Multi-Objective Grey Wolf Optimizer Algorithm for Task Scheduling in Cloud-Fog Computing

Abstract

The revolution of IoT and its capabilities to serve various fields led to generating a large amount of data for processing. Tasks that require an instant response, especially with sensitive delay tasks send to the fog node due to the close distance, and the complex tasks transfer to the cloud data center for its huge computation and storage. However, sending tasks to the fog decreases the transmission delay. Still, it increases the energy consumption of the end users, while transferring tasks to the cloud reduces users’ energy consumption but increases the transmission delay due to the long distance; besides, assigning tasks to appropriate resources compatible with task requirements. These are the main challenges in cloud-fog computing that need to improve. Thus, this study proposed a Multi-Objectives Grey Wolf Optimizer (MGWO) algorithm to reduce the QoS objectives delay and energy consumption and held in the fog broker, which plays an essential role in distributing tasks. The simulation result verifies the effectiveness of the MGWO algorithm compared to the state-of-the-art algorithms in reducing delay and Energy consumption.