Cloud systems are large scalable distributed systems that must be carefully monitored to timely detect problems and anomalies. While a number of cloud monitoring frameworks are available, only a few solutions address the problem of adaptively and dynamically selecting the monitored indicators, based on the actual needs of the operator. Unfortunately, these solutions are either limited to infrastructure-level indicators or technology-specific, for instance, they are designed to work with OpenStack only. This paper presents the VARYS monitoring framework, a technology-agnostic Monitoring-as-a-Service solution that can monitor KPIs at all levels of the Cloud stack, including the applicationlevel. Operators use VARYS to indicate their monitoring goals declaratively, letting the framework to perform the operations necessary to achieve a requested monitoring configuration automatically. Interestingly, the VARYS architecture is general and extendable, and can be used to support increasingly more platforms and probing technologies.

Cloud systems are complex, large, and dynamic systems whose behavior must be continuously analyzed to timely detect misbehaviors and failures. Although there are solutions to flexibly monitor cloud systems, cost-effectively controlling the anomaly detection logic is still a challenge. In particular, cloud operators may need to quickly change the types of detected anomalies and the scope of anomaly detection, for instance based on observations. This kind of intervention still consists of a largely manual and inefficient ad-hoc effort.In this paper, we present Anomaly Detection as-a-Service (ADaaS), which uses the same as-a-service paradigm often exploited in cloud systems to declarative control the anomaly detection logic. Operators can use ADaaS to specify the set of indicators that must be analyzed and the types of anomalies that must be detected, without having to address any operational aspect. Early results with lightweight detectors show that the presented approach is a promising solution to deliver better control of the anomaly detection logic.

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Network Function Virtualization has established itself as one of the most important paradigms towards softwarebased networking. While today Virtual Network Functions (VNFs) are typically deployed in the form of serverful virtualmachine or container-based applications, the emergence of serverless computing opens the door to the possibility of implementing them as serverless functions, with benefits in terms of scalability and resource efficiency. This paper aims to assess whether this really makes sense or not, given the system-level overheads that a serverless computing platform naturally brings.We propose an open source platform designed to optimize the execution of network-intensive VNFs and we implement a data-plane and a control-plane function (i.e., NAT and DHCP responder, respectively) as serverless functions. We carry out extensive benchmarking of performance with their serverful counterparts, implemented as stand-alone containerized applications. Our experience makes it possible to conclude that serverless computing is beneficial for the execution of short-lived and request-based control-plane VNFs, while it should be avoided for the execution of data-plane traffic-intensive VNFs.