A Hybrid Many-Objective Optimization Algorithm for Job Scheduling in Cloud Computing Based on Merge-and-Split Theory

Keyphrases

• Cloud Computing 100%
• Heuristic Approach 100%
• Job Scheduling 100%
• Many-objective Optimization Algorithm 100%
• Split-and-merge 100%
• Quality of Service 50%
• Energy Cost 50%
• Service Provider 50%
• Completion Time 50%
• Evaluation Metrics 50%
• Cloud Environment 50%
• Cloud Server 50%
• Search Process 50%
• Load Level 50%
• CloudSim 50%
• Overall System 50%
• Existing State 50%
• Cooperative Game Theory 50%
• Process Efficiency 50%
• Search Space 50%
• Evolutionary Algorithms 50%
• Excellent Performance 50%
• Local Optimum 50%
• High Utilization 50%
• Bandwidth Utilization 50%
• Grey Wolf Optimization 50%
• SLA Violation 50%
• Provider Outcomes 50%
• Scheduling Jobs 50%
• Software Version 50%
• Sunflower Optimization Algorithm 50%
• Search Domain 50%
• Time Latency 50%
• Process Optimizing 50%
• Pollination 50%
• Non-deterministic Polynomial 50%
• Asset Utilization 50%
• Many-objective Optimization Problems (MaOPs) 50%
• Polynomial Problem 50%
• Slow Convergence 50%
• Many-objective 50%

Computer Science

• Cloud Computing 100%
• Job Scheduling 100%
• Optimization Algorithm 100%
• Many-Objective Optimization 100%
• Metaheuristics 66%
• Objective Function 33%
• Completion Time 33%
• Optimization Problem 33%
• Evaluation Metric 33%
• Cloud Environment 33%
• Service-Level Agreement 33%
• cooperative game theory 33%
• Search Space 33%
• Conventional Method 33%
• Grey Wolf Optimization 33%
• Service Provider 33%
• Evolutionary Algorithm 33%
• Slow Convergence 33%
• Bandwidth Utilization 33%
• Outstanding Performance 33%