A study of resource management and scheduling techniques in cloud computing environment

A distributed resource monitoring and scheduling system was designed to optimize cloud computing resource allocation. Through the implementation of an improved ant colony algorithm and dynamic voltage frequency scaling, the system achieves efficient load balancing and energy optimization. Tests on 1000 servers demonstrate that the improved load balancing algorithm reduces response time by 25.3%, while energy-aware scheduling decreases peak energy consumption by 18.7%. The system has operated in production for 3 months, handling 10,000 concurrent users with 99.999% availability. This research provides valuable insights into large-scale cloud resource management and contributes to the development of more efficient and sustainable cloud computing infrastructure.