## 2 Requirements and Challenges in the Operationalization of the Cloud Continuum
### Cloud Resources Monitoring and Self Learning in Hybrid Environments  
Cloud resources' monitoring is a challenging task when applied to hybrid environments. Cloud providers claim to support extensive monitoring mechanisms to aid in controlling application performance and working conditions but they barely offer transparent access to actual law level metrics (e.g., Mean Time To Recover (MTTR), Mean Time Between Failures (MTBF), real time CPU consumption, etc.) nor provide common or standardized metrics that can be compared among different cloud resources types or providers. In [49], the authors identified the QoS and Service Level Agreement (SLA) assessment in complex hybrid scenarios as one of the four clear gaps of the cloud computing management, due to the lack of mechanisms to address the particularities of large-scale cloud setups with more complex environments in terms of resource heterogeneity and distribution, such as hybrid and multi-cloud scenarios. In [50], the authors proposed an approach to adapt the planning in Complex Service-Based Systems, should an SLA violation occur. However, in this dissertation the authors assumed that a violation occurs but do not support the actual monitoring nor go in depth into how the NFRs are monitored or calculated so that the violation can be identified.  
In [37] monitoring of cloud resources was identified as one of the missing functionalities in current solutions and approaches. This conclusion was extracted from an analysis of different multi-cloud abstraction solutions, as cloud brokers, cloud intermediators or cloud marketplaces which automate the assembly of complex applications, its deployment and operation on one or multiple cloud infrastructures. This approach proposes using a combination of push and pull monitoring [51] for relevant NFRs such as performance, cost, availability of location and supporting the ISO 19086.  
In [52] a cloud monitoring stack is proposed for different Cloud Services (virtual machines, databases as a service, etc.) from different providers (Amazon, Azure, ARSYS). Furthermore, it focuses the monitoring of the resources on the lower level, considering [PERSON]'s [53] definition. Nevertheless, it doesn't tackle the communication layer, which in some specific scenarios can pose relevant challenges [54]. The approach proposed in this paper tries to extend it to support the monitoring of distributed and heterogeneous (different nature) infrastructural elements. These metrics will be provided both to the self-learning and self-healing algorithms as extra input data to enhance their results.  
Monitoring and preventing QoS violations are a critical aspect of the design and planning of infrastructural services, hence for the operationalization of applications, in which predictive models play an essential role. Initial _post-mortem_ analysis techniques [55] applied to the management of these violations have made way for new approaches focused on the anticipation of the failures, first as an offline process [56] and more recently at run time [57; 58]. More recently, and due to the nature of heterogeneous distributed services especially in highly demanding environments, the Internet of Things (IoT) has become one of the main applications of stream learning [59], since it is composed of sensors and actuators connected by networks to computing systems, which manage the health and actions of connected objects or machines in real-time. One of the main problems of the Cloud Continuum paradigm referring to the prediction of QoS failures is that they are subject to configuration drifts, unauthorized changes, missing dependencies or any invisible or not monitorable environment variations that have arisen in the services. However, techniques such as concept drift [60; 61] or anomalies detection [62; 63], that could provide relevant benefits, have not attracted much attention yet in the research community.