IoT Computing Task Allocation for Fair Cloud-Edge Offloading
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge of insufficient computing power in IoT devices leads to network congestion and increased latency when processing computation-intensive and time-sensitive applications, with existing centralized computing task offloading methods neglecting device fairness.
Innovation Solution
A method for computing task allocation using centralized, distributed, or hybrid modes, considering factors like task features, terminal energy consumption, and network topology to optimize system performance and fairness, involving communication and computing resource allocation, and task offloading decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If computing tasks are offloaded to cloud or edge nodes, then computing power insufficiency is addressed, but network congestion and latency increase
Solution Approach 1:
The patent segments the centralized cloud computing system into a hierarchical structure with edge computing nodes distributed across the network. This segmentation allows tasks to be processed closer to their source, reducing network congestion and latency while maintaining adequate computing power distribution.
Solution Approach 2:
The patent introduces a spatial dimension to computing task allocation by distributing edge nodes across different network locations. This dimensional change transforms the single-point cloud computing model into a multi-dimensional distributed architecture, enabling tasks to be processed in closer proximity to IoT devices.
2Productivity
If centralized computing task offloading is used, then system performance is optimized, but device fairness is neglected
Solution Approach 1:
The patent applies local quality by allowing each edge node and IoT device to have customized computing task allocation parameters based on their specific characteristics, capabilities, and requirements. This enables fair treatment of diverse devices while maintaining overall system performance through localized optimization.
Solution Approach 2:
The patent implements dynamic task allocation where the system continuously adapts to changing device states, network conditions, and task characteristics. This dynamic approach ensures fairness by adjusting allocation decisions in real-time based on current system state rather than using static centralized rules.
3Adaptability or versatility
If multiple allocation modes are implemented, then flexibility and fairness are improved, but system complexity increases
Solution Approach 1:
The patent creates a universal task allocation framework that can operate in multiple modes (centralized, distributed, hybrid) depending on system requirements. This multi-functional design allows the same core system to adapt to different scenarios without requiring entirely separate implementations for each mode.
Solution Approach 2:
The patent introduces an intermediary layer that manages the complexity of multiple allocation modes by providing standardized interfaces and coordination mechanisms. This intermediary abstraction allows diverse allocation strategies to coexist while maintaining manageable system complexity through unified control structures.
Data Source
AI summary
The disclosure discloses a computing task allocation method, an updating method for computing task allocation, a terminal and a network device. When a computing task of a terminal is generated, computing task allocation is performed using at least one of a centralized mode, a distributed mode, or a hybrid mode; the computing task allocation includes communication resource allocation, computing resource allocation, and a task offloading decision; the above computing task allocation method subjected to dynamically updating according to a terminal state, a network state or a task state. Therefore, the compromise problem between overall system performance optimization and device fairness in a cloud-edge collaborative IoT system is solved.