Cloud-Based Secure High-Performance Application Clustering with AI Optimization

Abstract

The rapid and accelerated digitization of services, real-time apps and data-intensive platforms has placed a strong burden on scaling, secure and high performance computing platforms. Conventional monolithic server implementations usually have a hard time accommodating unforeseeable workloads, security issues, as well as latency demands. The application clustering, which is cloud-based, has proved to be a potent way of allocating the work loads to various and interconnected nodes and, as a result, enhance the availability, scalability, and performance. Nevertheless, the static clustering strategies do not usually keep up with the dynamic workload and changes in security threats. In recent years, Artificial Intelligence (AI) is being deployed into the cloud infrastructure to streamline the distribution of resources, workload distribution, and fault tolerance. Clustering with AI helps systems to examine real-time performance measurements, foresee system conduct and to optimize cluster arrangements dynamically. This will improve the responsiveness of the applications, minimize downtime, and improve the defenses against cybersecurity.