Server-Based Resource Allocation in Distributed Processing

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Solution Overview

Problem

Distributed processing systems face inefficiencies in processing large datasets due to unique evaluations by independent processing units, which can lead to prolonged processing times and resource inefficiencies, as these evaluations are based on individual learning and adaptability, making it difficult to standardize and optimize the processing task.

Innovation Solution

The system employs a server that receives data from independent processing units, generates attribute vectors, calculates contribution factors, and optimizes the subset measure by replacing data groups with those having the smallest and largest positive effects, thereby maximizing processing efficiency and directing data to the best-suited processors.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If independent processing units perform unique evaluations based on individual learning and adaptability, then processing can be customized and adapted to different data, but processing time increases and resource efficiency decreases

Engineering Contradiction:

Improvecustomization of processing evaluationsVSAvoidprocessing speed

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent segments the processing task into two distinct phases: a centralized server phase that performs standardized evaluations and generates attribute vectors, and distributed processing unit phase that performs only the necessary data processing. This segmentation eliminates redundant unique evaluations at each processing unit while maintaining adaptability through the server's centralized intelligence.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The server acts as an intermediary between data sources and processing units. It receives data, performs standardized evaluations, generates attribute vectors, and directs data to appropriate processing units. This intermediary role eliminates the need for each processing unit to perform complete unique evaluations, thereby reducing processing time while maintaining system adaptability.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If independent processing units perform unique evaluations based on individual learning, then processing can be optimized for specific data types, but the number of evaluations increases and resource waste occurs

Engineering Contradiction:

Improveoptimization for specific data typesVSAvoidnumber of evaluations

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent merges the evaluation function into a single centralized server that processes all data uniformly. Instead of each processing unit performing its own unique evaluations, the server performs evaluations once and generates attribute vectors that are reused across multiple processing units. This combining approach reduces the total number of evaluations while maintaining optimization for specific data types.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system changes the parameter of evaluation standardization by introducing standardized attribute vectors generated by the server. These attribute vectors serve as common references that processing units can use without performing redundant evaluations. This parameter change reduces the number of evaluations needed while preserving data-type-specific optimization through the standardized vector approach.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If data is distributed to multiple processing units without optimization, then system scalability is improved, but processing efficiency decreases due to mismatched data-processing unit assignments

Engineering Contradiction:

Improvesystem scalabilityVSAvoidprocessing efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The server performs preliminary actions by evaluating data beforehand and generating attribute vectors that characterize the data. Based on these pre-generated vectors, the server determines the optimal processing units to assign data to before distribution. This preliminary action enables scalable system architecture while ensuring processing efficiency through informed data-unit matching.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms where processing units provide information about their capabilities and performance to the server. The server uses this feedback to optimize future data distribution decisions, creating a feedback loop that improves processing efficiency while maintaining scalability. The attribute vectors and contribution factors serve as feedback structures that guide optimal data assignment.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS10594622B2Resource allocation in distributed processing systems

Publication Date: 2020.03.17 PEARSON EDUCATION INC

AI summary

A distributed processing system is disclosed herein. The distributed processing system includes a server, a database server, and an application server that are interconnected via a network, and connected via the network to a plurality of independent processing units. The independent processing units can include an analysis engine that is machine-learning-capable, and thus uniquely completes its processing tasks. The server can provide one or several pieces of data to one or several of the independent processing units, can receive analysis results from these one or several independent processing units, and can update the result based on a value characterizing the machine learning of the independent processing unit.