Cloud Replica Management-Based Hybrid Optimization

This research addresses the challenges in cloud-based replica management by proposing a novel strategy employing a Genetically Implied Greywolf with Oppositional Learning (GIGOL) hybrid optimization technique. This approach optimizes multi-objectives such as response time, load balancing, availability, replication cost, and energy consumption, ensuring cost-effectiveness and energy efficiency. The GIGOL model integrates Genetic Algorithm, opposition learning, and Grey Wolf Optimization, aiming to achieve optimal replica placement. The study emphasizes resolving overhead issues through machine learning techniques for efficient cloud-based replica management. Performance evaluation showcases improvements in response time, load balancing, availability, replication cost, and energy consumption, highlighting the effectiveness of the proposed approach within budget constraints and management policies.