Performance assessment of cloud applications

• Thesis title in Czech: Vyhodnocování výkonnosti cloudových aplikací
• Thesis title in English: Performance assessment of cloud applications
• Key words: edge-cloud; časové požadavky; sdílení zdrojů; optimalizace výkonnosti
• English key words: edge-cloud; real-time requirements; resource sharing; performance optimization
• Academic year of topic announcement: 2017/2018
• Thesis type: diploma thesis
• Thesis language: angličtina
• Department: Department of Distributed and Dependable Systems (32-KDSS)
• Supervisor: prof. RNDr. Tomáš Bureš, Ph.D.
• Author: hidden - assigned and confirmed by the Study Dept.
• Date of registration: 31.03.2018
• Date of assignment: 04.04.2018
• Confirmed by Study dept. on: 09.04.2018
• Date and time of defence: 01.07.2020 09:00
• Date of electronic submission: 29.05.2020
• Date of submission of printed version: 28.05.2020
• Date of proceeded defence: 01.07.2020
• Opponents: RNDr. David Bednárek, Ph.D.

Guidelines

Modern CPS and mobile applications like augmented reality or coordinated driving, etc. are envisioned to combine edge-cloud processing with real-time requirements. The real-time requirements however create a brand new challenge for cloud processing which has traditionally been best-effort. A key to guaranteeing real-time requirements is the understanding of how services sharing resources in the cloud interact on the performance level.
The objective of the thesis is to design a mechanism which helps to categorize cloud applications based on the type of their workload. This should result in specification of a model defining a set of applications which can be deployed on a single node, while guaranteeing a certain quality of the service. It should be also able to find the optimal node where the application could be deployed.

References

[1] Choy, Sharon, Bernard Wong, Gwendal Simon, and Catherine Rosenberg. “A Hybrid Edge-Cloud Architecture for Reducing on-Demand Gaming Latency.” Multimedia Systems 20, no. 5 (October 1, 2014): 503–19. https://doi.org/10.1007/s00530-014-0367-z.
[2] Xu, Minxian, Wenhong Tian, and Rajkumar Buyya. “A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing.” Concurrency and Computation: Practice and Experience 29, no. 12 (June 25, 2017): e4123. https://doi.org/10.1002/cpe.4123.
[3] Ahmed, A., and E. Ahmed. “A Survey on Mobile Edge Computing.” In 2016 10th International Conference on Intelligent Systems and Control (ISCO), 1–8, 2016. https://doi.org/10.1109/ISCO.2016.7727082.
[4] Orsini, Gabriel, Dirk Bade, and Winfried Lamersdorf. “Cloudaware: A Context-Adaptive Middleware for Mobile Edge and Cloud Computing Applications.” In Foundations and Applications of Self* Systems, IEEE International Workshops On, 216–221. IEEE, 2016.
[5] Verboven, S., K. Vanmechelen, and J. Broeckhove. “Network Aware Scheduling for Virtual Machine Workloads with Interference Models.” IEEE Transactions on Services Computing 8, no. 4 (July 2015): 617–29. https://doi.org/10.1109/TSC.2014.2312912.
[6] Östberg, P. O., J. Byrne, P. Casari, P. Eardley, A. F. Anta, J. Forsman, J. Kennedy, et al. “Reliable Capacity Provisioning for Distributed Cloud/Edge/Fog Computing Applications.” In 2017 European Conference on Networks and Communications (EuCNC), 1–6, 2017. https://doi.org/10.1109/EuCNC.2017.7980667.
[7] Wang, S., M. Zafer, and K. K. Leung. “Online Placement of Multi-Component Applications in Edge Computing Environments.” IEEE Access 5 (2017): 2514–33. https://doi.org/10.1109/ACCESS.2017.2665971.
[8] Oueis, J., E. C. Strinati, and S. Barbarossa. “The Fog Balancing: Load Distribution for Small Cell Cloud Computing.” In 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), 1–6, 2015. https://doi.org/10.1109/VTCSpring.2015.7146129.
[9] Jia, Mike, Weifa Liang, and Zichuan Xu. “QoS-Aware Task Offloading in Distributed Cloudlets with Virtual Network Function Services.” In Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems, 109–116. MSWiM ’17. New York, NY, USA: ACM, 2017. https://doi.org/10.1145/3127540.3127561.