Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm

Area of Science:

• Computer Science
• Cloud Computing
• Optimization Algorithms

Background:

• Cloud computing systems involve dynamic resource provisioning for scientific applications.
• Scheduling these applications is an NP-hard problem due to resource heterogeneity and dynamic changes.
• Existing metaheuristics lack focus on secure global scheduling.

Purpose of the Study:

• To introduce the Global League Championship Algorithm (GBLCA) for secure global task scheduling in cloud environments.
• To evaluate the performance of GBLCA against other scheduling techniques.

Main Methods:

• Implemented GBLCA for scientific application scheduling.
• Utilized the CloudSim simulator for experimental validation.
• Compared GBLCA with MinMin, MaxMin, Genetic Algorithm (GA), and Ant Colony Optimization (ACO).

Main Results:

• GBLCA achieved makespan improvements ranging from 14.44% to 46.41%.
• Demonstrated significant reductions in application scheduling response time, indicating enhanced security.
• Outperformed MinMin, MaxMin, GA, and ACO in scheduling quality.

Conclusions:

• GBLCA offers a superior scheduling solution for scientific applications in cloud computing.
• The proposed technique effectively addresses both performance and security aspects of cloud scheduling.
• GBLCA provides a robust and efficient approach for complex cloud environments.