Multi-Objective Cloud Workflow Scheduling Using Adaptive Multi-Hierarchy Optimization and Ensemble Learning
Keywords:
Cloud Workflow, Optimization, Multi-hierarchy, Virtual Machines, Resource AllocationAbstract
In this paper an adaptive and hybrid multi-hierarchy optimization (AHMHO) approach is presented for workflow scheduling for cloud computing. The objective is to optimize makespan time, energy consumption, and task distribution across multiple Virtual Machines (VMs). The simulation is conducted using the MATLAB environment. The proposed AHMHO approach is compared against Priority Based and PSO Priority Based task scheduling methods. Results show that the proposed AHMHO method achieves significant improvements in makespan time, lower ADT, stable DI, and reduced SD that makes it more efficient and scalable for cloud-based workflow scheduling for large task sets as well as with increased VMs.
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