Active monitoring of cloud applications

Topic Description:

The current Internet trend is to move applications, data, and computing to the cloud. This shift is affecting individuals as well as enterprise environments. This trend challenges the way applications and services are monitored, as traditional monitoring techniques such as Netflow or SNMP are unable to monitor the performance, availability and responsiveness of cloud applications. In addition, the availability of monitoring and diagnostic information from the cloud is limited and often dependent on the type of cloud and service availability.

This research topic focuses on designing active monitoring methods for cloud applications using a network of monitoring  agents that monitor the availability, performance, and security of applications in the cloud at the L3-L7 layers using modular tests. The monitoring data will be processed using machine learning methods with a focus on behavioral profiling, predictive analysis and fault detection.

References:

• Luuk Klaver, Thijs van der Knaap, Johan van der Geest, Edwin Harmsma, Bram van der Waaij, and Paolo Pileggi. 2021. Towards Independent Run-Time Cloud Monitoring. In Companion of the ACM/SPEC International Conference on Performance Engineering (ICPE '21). Association for Computing Machinery, New York, NY, USA, 21–26.
• Jacopo Soldani and Antonio Brogi. 2022. Anomaly Detection and Failure Root Cause Analysis in (Micro) Service-Based Cloud Applications: A Survey. ACM Comput. Surv. 55, 3, Article 59 (March 2023), 39 pages.
• H. Won and Y. Kim, "Performance Analysis of Machine Learning Based Fault Detection for Cloud Infrastructure," 2021 International Conference on Information Networking (ICOIN), Jeju Island, Korea (South), 2021, pp. 877-880, doi: 10.1109/ICOIN50884.2021.9333875.