Lightweight Self-adaptive Cloud-IoT Monitoring across Fed4FIRE+ Testbeds (LiSCIo)

Monitoring will be crucial to properly orchestrate next-gen services. Indeed, monitoring’s output can be exploited to choose where to deploy application services for the first time and to decide when and where to migrate them in case their QoS and contextual requirements cannot be satisfied by the current deployment and infrastructure state. However, only a few works have focused so far on the design and prototyping of monitoring tools for next-gen Cloud-IoT computing platforms. In this context, FogMon, described in (Brogi et al., 2019) and (Forti et al., 2021), is an open-source C++ distributed monitoring service targeting heterogeneous infrastructures along the Cloud-IoT continuum, e.g. Fog computing. FogMon monitors hardware and virtualised resources at different Cloud-IoT computing nodes, end-to-end network QoS between such nodes, as well as available IoT devices. Besides, it features a self-organising peer-to-peer overlay topology with self-restructuring mechanisms and differential monitoring updates, which feature scalability, fault-tolerance, and low communication overhead. The LiSCIo project aimed at assessing FogMon over increasing infrastructures from 20 to 40 Cloud and Edge nodes, spanning two testbeds within the Fed4Fire+ federated infrastructure. Particularly, LiSCIo implemented a new version of the service, i.e. FogMon 2.0, which was thoroughly fixed and tuned over a large number of experiments carried on Fed4Fire+ facilities. Throughout the project, data have been collected on all the measurements performed by FogMon 1.x and by FogMon 2.0 (viz. node hardware, IoT, latency, bandwidth) to assess their footprint on hardware resources and bandwidth in all settings, and the relative error on its estimates of latency and bandwidth against ground-truth configurations, enforced via GRE tunnels.