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Ryerson University  

Project 1046 - Conversion and retention of the users

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Running from 2017 to present

Conversion and retention of the users

Cloud computing is ubiquitous: more and more companies are moving the workloads into the Cloud. However, this rise in popularity challenges Cloud service providers, as they need to monitor the quality of their ever-growing offerings effectively. To address the challenge, we designed and implemented an automated monitoring system for the IBM Cloud Platform. This monitoring system utilizes deep learning neural networks to detect anomalies in near-real-time in multiple Platform components simultaneously. After running the system for a year, we observed that the proposed solution frees the DevOps team's time and human resources from manually monitoring thousands of Cloud components. Moreover, it increases customer satisfaction by reducing the risk of Cloud outages. In this project, we share our solutions' architecture, implementation notes, and best practices that emerged while evolving the monitoring system. They can be leveraged by other researchers and practitioners to build anomaly detectors for complex systems.

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Research team:

  • PI: Prof. Andriy Miranskyy, Ryerson University
  • Research Associate: Lei Zhang, Ryerson University
  • Student: Sarah Sohana, Ryerson University
  • Student: Mohammad Islam, Ryerson University
  • Student: William Pourmajidi, Ryerson University
  • IBM Project Lead (RCL): David Godwin, IBM
  • IBM Manager (RCM): John Steinbacher, IBM
  • IBM Sponsor (RCS): Pedro J. Rubio, IBM
  • IBM Contributor (RCC): Jeremy Brumer, IBM
  • IBM Contributor (RCC): Rolf Lear, IBM
  • IBM Contributor (RCC): Anthony W. Erwin, IBM
  • IBM Contributor (RCC): Steve Woolf, IBM
  • IBM Contributor (RCC): Jon Bennett, IBM

Institution:

Ryerson University   

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