NSF Award
2400459
Non-Technical Abstract: Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectiveness, improves research and teaching at the home institution, and involves undergraduate students in research experiences. The award to Florida Agricultural and Mechanical University (FAMU) supports research on developing robust and efficient resource allocation and scheduling systems in clouds. This project provides an opportunity to train undergraduate and graduate students, thereby contributing to the next generation of scientists who can contribute to scientific innovation, creativity, and productivity.<br/><br/>Technical Abstract: An efficient resource allocation and scheduling system helps to improve resource utilization and throughput while ensuring Service Level Objective (SLO) availability, which is crucial to cloud providers for high profit. The goal of this project is to develop a robust and efficient cooperative resource allocation and scheduling system to help cloud providers achieve high profit by improving resource utilization and throughput while ensuring SLO availability and robustness. The project breaks new ground by incorporating machine learning into cloud computing and designing and implementing a novel resource allocation and scheduling system for achieving high resource utilization and throughput while improving SLO availability and robustness in clouds, and it identifies the root cause of low resource utilization and enables new understanding of machine learning in optimizing resource allocation and scheduling in clouds. This research produces innovation in resource allocation and scheduling, and the results of this research help to advance theories, concepts, and methods in cloud computing. Specific aims of this project are to: 1) develop an efficient machine learning based resource allocation system consisting of cooperative demand-based resource allocation and cooperative opportunistic-based resource allocation for high resource utilization while ensuring SLO availability in clouds; 2) develop a robust machine learning based scheduling system for improving throughput and failure resilience; 3) develop cutting-edge educational training modules to train students at FAMU; 4) hold faculty capability-building workshops to train faculty in methodology for conducting studies on cloud computing and machine learning with an emphasis on resource allocation and scheduling; and 5) provide educational opportunities for undergraduate and graduate students, collaborate with other departments for underrepresented student recruitment, and reach out to K-12 students. This project can help to expand the FAMU capacity in cloud computing and machine learning and create new opportunities for STEM students at FAMU to engage in research, equipping them with interdisciplinary knowledge, thereby enhancing their competitiveness for the STEM workforce.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
