Distributed Management Units for Redundant Process Elimination
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Solution Overview
Problem
In autonomous distributed systems, unnecessary processes often occur due to a lack of efficient management among multiple agents, leading to redundant operations and increased resource utilization.
Innovation Solution
An information processing system comprising multiple processing modules and management units that determine and manage processing plans based on stored information and history, reducing redundant processes by identifying and eliminating unnecessary steps through coordinated communication and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If processes are executed in an autonomous and distributed manner among multiple agents, then system flexibility and autonomy are improved, but unnecessary redundant processes occur and resource utilization increases
Solution Approach 1:
The system performs preliminary actions by storing process history information and function information in advance before actual process execution. When a process is requested, the management unit checks the stored history to determine if the process has already been executed, thereby preventing redundant execution before it occurs.
Solution Approach 2:
The system implements feedback mechanisms where the management unit continuously monitors process execution status, stores results in history information, and uses this feedback to make intelligent decisions about subsequent process requests, avoiding unnecessary redundant processes while maintaining autonomous distributed execution.
2Adaptability or versatility
If multiple management units autonomously negotiate and determine task assignments, then system adaptability is improved, but communication overhead and process complexity increase
Solution Approach 1:
Function information and process history are stored in advance in a centralized manner, allowing management units to make quick decisions based on pre-existing information rather than engaging in extensive real-time negotiations, thereby reducing communication overhead while maintaining adaptability.
Solution Approach 2:
The system introduces an intermediary mechanism where management units reference stored process history and function information as a mediator to resolve task assignments, reducing the need for direct complex negotiations between multiple autonomous agents.
3Reliability
If process history is tracked and stored for each management unit, then redundant process detection is improved, but memory usage and information management complexity increase
Solution Approach 1:
The stored process history information serves multiple functions: it detects redundant processes, provides context for future decisions, and enables intelligent task assignment. This multi-functionality justifies the information storage by extracting maximum value from the stored data.
Solution Approach 2:
The system changes parameters by storing only essential process history information (process ID, execution status, result) rather than complete process details, optimizing the balance between redundant process detection capability and memory usage.
Data Source
AI summary
An information processing system includes plural processing modules that execute processes for a processing target based on functions thereof, plural management units that manage the processing modules, and a memory that stores information on the functions of the processing modules in association with identification information on the management units and identification information on the processing modules. Each of the management units includes a determining unit that determines a processing plan including at least a subsequent process based on the processing target, storage contents of the memory, and history information on a process that has been executed for the processing target, and a requesting unit that requests a process by transmitting, to one of the management units that manages a processing module that executes the subsequent process, a processing object including the processing target, the history information, and identification information on the processing module that executes the subsequent process.


