Towards a Network Queuing Assessment for Elasticity Management of Virtualized Services

With the increasing adoption of cloud computing, microservice architecture, and network function virtualization (NFV), addressing scalability and elasticity management becomes essential. The high demand for these services challenges the research community to create new automated management techniques, from which an essential part is the detection of bottlenecks in infrastructures and application boxes. The traditional approach based on hardware resource metrics (CPU and RAM) is the most straightforward strategy, providing independence from particular applications but may not capture the application's behavior in terms of workload variations. On the other hand, using an application-oriented approach provides a significant correlation with the end-user quality of experience but needs to be tailored for each case. We propose the Network Queuing Assessment (NQA) that breaks away with this tradeoff, capturing the application's workload variations and providing a significant correlation with the end-user quality of experience. Also, similarly to CPU and RAM, it is independent of particular applications. Our performance analysis results for CPU, RAM, and NQA metrics using virtualized applications and network functions in a cloud environment confirm this approach's usefulness.