article · IEEE Transactions on Consumer Electronics
Managing task scheduling in Internet of Things edge and cloud computing environments is increasingly challenging due to issues such as uneven workloads, slow computational convergence, and the under-use of virtual machines. A hybrid optimization approach, known as RFOAOA, combines search operators from the Red Fox Optimization and Arithmetic Optimization Algorithm to address these scheduling demands. The method was evaluated using both real and synthetic workload traces from high-performance computing datasets, specifically NASA Ames iPSC/860 and HPC2N. Experimental results indicate that this hybrid method outperforms existing state-of-the-art scheduling strategies, delivering superior performance by reducing overall completion time, or makespan, alongside lowering energy consumption across computing infrastructure.
As Internet of Things networks expand, data processing demands place heavy burdens on computing infrastructure. Better task scheduling ensures that edge and cloud systems complete workloads faster while consuming less electricity. This improves overall system reliability, supports sustainable computing operations, and helps reduce power costs for networks handling large volumes of data from connected devices.
This method could benefit cloud and edge computing operators or Internet of Things platform managers looking to reduce energy costs and improve task execution speeds. Tested and applied in simulated experiments using benchmark workload datasets, the technology sits at an applied algorithmic stage and would require integration into commercial task management middleware before deployment in live enterprise environments.
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The demand for task scheduling in Internet of Things (IoT)-based edge and cloud computing environments is experiencing exponential growth due to the need to address real-world issues, such as load instability, slow convergence rates, and under-utilization of virtual machine devices. In this paper, a hybrid enhanced optimization method called RFOAOA is designed to solve challenging task scheduling scenarios in edge-cloud computing-based IoT environments. The proposed method leverages the strengths of two powerful search operators, such as Red Fox Optimization (RFO) and Arithmetic Optimization Algorithm (AOA). To evaluate the effectiveness of the proposed method, we conducted experiments on real and synthetic workload traces of NASA Ames iPSC/860 and HPC2N. The comparative analysis demonstrates that the proposed algorithm achieves better performance in terms of Makespan time and energy consumption and outperforms the other state-of-the-art scheduling methods.
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DOI: 10.1109/tce.2023.3321783
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