An optimised AI-driven swarm-based enhanced task scheduling model for cloud computing environment

Task scheduling in cloud computing environment becomes difficult when complexity level of dispute, including task count and computing resources, rises with user growth. Solving this, an enhanced task scheduling (ETS) model with optimised artificial intelligence driven by swarm is proposed in paper. In proposed method, supervised machine learning algorithm, artificial neural networks (ANN) with swarm-based moth flame optimisation (MFO) is used to balance scheduling. MFO optimises by separating out virtual machines (VMs) considering basic properties like CPU utilisation, memory and bandwidth. ETS model is optimised based on resource allocation and balancing issues using back-propagation algorithm (BPA) with ANN (ANN-BPA) to analyse scheduling and problem identification mechanism. Efficiency of ETS model is assessed, focusing on aspects such as task allocation, task completion, execution time and energy consumption. The ANN-BPA-based task scheduling model outperforms by present technique and ANN-based model, which enhances resource utilisation by 7.54% and decreases completion time by 0.6 s.