Improved Jellyfish Algorithm-based multi-aspect task scheduling model for IoT tasks over fog integrated cloud environment

Corporations and enterprises creating IoT-based systems frequently use fog computing integrated with cloud computing to harness the benefits offered by both. These computing paradigms use virtualization and a pay-as-you-go strategy to provide IT resources, including CPU, memory, network and storage. Resource management in such a hybrid environment becomes a challenging task. This problem is exacerbated in the IoT environment, as it generates deadline-driven and heterogeneous data demanding real-time processing. This work proposes an efficient two-step scheduling algorithm comprising a Bi-factor classification task phase based on deadline and priority and a scheduling phase using an enhanced artificial Jellyfish Search Optimizer (JS) proposed as an Improved Jellyfish Algorithm (IJFA). The model considers a variety of cloud and fog resource parameters, including speed, capacity, task size, number of tasks, and number of virtual machines for resource provisioning in a fog integrated cloud environment. The model has been tested for the real-time task scenario with the number of tasks considering both the smaller workload and the relatively higher workload scenario matching the real-time situation. The model addresses the Quality of Service (QoS) parameters of minimizing the batch's make-span time, lowering the batch execution costs, and increasing the resource utilization. Simulation results prove the effectiveness of the proposed model.

Automated summary

Corporations and enterprises often integrate fog computing with cloud computing in IoT-based systems to maximize resource utilization and improve service quality. This study proposes an efficient two-step scheduling algorithm to address the challenges of real-time processing in the IoT environment.