Based on this feedback, the system automatically adjusts parallelization parameters to optimize execution efficiency. This closed-loop control eliminates the need for manual tuning while maintaining high productivity through data-driven parameter optimization.
4Stability of the object's composition
If fixed parallelization is used, then system stability is improved, but adaptability to varying workloads deteriorates
Solution Approach 1:
The patent implements dynamic parallelization where the system automatically adjusts the number of computing nodes and partition assignments based on real-time workload characteristics. This dynamic approach maintains system stability through controlled adaptation, allowing the system to respond to varying workloads while maintaining consistent performance through automated resource management and load balancing.
Data Source
AI summary
Methods, systems, and computer-readable media for distributed computing with adaptive parallelization are disclosed. An execution time metric is determined based (at least in part) on execution of a batch of jobs. A quantity of partitions for an additional batch of jobs is determined based (at least in part) on the execution time metric and on a target metric. The additional batch of jobs is divided between a set of partitions of the determined quantity. Execution is initiated of the additional batch of jobs in the set of partitions. The set of partitions comprises at least a first partition and a second partition. A first subset of jobs in the first partition is executed in parallel with a second subset of jobs in the second partition.