Developing a Smart and Adaptive Load-Balancing System in Cloud Computing with QoS Optimization

Algorithm for resource provisioning and cloud assignment for providing content in cloud based content delivery network is developed where dynamic use of large and small scale fluctuations in user demand rates are considered along with minimum lease period constraint. Virtual machines are simulated computers within a physical computer that can run their operating systems and applications. Intelligent Resource Allocation (IRA) is a QoS-aware resource allocation technique that uses advanced technologies like machine learning and artificial intelligence to optimize resource distribution in real-time. Profit-driven scheduling algorithms for cloud public service (PS) scheduling focus on optimizing resource allocation to maximize profit while prioritizing tasks related to public services. First-Come-First- Serve is the scheduling technique in which incoming tasks are served according to the first come first served procedure and the first arriving task is served first. Using entropy-based adaptive neuro-fuzzy inferences system (E-ANFIS) that combines fuzzy logic and neural networks, many factors can be analyse in real- basic for optimizing resource allocation and utilization. The conclusions established that the proposed technique, E-ANFIS, exhibits superior performance to the others, achieving an energy efficiency of 89%. Future works of this study can be direct more research efforts towards enhancing the application of the framework to multi-cloud and edge computing to integrate and optimise resources within different computing paradigms.