# Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet
###### Abstract
Determining a fair and justifiable price for cloud resources has always been a challenge due to the interplay between user satisfaction and provider profitability. Many existing pricing mechanisms fail to consider the diverse factors that influence resource allocation, such as energy consumption,fault likelihood, and resource interactions. By incorporating these parameters into a game-theoretic framework, the authors aim to create a dynamic pricing model that balances the needs of users and providers. This work proposes a comprehensive pricing mechanism to enhance the efficiency and profitability of cloud environments. In summary, the proposed game-based pricing model is a substantial advancement in cloud computing resource pricing. This study provides a comprehensive framework that is closer to real-world scenarios and addresses the limitations of existing mechanisms. The article lays the groundwork for further research integrating additional factors and refining the model to ensure its scalability and effectiveness in diverse cloud environments. This work is a valuable contribution to academic research and practical applications in the cloud computing industry. A critical gap in the field of GPU virtualization is discussed in article [7], specifically the lack of effective performance analysis tools for virtual GPUs (vGPUs). With GPUs playing an increasingly vital role in high-performance computing across diverse applications--ranging from media processing to autonomous systems--the advent of vGPUs has enabled scalable and flexible GPU resources for virtualized environments. However, existing performance tools largely overlook vGPU-based systems, leaving developers and practitioners with limited options to debug and optimize their applications. The authors are motivated to address this unmet need by introducing a novel diagnostic tool that enables detailed performance analysis of vGPU-accelerated virtual machines. This work is highly valuable for the rapid adoption of GPU virtualization in data centers and cloud platforms. Overall, this work not only fills an important gap in the field but also paves the way for further research and development in GPU virtualization. The proposed tool offers significant value to researchers and practitioners by improving the analysis, debugging, and optimization of vGPU-accelerated applications in heterogeneous computing environments. This work stands out as a practical, efficient, and extensible solution that addresses the current limitations of GPU performance analysis tools. Article [8] addresses a pressing issue in large-scale distributed systems, particularly those handling real-time, dynamic workloads such as crowdsourced image classification during natural disasters. These platforms face challenges from unexpected workload spikes, requiring robust optimization techniques to ensure rapid response times and resource efficiency. The proposed solution is timely and relevant, given the increasing reliance on crowdsourcing platforms and the importance of scalability, resource management, and efficiency in critical applications like disaster response. The focus on improving performance by dynamically optimizing platform configurations demonstrates a strong motivation to tackle real-world challenges in high-stakes scenarios. The experimental results indicate a reduction of 40% in processing time under high workloads when compared to alternative optimization methods. Overall, this work provides a robust and scalable solution to optimize the performance of P2P-based crowdsourcing platforms under dynamic workloads. Its innovative methodology, strong experimental validation, and potential for future expansion make it a valuable contribution to the field of distributed systems and real-time optimization. The rapid growth in genomic data, driven by next-generation sequencing technologies, has created significant computational challenges in sequence alignment tasks.