loading page

Monitoring Platform Evolution towards Serverless Computing for 5G and Beyond Systems
  • +7
  • Matteo Pergolesi ,
  • Ramon Perez ,
  • Priscilla Benedetti ,
  • Jaime Garcia-Reinoso ,
  • Aitor Zabala ,
  • Pablo Serrano ,
  • Mauro Femminella ,
  • Gianluca Reali ,
  • Kris Steenhaut ,
  • Albert Banchs
Matteo Pergolesi
Author Profile
Ramon Perez
Author Profile
Priscilla Benedetti
Author Profile
Jaime Garcia-Reinoso
Author Profile
Aitor Zabala
Author Profile
Pablo Serrano
Author Profile
Mauro Femminella
University of Perugia

Corresponding Author:[email protected]

Author Profile
Gianluca Reali
Author Profile
Kris Steenhaut
Author Profile
Albert Banchs
Author Profile


Fifth generation (5G) and beyond systems require flexible and efficient monitoring platforms to guarantee optimal key performance indicators (KPIs) in various scenarios. Their applicability in Edge computing environments requires lightweight monitoring solutions. This work evaluates different candidate technologies to implement a monitoring platform for 5G and beyond systems in these environments. For monitoring data plane technologies, we evaluate different virtualization technologies, including bare metal servers, virtual machines, and orchestrated containers. We show that containers not only offer superior flexibility and deployment agility, but also allow obtaining better throughput and latency. In addition, we explore the suitability of the Function-as-a-Service (FaaS) serverless paradigm for deploying the functions used to manage the monitoring platform. This is motivated by the event oriented nature of those functions, designed to set up the monitoring infrastructure for newly created services. When the FaaS warm start mode is used, the platform gives users the perception of resources that are always available. When a cold start mode is used, containers running the application’s modules are automatically destroyed when the application is not in use. Our analysis compares both of them with the standard deployment of microservices. The experimental results show that the cold start mode produces a significant latency increase, along with potential instabilities. For this reason, its usage is not recommended despite the potential savings of computing resources. Conversely, when the warm start mode is used for executing configuration tasks of monitoring infrastructure, it can provide similar execution times to a microservice-based deployment. In addition, the FaaS approach significantly simplifies the code logic in comparison with microservices, reducing lines of code to less than 38%, thus reducing development time. Thus, FaaS in warm start mode represents the best candidate technology to implements such management functions.