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

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