Matteo Orrù

Mariano Ceccato, Davide Corradini, Luca Gazzola et al.

The ecosystem in which mobile applications run is highly heterogeneous and configurable. All layers upon which mobile apps are built offer wide possibilities of variations, from the device and the hardware, to the operating system and middleware, up to the user preferences and settings. Testing all possible configurations exhaustively, before releasing the app, is unaffordable. As a consequence, the app may exhibit different, including faulty, behaviours when executed in the field, under specific configurations. In this paper, we describe a framework that can be instantiated to support in-vivo testing of a mobile app. The framework monitors the configuration in the field and triggers in-vivo testing when an untested configuration is recognized. Experimental results show that the overhead introduced by monitoring is unnoticeable to negligible (i.e., 0-6%) depending on the device being used (high- vs. low-end). In-vivo test execution required on average 3s: if performed upon screen lock activation, it introduces just a slight delay before locking the device.

Marco Mobilio, Matteo Orrù, Oliviero Riganelli et al.

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.

Bela Berde, Steven Van Rossem, Aurora Ramos et al.

This paper presents two new challenges for the Telco ecosystem transformation in the era of cloud-native microservice-based architectures. (1) Development-for-Operations (Dev-for-Operations) impacts not only the overall workflow for deploying a Platform as a Service (PaaS) in an open foundry environment, but also the Telco business as well as operational models to achieve an economy of scope and an economy of scale. (2) For that purpose, we construct an integrative platform business model in the form of a Multi-Sided Platform (MSP) for building Telco PaaSes. The proposed MSP based architecture enables a multi-organizational ecosystem with increased automation possibilities for Telco-grade service creation and operation. The paper describes how the Dev-for-Operations and MSP lift constraints and offers an effective way for next-generation PaaS building, while mutually reinforcing each other in the Next Generation Platform as a Service (NGPaaS) framework.

Matteo Orrù, Marco Mobilio, Anas Shatnawi et al.

Smart Cities are future urban aggregations, where a multitude of heterogeneous systems and IoT devices interact to provide a safer, more efficient, and greener environment. The vision of smart cities is adapting accordingly to the evolution of software and IoT based services. The current trend is not to have a big comprehensive system, but a plethora of small, well integrated systems that interact one with each other. Monitoring these kinds of systems is challenging for a number of reasons.

Anas Shatnawi, Matteo Orrù, Marco Mobilio et al.

Cloud systems are complex and large systems where services provided by different operators must coexist and eventually cooperate. In such a complex environment, controlling the health of both the whole environment and the individual services is extremely important to timely and effectively react to misbehaviours, unexpected events, and failures. Although there are solutions to monitor cloud systems at different granularity levels, how to relate the many KPIs that can be collected about the health of the system and how health information can be properly reported to operators are open questions. This paper reports the early results we achieved in the challenge of monitoring the health of cloud systems. In particular we present CloudHealth, a model-based health monitoring approach that can be used by operators to watch specific quality attributes. The CloudHealth Monitoring Model describes how to operationalize high level monitoring goals by dividing them into subgoals, deriving metrics for the subgoals, and using probes to collect the metrics. We use the CloudHealth Monitoring Model to control the probes that must be deployed on the target system, the KPIs that are dynamically collected, and the visualization of the data in dashboards.

Andrea Pinna, Roberto Tonelli, Matteo Orrú et al.

A Blockchain is a global shared infrastructure where cryptocurrency transactions among addresses are recorded, validated and made publicly available in a peer- to-peer network. To date the best known and important cryptocurrency is the bitcoin. In this paper we focus on this cryptocurrency and in particular on the modeling of the Bitcoin Blockchain by using the Petri Nets formalism. The proposed model allows us to quickly collect information about identities owning Bitcoin addresses and to recover measures and statistics on the Bitcoin network. By exploiting algebraic formalism, we reconstructed an Entities network associated to Blockchain transactions gathering together Bitcoin addresses into the single entity holding permits to manage Bitcoins held by those addresses. The model allows also to identify a set of behaviours typical of Bitcoin owners, like that of using an address only once, and to reconstruct chains for this behaviour together with the rate of firing. Our model is highly flexible and can easily be adapted to include different features of the Bitcoin crypto-currency system.