Extended : Multi-Objective Resource Optimisation using Enhanced FPA-DRL in a Heterogeneous Cloud Computing Environment
Abstract
Our proposed model is a multi-objective resource optimisation technique named MOROT used as MOROT enhanced flower pollination algorithm deep reinforcement learning (EFPA-DRL) for the purpose to handle the request having multi-objective.The EPFA algorithm, based on an extended DRL method, allows for the simultaneous optimisation of a large number of resources.The proposed model divided in 2 steps, first step is service analyser analyses the jobs, the cybershake seismogram prepares table of jobs based on size and time, minimum execution time (MET) create execution time based queue, In the second step jobs are received by EFPA-DRL for optimising local global region based on dynamic switching property.We used both synthetic and actual parallel workloads to evaluate the CloudSim toolset.
Fields of Study & Subjects
Cloud Computing and Resource Management
Distributed and Parallel Computing Systems
Energy Efficiency in Computing
Publication details
- Journal / source
- International Journal of Artificial Intelligence Governance and Human Rights
- Volume
- 1
- Issue
- 1
- Pages
- 109–130
- Publication date
- 1 January 2026
- Language
- EN