Automating Configuration and Performance Management of Data Centers

Data centers (DCs) in network softwarization and 5G eras are significantly different from those operated nowadays by public cloud providers. They are massively distributed, closer to end-users, heterogeneous (e.g., multi-access edge, central office as a data center, etc.) and rely on much more complex technologies (e.g., Network Functions Virtualization [NFV] and Software-Defined Networking [SDN]). This makes their Operation and Management (O&M) much more challenging. Much more intelligence is required for automating the various tasks. Several technologies that have recently emerged could help in this automation. Some examples are the new technologies on which data centers rely. An SDN based – management system, for instance, could assist in automating configuration and reconfiguration of intra-DC and inter-DC paths. Other examples are machine learning and big data analysis. Machine learning, for example, could aid in performance management by predicting the performance metrics for autonomic tuning of the behavior. This project aims at designing and validating architectures and models for automated performance and configuration management of large-scale, geo-distributed, highly heterogeneous, and NFV-SDN enabled-DC. An incremental approach will be followed. The first year will be devoted to configuration management and we will deal with performance management (including resource provisioning) during the second year.

Intern: 
Mohammad Abu-Lebdeh
Faculty Supervisor: 
Roch Glitho
Province: 
Quebec
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