Dynamic Network Resource Scheduling for Real-Time Task Optimization
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Solution Overview
Problem
High-end network applications require efficient resource scheduling to maximize bandwidth utilization and quality of service, but traditional static scheduling methods are inadequate for real-time critical tasks, leading to insufficient network capacity and suboptimal resource allocation.
Innovation Solution
A dynamic scheduling method that integrates reactive and proactive phases, using fuzzy logic to optimize task allocation and resource assignment in real-time, allowing for task swapping and re-allocation to ensure efficient resource utilization and minimize network congestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static scheduling methods are used for network resource allocation, then implementation simplicity is maintained, but network capacity utilization and quality of service deteriorate for real-time critical tasks
Solution Approach 1:
The patent implements dynamic scheduling that adapts to real-time network conditions and task priorities. The system continuously monitors network state and adjusts resource allocation dynamically, transitioning from static predetermined schedules to flexible real-time decision-making, thereby improving network capacity utilization for critical tasks while maintaining manageable complexity through structured algorithms.
Solution Approach 2:
The patent changes scheduling parameters based on task characteristics and network conditions. It adjusts allocation decisions by considering task priority levels, bandwidth requirements, and real-time network state, allowing the system to optimize resource distribution for different scenarios rather than using fixed static parameters.
2Loss of time
If static scheduling is used to simplify resource allocation, then scheduling computation time is reduced, but resource allocation optimality and service quality worsen
Solution Approach 1:
The system implements dynamic scheduling that computes allocations in real-time based on current network conditions and task requirements. This dynamic approach ensures that service quality is maintained by adapting to actual network state, while computation is optimized through efficient algorithms that make timely decisions without exhaustive processing.
Solution Approach 2:
The patent performs preliminary assessments of task requirements and network capacity before making allocation decisions. By pre-evaluating task priorities and bandwidth needs, the system prepares scheduling information in advance, reducing actual computation time while ensuring optimal service quality through informed allocation decisions.
3Device complexity
If traditional scheduling methods are used, then system simplicity is maintained, but adaptability to real-time critical tasks and network conditions deteriorates
Solution Approach 1:
The patent implements a dynamic scheduling system that adapts to real-time network conditions and task priorities. The system continuously monitors network state and adjusts resource allocation dynamically, enabling it to handle critical tasks with varying requirements while maintaining a structured approach that manages system complexity.
Solution Approach 2:
The scheduling system segments tasks by priority levels and allocates resources accordingly. By dividing the scheduling problem into discrete priority categories and handling each segment with appropriate algorithms, the system achieves high adaptability to different task types while keeping overall system complexity manageable through modular design.
4Use of energy by moving object
If static schedules are computed in advance, then scheduling overhead is minimized, but responsiveness to emerging critical tasks and network changes worsens
Solution Approach 1:
The patent implements dynamic scheduling that computes allocations in real-time based on current network conditions and task requirements. This dynamic approach ensures rapid responsiveness to critical tasks by making allocation decisions at the time of need rather than relying on predetermined static schedules, while computational overhead is managed through efficient real-time algorithms.
Data Source
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AI summary
The present invention provides a method which can be used to optimise the delivery of series over communications networks. Tasks which need to executed within a short timescale and those which are not due to be executed for a long time are excluded from the optimisation process. A score is determined, using fuzzy logic, for each task and its related resources and for each resource and its related tasks. This score is then used to determined which tasks should be optimised.