An Improved Hybrid Swarm Intelligence for Scheduling IoT Application Tasks in the Cloud

The usage of cloud services is growing exponentially with the recent advancement of Internet of Things (IoT)-based applications. Advanced scheduling approaches are needed to successfully meet the application demands while harnessing cloud computing's potential effectively to schedule the IoT services onto cloud resources optimally. This article proposes an alternative task scheduler approach for organizing IoT application tasks over the CCE. In particular, a novel hybrid swarm intelligence method, using a modified Manta ray foraging optimization (MRFO) and the salp swarm algorithm (SSA), is proposed to handle the problem of scheduling IoT tasks in cloud computing. This proposed method, called MRFOSSA, depends on using SSA to improve the local search ability of MRFO that typically enhances the rate of convergence towards the global solution. To validate the developed MRFOSSA, a set of experimental series is performed using different real-world and synthetic datasets with variant sizes. The performance of MRFOSSA is tested and compared with other metaheuristic techniques. Experiment results show the superiority of MRFOSSA over its competitors in terms of performance measures, such as makespan time and cloud throughput.

Automated summary

This article proposes a new task scheduling method, called MRFOSSA, for optimizing the scheduling of IoT application tasks in cloud computing. This method uses a hybrid swarm intelligence approach, utilizing a modified Manta ray foraging optimization algorithm and the salp swarm algorithm, to improve local search ability and outperform other metaheuristic techniques.