Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling.

Area of Science:

• Cloud Computing
• Computer Science
• Algorithm Optimization

Background:

• Cloud computing offers remote data and service access but faces challenges in task scheduling.
• Resource heterogeneity, task diversity, and user priority complicate cloud scheduling.
• Increasing user numbers heighten the challenge of managing user priority effectively.

Purpose of the Study:

• To address the complex issue of user priority in cloud computing task scheduling.
• To enhance cloud service efficiency and reduce user request response times.
• To propose a novel scheduling approach prioritizing user needs over payment models.

Main Methods:

• An Advanced Encryption Standard (AES) algorithm was employed to manage user priorities.
• Task scheduling was implemented using a First-Come, First-Serve (FCFS) approach, independent of payment.
• Performance was evaluated against traditional methods like FFOA, DE, ABC, PSO, GA, and ETC.

Main Results:

• The proposed AES-based method demonstrated significant improvements over traditional techniques.
• Improvements ranged from 24.26% to 36.98% compared to existing methods.
• At iteration 5, the approach showed enhanced performance, with improvements between 15.20% and 36.23%.

Conclusions:

• The investigated method is highly efficient for cloud environments where user priority is critical.
• The approach ensures uninterrupted service delivery by effectively managing user requests.
• This technique offers a practical solution for optimizing cloud task scheduling with a focus on user experience.