Energy-Aware Cloud Task Scheduling with XGBoost and Tabu Search: The XGTabu Approach

The research endeavor introduces the XGTabu optimization technique an innovative approach that boosts job scheduling in acloud computing atmosphere by combining XGBoost with Tabu search. This strategy is intended to optimize the cloud computing environment's efficiency with resources by lessening CPU and memory utilization, resulting in ultimately decreases total power consumption. The XGBoost classification method uses the Borg Google cluster dataset to identify failed tasks. Tabu Search is subsequently applied to investigate adjacent solutions for those tasks categorized into the overutilization or underutilization groups. The optimization algorithm swaps and migrates jobs, examining solutions according to the amount power and resource they consume. Results obtained demonstrate that the XGTabu strategy has greatly enhanced resource efficiency and decreased power consumption with the aid of visualization. This metaheuristic technique portrays potential for power consumption optimization and job performance maintenance in cloud infrastructure. It provides an adaptable and environment friendly approach to meet the growing need for cloud-based solutions that are low on energy.