# Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet
###### Abstract  
Cloud computing and High-Performance Computing (HPC) have become emerging paradigms that drive innovation and reshape the landscape of Information and Communication Technology (ICT). As cloud technologies and service models continue to evolve, adopting cloud-first with Artificial Intelligence (AI) strategies has become a critical enabler for businesses aiming to harness the full potential of transformative applications. To address the critical challenges and advances in cloud computing and HPC technologies, this Special Issue brings together breakthrough research and state-of-the-art methodologies to address the emerging issues of low latency, high-throughput computing, scalable storage, and efficient resource management in rapidly evolving technologies. By exploring key topics such as AI-driven optimization, dynamic orchestration of distributed systems, pricing strategies for cloud services, and performance enhancements for emerging workloads, this collection aims to inspire researchers, industry professionals, and decision-makers alike. Through cutting-edge research and innovative solutions, the contributions in this collection shape the next generation of Internet-enabled applications. First of all, a critical aspect of modern IT systems--High Availability (HA)--is discussed in the article [1]. HA is essential for ensuring uninterrupted access to services and applications. With the proliferation of container-based architectures, understanding and comparing HA solutions across different platforms is vital for businesses and organizations seeking to optimize performance, reliability, and scalability. This study conducts a comparative analysis of three widely used Linux container platforms: Docker, Kubemetes, and Proxmox. The methodology ensures a robust evaluation of container HA solutions, providing actionable insights for organizations. This work is motivated by the need to evaluate HA solutions to help organizations select the best container platform for their specific use cases, considering key metrics like service recovery time, data transfer rates, and call failures. In summary, this study thoroughly analyzes HA solutions for Linux containers, offering valuable insights for both research and practical applications. By highlighting the strengths and weaknesses of Docker, Kubemetes, and Proxmox, this work enables organizations to make informed decisions about container platform selection. The forward-looking focus on emerging challenges, such as security, scalability, and cost optimization, ensures relevance in the rapidly evolving field of container-based architectures. Therefore, this work sets a strong foundation for future research and development in this critical domain. Afterward, an insightful exploration of applying Machine Learning (ML) is introduced in article [2] to predict pricing trends in the cloud services industry, particularly focusing on Reserved Instances (RI) in cloud Infrastructure-as-a-Service (IaaS) offered by Amazon Web Services (AWS). This study leverages the CatBoost algorithm to model pricing trends using historical data. A promising use case is presented for ML in optimizing cloud resource utilization and enabling more informed decision-making for cloud consumers. Overall, this work contributes significantly to the growing body of research on ML applications in cloud computing by presenting a robust methodology for predicting IaaS pricing trends. This studyfills a gap in understanding pricing dynamics across multiple instance families, extending beyond narrow analyses of specific instance types. Additionally, the findings offer actionable insights for cloud consumers and IT managers aiming to optimize resource utilization and make strategic budgetary decisions. By addressing the limitations and pursuing the proposed future directions, this research can further refine predictive models to offer even greater value to organizations navigating the complex and dynamic cloud services market.