This study explores how machine learning algorithms can be used to optimize cloud resource allocation for energy efficiency. By predicting workload demands and adjusting resources dynamically, the system reduces energy consumption, enhancing sustainability while maintaining the performance of cloud-based applications.

Olivia Sophia Mitchell, Alexander Henry Davis, Ava Amelia Thompson, Matthew Elijah Moore, Charlotte Grace Robinson

This paper discusses the integration of edge computing with IoT systems, focusing on real-time data processing for smart cities. It highlights the advantages of bringing computation closer to data sources, reducing latency and improving decision-making in applications like traffic management and resource allocation.

Lucas Nathaniel Carter, Sophia Madison Bell, Liam Oliver Harris, Amelia Grace Bennett, Nathan Andrew Wilson

This research explores the use of artificial intelligence to enhance security measures in cloud platforms. By applying machine learning and anomaly detection techniques, the system can identify and mitigate potential cyberattacks in real time, ensuring better protection for sensitive data and systems.

James Oliver Clark, Lucas Benjamin Scott, Amelia Rose Taylor, Daniel Lucas Perez, William Alexander Evans

This study investigates the application of blockchain technology to cloud computing, focusing on how decentralization can improve data security and privacy. By utilizing smart contracts and consensus algorithms, this approach enables secure and transparent data sharing and storage across distributed cloud environments.

Ethan Daniel Johnson, Mia Ava White, Noah Jackson King, Lucas Harrison Cooper, Ella Grace Carter

This paper presents a method for optimizing cloud infrastructure using AI algorithms to dynamically allocate resources based on performance needs and cost-efficiency. By analyzing resource usage patterns, the system minimizes operational costs while maximizing performance, making cloud computing more sustainable and affordable.

Charlotte Victoria Adams, Ethan Matthew White, Olivia Harper Mitchell, Gabriel Samuel Walker, Isabella Rose Bennett

This paper investigates the integration of cloud computing with edge devices for real-time data processing in IoT systems. The hybrid approach improves latency, processing power, and scalability, enabling efficient data collection, analysis, and decision-making in applications such as smart cities and autonomous vehicles.

Alexander Ryan Miller, Grace Emily Thomas, Christopher Jason Turner, Olivia Faith Harris, Aidan John Phillips