IT systems monitoring is a crucial process for managing and orchestrating network resources, allowing network providers to rapidly detect and react to most impediment causing network degradation. However, the high growth in size and complexity of current operational networks (2022) demands new solutions to process huge amounts of data (including alarms) reliably and swiftly. Further, as the network becomes progressively more virtualized, the hosting of nfv on cloud environments adds a magnitude of possible bottlenecks outside the control of the service owners. In this paper, we propose two deep learning anomaly detection solutions that leverage service exposure and apply it to automate the detection of service degradation and root cause discovery in a cloudified mobile network that is orchestrated by ETSI OSM. A testbed is built to validate these AI models. The testbed collects monitoring data from the OSM monitoring module, which is then exposed to the external AI anomaly detection modules, tuned to identify the anomalies and the network services causing them. The deep learning solutions are tested using various artificially induced bottlenecks. The AI solutions are shown to correctly detect anomalies and identify the network components involved in the bottlenecks, with certain limitations in a particular type of bottlenecks. A discussion of the right monitoring tools to identify concrete bottlenecks is provided.

Energy-efficient computing model is a popular choice for high performance as well as throughput oriented computing ecosystems. Mobile (computing) devices are becoming increasingly ubiquitous to our computing domain, but with limited resources (true both for computation as well as for energy). Hence, workload offloading from resource-constrained mobile devices to the Edge and maybe (later) to the cloud become necessary as well as useful. Thanks to the persistent technical breakthroughs in global wireless standards (or in mobile networks) together with the almost limitless amount of resources in public cloud platforms, workload offloading is possible and cheaper. In such scenarios, Mobile Edge Computing (MEC) resources could be provisioned in proximity to the users for supporting latency-sensitive applications. Here, two relevant problems could be: i) How to distribute workload to the resource pools of MEC as well as public (multi-)clouds? ii) How to manage such resource pools effectively? To answer these problems in this paper, we examine the performance of our proposed approach using the Reliable Server Pooling (RSerPool) framework in more detail. We also have outlined the resource pool management policies to effectively use RSerPool for workload offloading from mobile devices into the cloud/MEC ecosystem.