Ant Colony Optimization in Cloud Computing: A Review

The worldwide IT industry has been greatly impacted by the cloud computing industry's explosive growth, which is expected to produce up to $150 billion in sales by 2014, according to Gartner. With its adaptable computational architecture, cloud computing offers clients always-available services even as web technologies progress. Nonetheless, there are still issues with cost effectiveness, load balance, and energy management. Cloud providers are very concerned about energy consumption, and solutions such as virtual machine deployment have been suggested. Ant colony optimization (ACO), which provides distributed computation and robustness, stands out as a potent technique for handling challenging optimization problems in cloud computing. In cloud computing, load balancing is essential for improving resource availability and cutting costs. In order to achieve quicker reaction times, the study suggests an enhanced load balancing architecture and algorithm that makes use of ACO. Managing energy consumption and guaranteeing SLA(Service Level Agreement) compliance are difficult tasks when dealing with Virtual Machine Placement (VMP). The GACA-VMP method works well for optimizing pheromone calculations for the best physical server amount selection because it combines the advantages of genetic and ant colony algorithms. Green cloud computing prioritizes energy-efficient techniques and is essential for sustainability. The paper offers a comprehensive overview of state-of-the-art energy-efficient algorithms and offers insights into the current environment to help researchers choose the right algorithms. The study also discusses work scheduling and suggests a multi-QoS(Quality of Service)-restriction technique for effective resource balancing and QoS assurance that combines ant colony and genetic optimization. A self-adaptive ant colony optimization methodology is used to address the dynamic nature of cloud settings, exceeding earlier approaches in terms of make span and load balancing. The numerous studies' conclusions emphasized the value of bio-inspired algorithms, especially ACO, in resolving a range of cloud computing issues. These developments support continuous optimization, improving cloud computing's performance, sustainability, and affordability.