Logistic info 

Monday 12-15 Room A5-A6 - Via Ariosto,25

Wednesday 13-15 Room A5-A6 - Via Ariosto, 25

classroom CODE u3mgbyn3 

For Information on exam requirements and assessment: Please refer to Slide 1 of the course slides.

PAPERS — CHOOSE ONE FOR THE EXAM

The following papers are NOT available for the flipped classroom: 1,2,4,5,6,7,8,9,13,14,15,18,19,20,24,32

1. 1. Yuyi Mao, Xianghao Yu, Kaibin Huang, Fellow, Ying-Jun Angela Zhang, Jun Zhang, "Green Edge AI: A Contemporary Survey", Proceedings of the IEEE, vol. 112, no. 7, pp. 880-911, July 2024 

   2. G. R. Russo, E. D'Alessandro, V. Cardellini and F. L. Presti, "Towards a Multi-Armed Bandit Approach for Adaptive Load Balancing in Function-as-a-Service Systems," 2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), Aarhus, Denmark, 2024, pp. 103-108, doi: 10.1109/ACSOS-C63493.2024.00039. 

   3. H. Tran-Dang, K. -H. Kwon and D. -S. Kim, "Bandit Learning-Based Distributed Computation in Fog Computing Networks: A Survey," in IEEE Access, vol. 11, pp. 104763-104774, 2023, doi: 10.1109/ACCESS.2023.3314889. 

   4. G. R. Russo, T. Mannucci, V. Cardellini and F. L. Presti, "Serverledge: Decentralized Function-as-a-Service for the Edge-Cloud Continuum," 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom), Atlanta, GA, USA, 2023, pp. 131-140, 

   5. M. S. Aslanpour, A. N. Toosi, M. A. Cheema and M. B. Chhetri, "faasHouse: Sustainable Serverless Edge Computing through Energy-aware Resource Scheduling," in IEEE Transactions on Services Computing

   6. Agache et al. "Firecracker: lightweight virtualisation for serverless applications", 17th USENIX Symposium on Networked Systems Design and Implementation, NSDI'20

   7. R. Morabito, V. Cozzolino, A. Y. Ding, N. Beijar and J. Ott, "Consolidate IoT Edge Computing with Lightweight Virtualization," in IEEE Network, vol. 32, no. 1, pp. 102-111, Jan.-Feb. 2018

   8. T. Goethals, M. Sebrechts, M. Al-Naday, B. Volckaert and F. De Turck, "A Functional and Performance Benchmark of Lightweight Virtualization Platforms for Edge Computing," 2022 IEEE International Conference on Edge Computing and Communications (EDGE)

   9. F. Rossi, V. Cardellini, F. L. Presti and M. Nardelli, "Dynamic Multi-Metric Thresholds for Scaling Applications Using Reinforcement Learning," in IEEE Transactions on Cloud Computing, vol. 11, no. 2, pp. 1807-1821, 1 April-June 2023, doi: 10.1109/TCC.2022.3163357.

   10. T. Theodoropoulos, et al "GraphOpticon: A Global proactive horizontal autoscaler for improved service performance and resource consumption", Future Generation Computer Systems, Volume 174, 2026

   11. Ameni Kallel, Molka Rekik, Mahdi Khemakhem, A deep reinforcement learning-based optimization approach for containerized microservice scheduling in Hybrid Fog/Cloud environments, Engineering Applications of Artificial Intelligence, Volume 141, 2025

   12. Y. Zou, S. Feng, D. Niyato, Y. Jiao, S. Gong and W. Cheng, "Mobile Device Training Strategies in Federated Learning: An Evolutionary Game Approach," 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) 

   13. A. Gómez-González, Carmen Carrión, M. B. Caminero, "Enhancing fog IoT container deployment: A customizable Kubernetes scheduler, Future Generation Computer Systems, Volume 176, 2026

   14. R. Kesavan, et al: "Firestore: The NoSQL Serverless Database for the Application Developer" IEEE 39th International Conference on Data Engineering (ICDE), 2023

   15. Ricardo Bianchini, et al., "Toward ML-Centric Cloud Platforms", Communications of the ACM, February 2020, Vol. 63 No. 2, Pages 50-59

   16. A. Ali et al., "Joint Optimization of Computation Offloading and Task Scheduling Using Multi-Objective Arithmetic Optimization Algorithm in Cloud-Fog Computing," in IEEE Access, vol. 12, pp. 184158-184178, 2024, doi: 10.1109/ACCESS.2024.3512191.

   17. X. Chen, J. Cao, Y. Sahni, M. Zhang, Z. Liang and L. Yang, "Mobility-aware Dependent Task Offloading in Edge Computing: a Digital Twin-assisted Reinforcement Learning Approach," in IEEE Transactions on Mobile Computing, doi: 10.1109/TMC.2024.3506221.

   18. A. Uddin, A. H. Sakr and N. Zhang, "Adaptive Prioritization and Task Offloading in Vehicular Edge Computing Through Deep Reinforcement Learning," in IEEE Transactions on Vehicular Technology, doi: 10.1109/TVT.2024.3499962.

   19. F. Rossi, M. Nardelli and V. Cardellini, "Horizontal and Vertical Scaling of Container-Based Applications Using Reinforcement Learning," 2019 IEEE 12th International Conference on Cloud Computing (CLOUD),

   20. H. A. M. N. Balla, C. G. Sheng and J. Weipeng, "Reliability Enhancement in Cloud Computing Via Optimized Job Scheduling Implementing Reinforcement Learning Algorithm and Queuing Theory," 2018 1st International Conference on Data Intelligence and Security (ICDIS), South Padre Island, TX, USA, 2018, pp. 127-130, 

   21. M. T. Islam, S. Karunasekera and R. Buyya, "Performance and Cost-Efficient Spark Job Scheduling Based on Deep Reinforcement Learning in Cloud Computing Environments," in IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 7, pp. 1695-1710, 1 July 2022

   22. D. Cui, Z. Peng, J. Xiong, B. Xu and W. Lin, "A Reinforcement Learning-Based Mixed Job Scheduler Scheme for Grid or IaaS Cloud," in IEEE Transactions on Cloud Computing, vol. 8, no. 4, pp. 1030-1039, 1 Oct.-Dec. 2020, doi: 10.1109/TCC.2017.2773078.

   23. H Qiu et al. Reinforcement learning for resource management in multi-tenant serverless platforms, Proceedings of the 2nd European (EuroMLSys '22)

   24. H. Yu, A. A. Irissappane, H. Wang and W. J. Lloyd, "FaaSRank: Learning to Schedule Functions in Serverless Platforms," 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), Washington, DC, USA, 2021, pp. 31-40, doi: 10.1109/ACSOS52086.2021.00023. 

   25. M. Mitzenmacher, "The power of two choices in randomized load balancing," in IEEE Transactions on Parallel and Distributed Systems, vol. 12, no. 10, pp. 1094-1104, Oct. 2001, doi: 10.1109/71.963420.

1. 26. Cérin, Christophe & Sow, Mamadou & Andrès, Frédéric. (2026). Towards a Cloud Fog Edge System for Smart Building. 10.48550/arXiv.2610.01647. 

   27. Ala'anzy, Ahmad e Mukash (2025), PLATOS: A Power and Latency-Aware Task-Oriented Scheduling Strategy for Healthcare IoT in Fog Computing, 'Eurasian Journal of Mathematical and Computer Applications, 13(3), 4-21. 

   28. N. A. Abu-Taleb, A. T. Zahary, A. A. Al-Shargabi and W. A. Omar, "Urgency-Aware Reward Redesign for Quality-of-Experience-Oriented Computation Offloading in Deadline-Constrained Mobile Edge Computing," 2026 6th International Conference on Emerging Smart Technologies and Applications (eSmarTA)

   29. Jing-Yang Voon, Yao Chiang, Hung-Yu Wei, "Resource Allocation and Container Scaling for Microservices in Multi-Cluster Edge Computing System", IEEE Transactions on Network and Service Management, vol.23, pp.6244-6260, 2026. 

   30. G. R. Russo, P. Spaziani and V. Cardellini, "Towards QoS-Aware Serverless Function Offloading in the Edge-Cloud Continuum through Reinforcement Learning," 2025 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

   31. Hasan Nayir, Alper Demir, Hüseyin Kaya, "Multi-Dimensional Autoscaling in Microservices Using Reinforcement Learning", 2025 33rd International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), pp.1-8, 2025. 

   32. José Santos, Efstratios Reppas, Tim Wauters, Bruno Volckaert, Filip De Turck, "Can Reinforcement Learning be Generalized for Efficient Auto-Scaling in Containerized Clouds?", NOMS 2025-2025 IEEE Network Operations and Management Symposium,