Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

Abstract

Owing to massive technological developments in Internet of Things (IoT) and cloud environment, cloud computing (CC) offers a highly flexible heterogeneous resource pool over the network, and clients could exploit various resources on demand. Since IoT-enabled models are restricted to resources and require crisp response, minimum latency, and maximum bandwidth, which are outside the capabilities. CC was handled as a resource-rich solution to aforementioned challenge. As high delay reduces the performance of the IoT enabled cloud platform, efficient utilization of task scheduling (TS) reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user job. Therefore, this article concentration on the design of an oppositional red fox optimization based task scheduling scheme (ORFO-TSS) for IoT enabled cloud environment. The presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud platform. It achieves the makespan by performing optimum TS procedures with various aspects of incoming task. The designing of ORFO-TSS method includes the idea of oppositional based learning (OBL) as to traditional RFO approach in enhancing their efficiency. A wide-ranging experimental analysis was applied on the CloudSim platform. The experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.

Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm

“…In the work conducted by 15 , a task scheduling scheme called ORFO-TSS, which is based on CloudSim and incorporates IoT capabilities, utilizing the oppositional red fox optimization algorithm is developed. The allocation of cloud-based Internet of Things (IoT) resources posed a significant challenge, which was effectively addressed through the implementation of the ORFO-TSS paradigm.…”