Reducing Task Scheduling Time in Cloud Computing using Novel Improved Whale Optimization Algorithm over Ant Colony Algorithm

To decrease Cloud Computing Task Scheduling Time utilising Novel Improved Whale Optimisation Algorithm over Ant Colony Algorithm. A Novel Improved Whale Optimisation Algorithm schedules tasks in cloud computing. Cloud computing involves internet-based distribution of data storage, servers, databases, networking, and applications. Task scheduling data is obtained using cloudsim. We analyse, create, and implement cloud computing application framework and cloud computing infrastructures. After data import, Improved Whale Optimisation and Ant Colony Algorithm are tried. Two algorithms have two groups. Each group has 120 samples with 80% G power, 0.05% threshold, and 95% CI. Task Scheduling Time (ms) is returned for inputs. The findings come from IBM SPSS. Results indicate a significant independent sample t test (p= 0.000, p<0.05). The algorithms vary statistically. Ant Colony takes longer to schedule tasks than Improved Whale Optimisation Algorithm. According to study, the Improved Whale Optimisation Algorithm (53.6000 MS) has a shorter Task Scheduling Time than the Ant Colony Algorithm (93.1000 MS).