Task Offloading Decision Graph for Wireless Equipment
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Solution Overview
Problem
Current methods for offloading computing tasks from wireless equipment to remote third-party equipment in cellular radio communication systems often rely on single parameters, such as energy consumption, which are insufficient to ensure holistic decision-making and satisfy user quality of experience (QoE), leading to suboptimal resource utilization and network congestion.
Innovation Solution
A method that classifies tasks into multiple execution classes using a hierarchical directed graph, considering feasibility, criticality, and opportunity criteria, allowing for multi-parameter decision-making without increasing complexity, and enabling tasks to be executed locally or remotely based on specific criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single parameter (energy consumption) is used to decide task offloading, then the decision process is simple, but the decision quality is insufficient and does not meet user QoE requirements
Solution Approach 1:
The patent segments the decision-making process into three distinct hierarchical levels: feasibility criteria (can the task be offloaded?), criticality criteria (how urgent is the task?), and opportunity criteria (is offloading beneficial?). Each level evaluates specific aspects of the offloading decision, allowing comprehensive multi-parameter consideration while maintaining structured simplicity through modular assessment at each segment.
2Reliability
If multiple parameters are considered for task offloading decision, then decision quality improves, but the optimization problem becomes complex
Solution Approach 1:
The patent divides the multi-parameter decision problem into three separate criterion groups evaluated at different hierarchical levels. Feasibility criteria assess offloading capability, criticality criteria evaluate task urgency, and opportunity criteria determine offloading benefit. This segmentation transforms a complex multi-parameter optimization problem into a structured sequence of simpler evaluative steps, each handling specific parameters independently.
Solution Approach 2:
The patent introduces a hierarchical dimension to the decision-making process, organizing criteria into three levels (feasibility, criticality, opportunity) rather than evaluating all parameters simultaneously in a single dimension. This dimensional restructuring allows multiple parameters to be considered systematically across different hierarchical layers, reducing the complexity of the overall optimization problem while maintaining comprehensive decision quality.
3Duration of action of moving object
If tasks are offloaded to remote third-party equipment, then wireless equipment battery life is extended, but network congestion increases
Solution Approach 1:
The patent applies local quality by differentiating between immediate remote execution (offloading to third-party equipment) and immediate local execution (processing tasks locally on wireless equipment). The decision mechanism selectively applies offloading only when feasibility, criticality, and opportunity criteria are satisfied, rather than uniformly offloading all tasks. This localized approach ensures battery extension benefits are achieved while avoiding unnecessary network traffic that would contribute to congestion.
4Loss of time
If task offloading is performed immediately, then user experience is improved, but energy consumption increases
Solution Approach 1:
The patent changes the decision parameters by introducing a multi-criteria evaluation framework that assesses feasibility, criticality, and opportunity before determining immediate remote execution. Instead of always performing immediate offloading, the system dynamically evaluates multiple parameters and only executes tasks remotely when the opportunity criterion confirms energy benefits. This parameter-based decision mechanism balances execution speed with energy consumption by making offloading conditional rather than automatic.
Data Source
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AI summary
The invention pertains to a method of processing a computing task whose execution is required by an item of wireless equipment of a cellular communication network, comprising a step of classifying the task in at least one class of immediate off-site execution and a class of immediate local execution, carried out by traversing an oriented graph consisting of decision nodes (D1-D7)and of terminal nodes (T1-T8) each assigned to one of the classes, the decision nodes being distributed into a least three hierarchical levels (N1-N4) themselves distributed into at least three subsets of levels (A1-A3), the traversal of the graph comprising: - during the transit through any decision node (D1) of a first subset of levels (A1), the verification of a feasibility criterion (F) for the off-siting of the task for execution by the third-party equipment, - during the transit through any decision node (D2-D4) of a second subset of level (A2), the verification of a criticality criterion (C1, C2) for the execution of the task, and - during the transit through any decision node (D5-D7) of a third subset of levels (A3), the verification of a criterion of opportunity (O1, O2, O3) for the execution of the task.