Research Article
EEMS - Examining the Environment of the Job Metaverse Scheduling for Data Security
@INPROCEEDINGS{10.1007/978-3-031-48888-7_20, author={Venkata Naga Rani Bandaru and P. Visalakshi}, title={EEMS - Examining the Environment of the Job Metaverse Scheduling for Data Security}, proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I}, proceedings_a={IC4S}, year={2024}, month={1}, keywords={Scheduling Cloud Computing Solution data security threat detection Framework}, doi={10.1007/978-3-031-48888-7_20} }- Venkata Naga Rani Bandaru
P. Visalakshi
Year: 2024
EEMS - Examining the Environment of the Job Metaverse Scheduling for Data Security
IC4S
Springer
DOI: 10.1007/978-3-031-48888-7_20
Abstract
Job scheduling is one of the main barriers to achieving resource efficiency and cost-effective execution in the cloud computing environment. Finding a nearly perfect solution in a fair quantity of time is challenging when it comes to work scheduling. Consequently, the delayed convergence and local minimums are still existing. This EEMS - examining the environment of the job metaverse schooling for data security proposed method describes a new secure job scheduler to meet the challenge of scheduling jobs in a cloud computing environment. This method is based on secure differential evolution, and Cloud Sim has been used in numerous tests to show that EEMS works. This innovative method lays a heavy emphasis on upholding data security and integrity throughout the scheduling process in addition to attempting to maximize resource usage and save costs in cloud computing settings. This combination of secure differential evolution and EEMS principles offers a strong solution that looks to address remaining issues with cloud-based task scheduling.
