| Abstrakt: |
Scheduling tasks is a pivotal and practical action within the fog and cloud layers. This study introduces a two-tier task scheduling approach with improved version of the Harris Hawk optimization algorithm to lower delay and power consumption. We employ the ConvLSTM neural network in fog layer to predict the optimal location for task execution and estimate the workload of virtual machines. We harness the Jellyfish Search (JS) optimizer to schedule executable tasks within the fog layer efficiently. Furthermore, we present an enhanced version of the Harris Hawk optimization algorithm, incorporating sine and cosine searching for task scheduling optimization. Our proposed algorithm can predict virtual machine workloads and task execution locations based on task and resource characteristics, resulting in reduced task completion times and energy consumption. Our experiments demonstrate that our approach exhibits lower delays and energy consumption in cloud layer task scheduler compared to the MGWO, NSGA-II, and MOPSO algorithms. Furthermore, it outperforms several algorithms, including CGO, AOS, CSA, WOA, HGSWC, MPA, and CHMPAD, when it comes to minimizing delays in fog layer task scheduling, ultimately leading to faster task execution. [ABSTRACT FROM AUTHOR] |
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