A Chaos Engineering Approach for Improving the Resiliency of IT Services Configurations

Testing the resiliency of complex IT services deployed in hybrid Cloud scenarios is a challenging task that requires expensive and possibly destructive operations. An interesting approach lies in Chaos Engineering, a set of practices to test the resiliency of software systems running in a production...

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Bibliographic Details
Main Authors: Poltronieri, Filippo, Tortonesi, Mauro, Stefanelli, Cesare
Format: Conference Proceeding
Language:English
Subjects:
Chaos Engineering
Cloud computing
Complexity theory
Digital Twin
Digital twins
Optimization
Production
Resilience
Service Management
Software systems
Task analysis
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Summary:Testing the resiliency of complex IT services deployed in hybrid Cloud scenarios is a challenging task that requires expensive and possibly destructive operations. An interesting approach lies in Chaos Engineering, a set of practices to test the resiliency of software systems running in a production environment. However, Chaos Engineering is an expensive practice that requires the setup of complicated operations that further increase the complexity of management operations. To reduce this complexity, Chaos Engineering can benefit from the adoption of non-destructive approaches such as the definition of realistic digital twins. A digital twin is a virtual replica of a real-system on which experimenting with management configurations. This paper embraces this research avenue by extending our previous efforts to integrate Chaos Engineering techniques into an IT services management framework called ChaosTwin. ChaosTwin leverages novel methodologies and tools capable of identifying and promptly react to unexpected failures. Finally, to implement autonomous fault management, ChaosTwin defines scaling and migration policies that can quickly explore for more resilient placements of software components in case of system failures. We believe that ChaosTwin can provide useful guidance to service providers in finding cost-effective service configurations capable of minimizing the negative effects of unpredictable events.
ISSN:2374-9709
DOI:10.1109/NOMS54207.2022.9789887