Real-Time Task Scheduling and Resource Scheduling in Fog Computing using Deep Learning Techniques
Author:
Affiliation:
1. Noida Institute of Engineering and Technology,Department of CS,Greater Noida,India
2. Galgotias University,Department of Computer Science and Engineering,Greater Noida,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10150430/10150434/10150474.pdf?arnumber=10150474
Reference15 articles.
1. Classification of resource management approaches in fog/edge paradigm and future research prospects: a systematic review
2. Improved Jellyfish Algorithm-based multi-aspect task scheduling model for IoT tasks over fog integrated cloud environment;jangu;Journal of cloud computing,2022
3. Survey on Load Balancing and Scheduling Algorithms in Cloud Integrated Fog Environment
4. EMCS: An Energy-Efficient Makespan Cost-Aware Scheduling Algorithm Using Evolutionary Learning Approach for Cloud-Fog-Based IoT Applications
5. Scheduling Real Tim Security Aware Tasks in Fog Networks
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2. Task scheduling approach in fog and cloud computing using Jellyfish Search (JS) optimizer and Improved Harris Hawks optimization (IHHO) algorithm enhanced by deep learning;Cluster Computing;2024-04-13
3. Resource allocation problem and artificial intelligence: the state-of-the-art review (2009–2023) and open research challenges;Multimedia Tools and Applications;2024-01-29
4. Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment;Frontiers in Computer Science;2023-12-14
5. PaF & MbOTS: Novel Task Scheduling Strategies for Fog Computing Environment;2023 6th International Conference on Recent Trends in Advance Computing (ICRTAC);2023-12-14
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