Multi-Access Edge Computing Task Dispatching for Latency Optimization
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Solution Overview
Problem
Current Multi-Access Edge Computing (MEC) systems lack efficient task delegation mechanisms based on latency, data size, and region-specific context, leading to inefficient data processing and communication in advanced driver-assistance systems and navigational services.
Innovation Solution
A system that classifies data packets as tasks, allocates them to processing locations based on latency, data lifetime, and data size, and dynamically calculates alternate processing locations within a radius, redistributing task portions to optimize processing based on scoring values associated with changes in the user's position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If map information is received from all areas, then completeness of map data is improved, but processing load and latency increase
Solution Approach 1:
The system divides the service area into multiple data regions and assigns different processing priorities to different areas. Map information from the user's current area is processed with high priority, while information from other areas is processed with lower priority or deferred, allowing selective reception that maintains completeness while reducing processing load.
Solution Approach 2:
The patent segments map information processing into different priority levels based on geographic regions. The data packet classifier divides incoming map data into segments corresponding to different data regions, enabling the system to handle information from multiple areas without processing everything simultaneously, thus reducing latency while maintaining data completeness.
2Productivity
If map information is received from a single area, then processing load is reduced, but completeness and accuracy of map services deteriorate
Solution Approach 1:
The system performs preliminary classification of map information by geographic region before full processing. Data packets are pre-sorted into different data regions based on their origin, allowing the system to efficiently manage incoming information from multiple areas by organizing it into manageable segments that can be processed according to priority.
Solution Approach 2:
The data packet classifier acts as an intermediary between incoming map information from multiple areas and the processing system. It mediates the flow of information by routing data packets to appropriate data regions based on their geographic origin, enabling efficient handling of multi-area information without overwhelming any single processing component.
3Device complexity
If static task allocation is used, then system simplicity is maintained, but adaptability to user position changes deteriorates
Solution Approach 1:
The system implements dynamic task allocation where the data packet classifier continuously monitors user position and adjusts the priority of different data regions accordingly. As the user moves between areas, the system dynamically reweights which regions receive high-priority processing, creating an adaptive system that responds to position changes without requiring complete system redesign.
Solution Approach 2:
The patent changes the parameter of task priority based on user position. The data packet classifier modifies the priority weights assigned to different data regions dynamically as the user moves, allowing the same system structure to adapt to different geographic contexts by changing operational parameters rather than structural configuration.
4Loss of time
If dynamic task redistribution is implemented, then processing efficiency and latency are improved, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the data packet classifier continuously monitors user position, processing status, and latency metrics, then adjusts task allocation accordingly. This closed-loop control enables dynamic redistribution of processing tasks based on real-time conditions, optimizing latency while keeping complexity manageable through systematic feedback-driven adjustments.
Data Source
AI summary
Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises in response to receiving a data packet from a computing device, classifying the data packet as a task having one or more portions; allocating the classified task to a processing location within a data region based on a location of the computing device; in response to a change associated with the task, dynamically calculating alternate processing locations within a radius of the data region to process one or more portions of the task based on scoring values associated with the change; and redistributing at least one portion of the classified task according to an alternate processing location of dynamically calculated alternate processing locations.