Multi-Objective Adaptive Manta-Ray Foraging Optimization for Workflow Scheduling with Selected Virtual Machines Using Time-Series-Based Prediction

Optimization problems are challenging, but the larger challenge is to deal with the energy issue of the cloud, with continuous dynamic load and fluctuating VM performance. The optimization technique aids in efficient task-resource mapping ensuring optimal resource utilization with minimum active hosts and energy consumption. Existing works focused on time-invariant and bounded VM performance with major concentration on minimizing the execution cost and time. A multi-objective adaptive manta-ray foraging optimization (MAMFO) has been proposed in the paper for efficient scheduling with optimum resource utilization and energy consumption. The paper contributes by considering the time-varying VM performance and performance prediction using a dynamic time-series based ARIMA model, filtering out the VMs with larger fluctuating possibility, and employing only the selected VMs to be scheduled using MAMFO to meet the optimization goal with minimum SLA violations. The experimental analysis improves the work efficiency (e.g., energy consumption attained to be 0.405 kWh, and 5.97% of SLA violations).