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Self-adaptive deep learning techniques provide scalability and flexibility in deploying and administrating deep learning models in the cloud environment. DL is widely used in cloud computing architecture, and these methods seek to optimize performance and resource utilization by automatically adjusting the resources allotted to machine learning tasks in response to workload fluctuations. Adaptive task scheduling algorithms maximise the distribution of DL techniques to available resources based on their features and needs. DL algorithms make intelligent judgements regarding job allocation, guaranteeing effective resource utilization and workload management. They consider variables, including task priority, resource availability, and resource capabilities. This research work deploys the Deep Convolutional Neural Network with a Fuzzy (DCNN-F) technique by differentiating the cloud nodes. The complexity of workflow scheduling in the cloud context is optimized by efficient learning, whereas energy and time consumption are effectively handled. The DCNN-F is trained with the resources in the cloud, and the solution for scheduling issues is rectified by learning data. The network is iteratively refined and optimized based on the feedback mechanism in DCNN-F. By combining the power of DCNN-Fs with efficient resource allocation strategies, research can maximise energy and time scheduling precedence-constrained tasks in cloud computing environments. The simulation outcome of DCNN-F is compared with state-of-art techniques, and DCNN-F outperforms Deep Q-Learning (DQL), Deep Reinforcement Learning based Optimization (DRL-O) and Deep Reinforcement Learning based Scheduling (DRL-S) techniques. |
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