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Mobility-aware task scheduling based on artificial intelligence in edge-cloud systems

algorithms-and-analysis-of-algorithms
artificial-intelligence
distributed-and-parallel-computing
real-time-and-embedded-systems
neural-networks

Abstract

In the rapidly evolving landscape of edge-cloud computing, optimizing task scheduling and ensuring reliable fault prediction are critical for maintaining system performance and resource efficiency. We propose an innovative fault prediction model based on Graph Attention Networks (GAT) and a migration decision model leveraging Generative Adversarial Networks (GAN). Driven by the increasing demand for efficient resource management in edge environments, fueled by the proliferation of IoT devices and the need for real-time data processing, our hybrid approach uses GAT to identify possible failure points in task execution based on historical data, while the GAN model predicts optimal migration paths to proactively mitigate these failures. This dual strategy enhances fault tolerance while ensuring seamless task migration with minimal latency, improving overall system throughput. We conducted a thorough evaluation of our proposed models against existing fault prediction and task migration methods, demonstrating significant improvements in execution time and energy consumption. The results show that our hybrid approach enhances predictive accuracy and optimizes resource allocation in edge-cloud environments.
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