Satellite Network Task Deployment Using Genetic Algorithm Optimization
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Solution Overview
Problem
The dynamic topology and limited on-board resources in satellite networks make it challenging to deploy tasks efficiently for computing and processing, leading to increased processing delays and traffic.
Innovation Solution
A method is introduced that involves constructing task processing delay and traffic models, using a genetic algorithm to determine optimal task deployment locations by iteratively updating populations with crossover and mutation operations, ensuring that satellite nodes have sufficient computation and caching resources for task processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are deployed in satellite network, then processing capability is improved, but processing delay and traffic increase due to dynamic topology and limited resources
Solution Approach 1:
The patent applies dynamics by making the task deployment strategy adaptive to the changing satellite network topology. The genetic algorithm continuously optimizes deployment locations as satellites move and network conditions change, transforming a static deployment problem into a dynamic optimization process that responds to real-time network state
Solution Approach 2:
The patent uses preliminary action by pre-calculating and optimizing task deployment locations before tasks are executed. The genetic algorithm performs iterative optimization in advance to determine optimal satellite nodes for task deployment, considering future network states and resource availability, thereby reducing actual processing delay
2Productivity
If tasks are deployed in satellite network, then computing resources are utilized, but on-board resource limitations are exceeded
Solution Approach 1:
The patent applies parameter changes by optimizing the deployment location parameters of tasks across the satellite network. The genetic algorithm adjusts the parameters (satellite node selections) to maximize resource utilization while staying within on-board resource constraints, finding the optimal configuration that balances productivity and resource limits
Solution Approach 2:
The patent uses partial action by selectively deploying tasks to only those satellite nodes that have sufficient available resources. Rather than attempting to deploy all tasks to all satellites, the system performs partial deployment to feasible nodes, ensuring resource constraints are not violated while still achieving task processing
3Productivity
If genetic algorithm is used for optimization, then deployment efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex optimization problem into smaller sub-problems handled by the genetic algorithm's modular operations: selection, crossover, and mutation. Each operation processes individuals in the population independently, breaking down the overall complexity into manageable computational steps that can be executed efficiently
Data Source
AI summary
Disclosed is a method for deploying tasks in a satellite network. In the method for deploying tasks in a satellite network, a utility function is determined by constructing a task processing delay model and a traffic model; a fitness degree of each individual is determined according to the utility function, and an individual with a highest fitness degree is put into a next-generation population; a probability that each of the plurality of individuals is selected is determined according to the fitness degree; a crossover operation and a mutation operation are performed on the individual according to the probability, to obtain a crossover individual and a mutation individual respectively; an available crossover individual and an available mutation individual are put into a next-generation population, to perform a repeated iteration and complete population updating; an optimal task deployment location table is output; and satellite network task deployment is completed.