Task scheduling in multi-cloud environment via improved optimisation theory

As one of the most popular technologies nowadays, cloud computing has a big demand in the distributed software space. It is highly difficult for CSPs to work together in a multi-cloud context, and contemporary literature does not adequately address this issue. In this work, a protected TS paradigm in a multi-cloud environment is introduced. The suggested scheme mainly focuses on the optimal scheduling of tasks by considering a modified Deep Neural Network (DNN) as a task scheduler. Accordingly, the task is allotted based upon ‘makespan, execution time, security constraints (risk assessment), utilisation cost, maximal Service Level Agreement (SLA) adherence and Power Usage Effectiveness (PUE)’. Moreover, the weights of DNN are tuned optimally by Self-Improved Aquila Optimisation (SI-AO) technique. The developed model has obtained a lower MAE value = 0.052581 which is 46.67%, 90.85%, 89.29% and 86.43% better than DNN, NN, RNN and LSTM, respectively. Copyright © 2024 Inderscience Enterprises Ltd.