macc2026
Mobile Applications and Cloud Computing
Mobile Applications and Cloud Computing
Monday, A5-A6 12 noon -3:00 pm
Friday, A5-A6 12 noon -2:00 pm
26.9 [2,58]: Introduction to the course [slide 01]
29.9 [3,55]: Review of main distributed interaction patterns; NIST's cloud computing definition [slides 01]
3.10 [2,53H]h: Review of interaction patterns [slides 02]
7.10 [3,50]: Cloud computing: enabling technologies part 1 [Ref. 1, chapter 3,P1] [slides 03]
10.10 [2,48]h: Cloud computing: enabling technologies part 2 [Ref. 1, chapter 3,P1] [slides 04]
13.10 [3,45]: Docker [slides 05]
17.10 [2,43]h: PaaS and SaaS [slides 06]
20.10 [3,40]: Advanced resource management [slides 07]
24.10 [2,38]: Edge computing [slides 08]
27.10 [3,35]: Hands-on GCP [slides 09]
30.10 [2,33]: Android framework [slides 10]
3 [3,30]: The Presentation layers [slides 11]
7 [2,28]: Navigation and Intents [slide 12]; Coroutines [slides 13]
10 [3,25]: The Domain layer [slides 14]
14 [2,23]: The data layer
17 [3,20]: The Domain layer [slides 14]
21 [2,18]: The Domain layer [slides 14]
24 [3,15]: The data layer and data sources [slides 15_1]
28 [2,17]: Sensors and orientation and compass
1[3,10]: 2D graphics
5[2,8]: examples
8[3,5]: ML kit
12[2,3]: Introduction to AR
15[3,0]: DTCM and load balancing ?
Ref 1. Kai Hwang, Jack Dongarra, and Geoffrey C. Fox. 2011. Distributed and Cloud Computing: From Parallel Processing to the Internet of Things (1st. ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
GPS-Essential of Satellite Navigation (Chapter 1,2,3)
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
Agache et al. "Firecracker: lightweight virtualisation for serverless applications", 17th USENIX Symposium on Networked Systems Design and Implementation, NSDI'20
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
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)
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.
T. Theodoropoulos, et al "GraphOpticon: A Global proactive horizontal autoscaler for improved service performance and resource consumption", Future Generation Computer Systems, Volume 174, 2026
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
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)
A. Gómez-González, Carmen Carrión, M. B. Caminero, "Enhancing fog IoT container deployment: A customizable Kubernetes scheduler, Future Generation Computer Systems, Volume 176, 2026
R. Kesavan, et al: "Firestore: The NoSQL Serverless Database for the Application Developer" IEEE 39th International Conference on Data Engineering (ICDE), 2023
G. Aceto, G. Bovenzi, D. Ciuonzo, A. Montieri, V. Persico and A. Pescapé, "Characterization and Prediction of Mobile-App Traffic Using Markov Modeling," in IEEE Transactions on Network and Service Management, vol. 18, no. 1, pp. 907-925, March 2021, doi: 10.1109/TNSM.2021.3051381.
J. O. Kephart et al., "Coordinating Multiple Autonomic Managers to Achieve Specified Power-Performance Tradeoffs," Fourth International Conference on Autonomic Computing (ICAC'07), Jacksonville, FL, USA, 2007, pp. 24-24, doi: 10.1109/ICAC.2007.12.
Ricardo Bianchini, et al., "Toward ML-Centric Cloud Platforms", Communications of the ACM, February 2020, Vol. 63 No. 2, Pages 50-59
Khan, Tahseen & Tian, Wenhong & Buyya, Rajkumar. (2021). Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions, Journal of Network and Computer Applications, Volume 204, August 2022, 103405
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.
Kay Ousterhout, Patrick Wendell, Matei Zaharia, and Ion Stoica. 2013. Sparrow: distributed, low latency scheduling. In Proceedings of the 24th ACM Symposium on Operating Systems Principles (SOSP '13).
Abraham, L., Allen, J., Barykin, O., Borkar, V.R., Chopra, B., Gerea, C., Merl, D., Metzler, J., Reiss, D., Subramanian, S., Wiener, J.L., & Zed, O. (2013). Scuba: Diving into Data at Facebook. Proc. VLDB Endow., 6, 1057-1067.
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.
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.
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.
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),
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,
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
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.
H Qiu et al. Reinforcement learning for resource management in multi-tenant serverless platforms, Proceedings of the 2nd European (EuroMLSys '22)