AI-Based Information Transfer Scheduling in Cloud for Big Data Applications
Dr. Farheen Mohammed*
Department of Computer Science and Engineering of Lords Institute of Engineering & Technology, Hyderabad, Telangana - 500091
E-mail: farheen0122@gmail.com
ORCID iD: https://orcid.org/0000-0003-0658-6412
*Corresponding author
Dr. Khaja Mizbahuddin Quadry
Department of Computer Science and Engineering of Lords Institute of Engineering & Technology, Hyderabad, Telangana - 500091
E-mail: quadry1973@gmail.com
ORCID iD:https://orcid.org/0009-0004-7889-2376
Essam Azeemuddin
Department of Computer Science and Engineering of Lords Institute of Engineering & Technology (LIET), Hyderabad, Telangana - 500091
E-mail:essamazeemuddin@gmail.com
ORCID iD:https://orcid.org/ 0009-0008-8913-7535
Abstract: In the era of big data, the efficient processing and transfer of massive volumes of data have become paramount. Cloud computing offers a scalable and cost-effective solution for handling big data, but optimizing the transfer of information within cloud environments remains a challenging task. This research paper presents an AI-based approach to information transfer scheduling in the cloud specifically tailored for big data applications. The proposed methodology leverages machine learning algorithms to dynamically schedule data transfers based on various factors such as network conditions, data size, and computational resources availability. Through extensive simulations and experiments, we demonstrate the effectiveness of our approach in improving data transfer efficiency, reducing latency, and enhancing overall system performance. The results showcase the potential of AI techniques in optimizing information transfer in cloud environments, thereby facilitating more efficient utilization of resources for big data processing tasks.
Index Terms: AI, Cloud Computing, Big Data, Information Transfer, Scheduling, Machine Learning
