Distributed Computing Data Partitioning for Linear and Non-Linear Operations
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Solution Overview
Problem
Distributed computing environments face challenges in efficiently executing non-linear operations due to limitations in partitioning and parallel processing, leading to reduced data processing capability and increased execution time.
Innovation Solution
The system partitions input data into subsets with distinct characteristics and assigns them to processing nodes for parallel execution, allowing both linear and non-linear operations to be efficiently processed by distinguishing between operation types and optimizing data distribution and execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If non-linear operations are executed in a distributed computing environment using traditional partitioning methods, then the operations can be performed, but the data processing capability is reduced and execution time increases
Solution Approach 1:
The patent segments the distributed computing environment into multiple processing nodes, each capable of independently executing portions of non-linear operations. The input data is partitioned into subsets that can be processed in parallel across different nodes, with each node maintaining local state information needed for non-linear computations. This segmentation enables efficient parallel processing of non-linear operations while maintaining data processing capability.
2Speed
If data is partitioned into subsets for parallel processing, then processing speed can be improved, but network bandwidth consumption increases due to data transmission
Solution Approach 1:
The patent implements local quality by enabling each processing node to perform non-linear operations using only locally stored data subsets and local computational resources. Nodes process data in-place without requiring frequent data transmission across the network, thereby maintaining high processing speed while minimizing network bandwidth consumption. Each node has the necessary computational capabilities to execute non-linear operations locally.
3Adaptability or versatility
If traditional distributed computing methods are used for linear operations, then processing can be done, but the system cannot efficiently handle non-linear operations
Solution Approach 1:
The patent creates a universal distributed computing framework where processing nodes can efficiently handle both linear and non-linear operations. The system uses a unified approach with local processing capabilities that work for both operation types, eliminating the need for separate processing pathways. This multi-functionality enables the system to adapt to different operation types while maintaining high data processing capability.
Data Source
AI summary
Data processing can be managed in a distributed computing environment (DCE). In one example, the DCE can receive a sequence of computing operations to be consecutively executed in the DCE. For each computing operation in the sequence, the DCE can receive input data for the computing operation, partition the input data into subsets, and determine whether the computing operation is linear or non-linear. The DCE can then apply different processing techniques to the subsets depending on whether the computing operation is linear or non-linear.