Leveraging CNN-CPU Scheduling for Enhanced Resource Management in Storage Edge Network Frameworks

• Published in: 2024 5th International Conference on Computer Engineering and Application (ICCEA) pp. 615 - 619
• Main Authors: Alwahedi, Mazen, Duanmu, Chunjiang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 12-04-2024
• Subjects: cpu-cnn; Data processing; Deep learning; EDRs schedule; enhanced computing; enhanced send; Feature extraction; Resource management; RRAPS; Scheduling; Scheduling algorithms; System performance
• ISSN: 2159-1288
• DOI: 10.1109/ICCEA62105.2024.10603504

Because the landscape of data processing created is likely to rise considerably in the next years due to the proliferation of devices that require data processing at edge computing, it is imperative for data processing issues at the network's edge storage frame. The goal of this study is to find out how deep learning can be used to speed up operation processing. Effective resource management is essential in the age of edge computing to maximize the performance of storage edge networked frameworks. The purpose of this work is to investigate how resource management in such systems could be improved by utilizing CNN-CPU scheduling approaches. Our goal is to optimize job allocation, prioritization, and scheduling in storage edge situations by fusing Convolutional Neural Networks (CNNs) with CPU-based scheduling methods. The suggested method aims to improve overall system performance, reduce latency, and maximize resource use. We illustrate the efficacy of CNN-CPU scheduling in improving resource management in storage edge networked frameworks with experimental validation and performance analysis. This research contributes to advancing the capabilities of edge computing systems by leveraging deep learning techniques for efficient resource allocation and management. The proposed approach seeks to overcome the limitations of traditional scheduling methods by leveraging the power of deep learning. CNNs are renowned for their ability to extract meaningful features from data, and by harnessing this capability, we can optimize resource utilization and minimize latency in storage edge networked frameworks. Through a combination of CNN-based feature extraction and CPU-based scheduling decisions, we strive to achieve efficient and intelligent resource management. This study investigates the potential of CNN-CPU scheduling techniques to address these challenges and enhance resource management in such frameworks. By integrating Convolutional Neural Networks (CNNs) with CPU-based scheduling algorithms, we aim to revolutionize task allocation, prioritization, and scheduling within storage edge environments.