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Abstract

The advancement in the Internet of Things (IoT) and its broad scope of applications have led to the generation of huge volumes of data to be processed. Time-consuming operations, particularly time-critical operations, are submitted to fog nodes due to their proximity. Meanwhile, advanced operations are submitted to cloud computing centers for extensive computation and storage. However, task allocation to fog nodes lessens transmission latency and improves resource utilization. On the other hand, task offloading to cloud data centers maximizes resource utilization while increasing transmission delay because of the greater distance. The difficulty is in efficiently mapping tasks with appropriate resources that have matching requisites with tasks, which is the key problem in cloud-fog computing that needs to be addressed. In light of these challenges, this study introduces an innovative approach named Multi-objective Reptile Search Algorithm (MRSA), aimed at mitigating concerns about quality of service (QoS). This algorithm is implemented within the fog broker, a pivotal component responsible for task distribution. The simulation results demonstrate the efficacy of MRSA in enhancing resource utilization, makespan, and load balancing, substantiated through comparison with existing algorithms.

Keywords

cloud computing task scheduling resource utilization load balancing

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Multi-objective task scheduling in cloud-fog computing using reptile search algorithm

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