Self-Balancing Task Server for Distributed System Efficiency
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Solution Overview
Problem
Current distributed task systems rely on task execution units that operate passively, lacking self-balancing capabilities, which can lead to inefficiencies when handling large numbers of tasks, especially when mixed-duration tasks are involved, resulting in potential loss of control and reduced processing capacity.
Innovation Solution
Incorporating a self-balancing unit within the task server that assesses its operational conditions and, if busy, transfers tasks to other servers for execution, actively managing task distribution and ensuring linear task execution through feedback mechanisms from a task transaction server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If task execution units operate passively under centralized control, then system simplicity is maintained, but task distribution efficiency deteriorates when handling large numbers of mixed-duration tasks
Solution Approach 1:
Task execution units are equipped with self-balancing units that enable them to autonomously assess their own operational conditions and actively participate in task distribution by transferring tasks to other servers when busy, eliminating the need for passive waiting and improving task distribution efficiency without significantly increasing system complexity
Solution Approach 2:
Instead of the traditional model where the task strategy unit alone controls task distribution, the invention inverts the control dynamic by enabling task execution units to actively initiate task transfers based on their own status, making the previously passive units the active decision-makers in task distribution
2Productivity
If task execution units actively manage their own task distribution, then task processing efficiency is improved, but control complexity increases
Solution Approach 1:
The task transaction server provides feedback to task execution units about task execution status and server availability, enabling informed decision-making for task transfers while maintaining centralized coordination, thus improving efficiency without excessive control complexity
Solution Approach 2:
The system implements dynamic task distribution where task execution units can change their operational state from passive to active based on real-time conditions, with the self-balancing unit dynamically assessing workload and initiating transfers when necessary, adapting to changing system states without rigid control structures
3Stability of the object's composition
If centralized task strategy unit manages all task distribution, then system control is maintained, but response time to workload changes increases
Solution Approach 1:
Task execution units continuously monitor their own operational conditions and prepare for task transfers in advance when detecting high workload, rather than waiting for centralized instructions, reducing response time to workload changes while maintaining system control through coordinated feedback
Data Source
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AI summary
A distributed task system has a task transaction server and at least one task server. Instead of being merely passively called by the task transaction server for executing a task, the task server performs self-balancing according to task execution conditions and operation conditions of the task server. The task transaction server receives task requests from the task server, records the execution conditions, and provides feedback to the task server, and the task server executes the task according to the received feedback and the operation conditions of the task server. The task transaction server may determine if the task server can execute the task according to the execution conditions of the task, and feedback to the task server. A self-balancing unit of the task server may further determine whether the task server is busy, and if not busy, trigger a task execution unit of the task server to execute the task.