Mobile Applications and Cloud Computing 2023-2024

Logistic info

Lessons are in presence: Monday 12:00 am-4:00 pm - aule A5-A6, Friday 12:00 am-2:00 pm aule A5-A6

Office hours: Friday 3:00 pm (send confirmation mail first)

Syllabus

• Mobile applications for Android

  1. Reference Frames and device orientation 

  2. Sensors

  3. 2D graphics basics

  4. Core components: Activity, Broadcast Receiver, Service, Content provider 

  5. Modern Android Development (MAD) 

  6. Kotlin for Java programmers 

  7. The Jetpack extension

  8. AI and ML for mobile devices 

• Cloud computing:

  1. Recap of distributed system models

  2. Basic queueing models 

  3. Cloud computing models 

  4. Resource management for cloud computing 

  5. Services for mobile apps

• Edge computing

About the exam

To pass the exam a student should:

 (1) design and develop an application for smartphones that uses at least sensors, 2D graphics, image processing, and cloud services (see slides of the first lesson for more details). The project can be developed by a single student or a group of at most three students. The code of the project should be available on GitHub (or another equivalent site) and registered before the exam here 

(2)  study and discuss one of the papers listed in the readings section. The student can pick one paper that he/she likes. The discussion of the paper is per student. The paper is not assigned and it is not exclusive: the same paper can be selected by more than one student.   

The exam includes an oral part on topics discussed during the lectures. 

Readings

1. J. Tu, C. Chen, Q. Xu, and X. Guan, "EdgeLeague: Camera Network Configuration With Dynamic Edge Grouping for Industrial Surveillance," in IEEE Transactions on Industrial Informatics, vol. 19, no. 5, pp. 7110-7121, May 2023, doi: 10.1109/TII.2022.3205938.

2. S. H. Rastegar, H. Shafiei and A. Khonsari, "EneX: An Energy-Aware Execution Scheduler for Serverless Computing," in IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2023.3290985.

3. Zhao, Yong et al. “Preemptive Multi-Queue Fair Queuing.” Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing (2019)Pages 147–158https://doi.org/10.1145/3307681.3326605

4. T. Zhang, S. Xie and O. Rose, "Real-time job shop scheduling based on simulation and Markov decision processes," 2017 Winter Simulation Conference (WSC), Las Vegas, NV, USA, 2017, pp. 3899-3907, doi: 10.1109/WSC.2017.8248100.

5. Jin, X., Hua, W. & Wang, Z. Task admission control for application service operators in mobile cloud computing. J Wireless Com Network 2020, 217 (2020). https://doi.org/10.1186/s13638-020-01827-w

6. Majid Raeis, Ali Tizghadam, Alberto Leon-Garcia, Reinforcement Learning-based Admission Control in Delay-sensitive Service Systems, arXiv:2008.09590

7. Abhishek Hazra, Pradeep Rana, Mainak Adhikari, Tarachand Amgoth, Fog computing for next-generation Internet of Things: Fundamental, state-of-the-art and research challenges, Computer Science Review, Volume 48, 2023

8. M. Satyanarayanan, P. Bahl, R. Caceres and N. Davies, "The Case for VM-Based Cloudlets in Mobile Computing," in IEEE Pervasive Computing, vol. 8, no. 4, pp. 14-23, Oct.-Dec. 2009, doi: 10.1109/MPRV.2009.82.

9. J. Zhang, F. R. Yu, S. Wang, T. Huang, Z. Liu and Y. Liu, "Load Balancing in Data Center Networks: A Survey," in IEEE Communications Surveys & Tutorials, vol. 20, no. 3, pp. 2324-2352, third quarter 2018, doi: 10.1109/COMST.2018.2816042.

10. Neil J. Gunther, A General Theory of Computational Scalability Based on Rational Functions

11. Michael David Mitzenbacher, The Power of Two Choices in Randomized Load Balancing

12. Kay Ousterhout, Patrick Wendell, Matei Zaharia, Ion Stoica Sparrow: Distributed, Low Latency Scheduling 

13. Lior Abraham et. al. Scuba: Diving Into Data at Facebook

14. L. A. Barroso and U. Hözle. “The case for energy-proportional computing.” IEEE Computer, 40(12):33–7, 24 2007.

15. 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.

16. Ricardo Bianchini, Marcus Fontoura, Eli Cortez, Anand Bonde, Alexandre Muzio, Ana-Maria Constantin, Thomas Moscibroda, Gabriel Magalhaes, Girish Bablani, Mark Russinovich, Toward ML-Centric Cloud Platforms, Communications of the ACM, February 2020, Vol. 63 No. 2, Pages 50-59

17. 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

18. 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.

19. Ashkan Yousefpour, Caleb Fung, Tam Nguyen, Krishna Kadiyala, Fatemeh Jalali, Amirreza Niakanlahiji, Jian Kong, Jason P. Jue, All one needs to know about fog computing and related edge computing paradigms: A complete survey, Journal of Systems Architecture, Volume 98, 2019

Resources

1. R. Buyya et al., "A manifesto for future generation Cloud Computing: Research directions for the next decade", ACM Comput. Surv., Vol 51, No. 5, 2019. (pdf)

2. Ashkan Yousefpour, Caleb Fung, Tam Nguyen, Krishna Kadiyala, Fatemeh Jalali, Amirreza Niakanlahiji, Jian Kong, Jason P. Jue, All one needs to know about fog computing and related edge computing paradigms: A complete survey, Journal of Systems Architecture, Volume 98, 2019, Pages 289-330

3. P. Mell, T. Grance, "The NIST definition of cloud computing", Sept. 2011. (pdf)

4. S.A. Baset, "Cloud SLAs: Present and future", SIGOPS Oper. Syst. Rev., Vol. 46, No. 2, pp. 57-66, July 2012. (pdf)

5. Steven M. La Valle, Virtual reality, (pdf)

6. Probability and Statistics with Reliability, Queuing and Computer Science Applications, Author(s): Kishor S. Trivedi, 2016 (chapters 7,8)

7. Reinforcement Learning, an introduction, Barto and Sutton [ref, chapter 3, 4.4, 6.5] 

Architecture component samples

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