Complexity of current computer networks, including e.g., local networks, large structured networks, wireless sensor networks, datacenter backbones, requires a thorough study to perform analysis and support design. Simulation is a tool of paramount importance to encompass all the different aspects that contribute to design quality and network performance (including as well energy issues, security management overheads, dependability), due to the fact that such complexity produces several interactions at all network layers that is not easily modellable with analytic approaches. In this systematic literature review we aim to analyze, basing our investigation on available literature, the adoption of a popular network simulator, namely ns-3, and its use in the scientific community. More in detail, we are interested in understanding what are the impacted application domains in which authors prefer ns-3 to other similar tools and how extensible it is in practice according to the experience of authors. The results of our analysis, which has been conducted by especially focusing on 128 papers published between 2009 to 2019, reveals that 10% of the evaluated papers were discarded because they represented informal literature; most of the studies presented comparisons among different network simulators, beyond ns-3 and conceptual studies related to performance assessment and validation and routing protocols. Only about 30% of considered studies present extensions of ns-3 in terms of new modules and only about 10% present effective case studies demonstrating the effectiveness of employing network simulator in real application, except conceptual and modeling studies.

Big Data applications allow to successfully analyze large amounts of data not necessarily structured, though at the same time they present new challenges. For example, predicting the performance of frameworks such as Hadoop and Spark can be a costly task, hence the necessity to provide models that can be a valuable support for designers and developers. Big Data systems are becoming a central force in society and the use of models can also enable the development of intelligent systems providing Quality of Service (QoS) guarantees to their users through runtime system reconfiguration. This paper provides a new contribution in studying a novel modeling approach based on fluid Petri nets to predict MapReduce and Spark applications execution time which is suitable for runtime performance prediction. Models have been validated by an extensive experimental campaign performed at CINECA, the Italian supercomputing center, and on the Microsoft Azure HDInsight data platform. Results have shown that the achieved accuracy is around 9.5% for Map Reduce and about 10% for Spark of the actual measurements on average.

The design and the requirements of modern computer-based systems have reached a complexity level that calls for the use of models for the verification of non functional requirements since the beginning of their design cycle. Such systems are however too complex to be modeled directly in a simple unstructured formal language like Queueing Networks or Petri Nets. SIMTHESys (Structured Infrastructure for Multiformalism modeling and Testing of Heterogeneous formalisms and Extensions for SYStems) is a novel approach to multiformalism compositional modeling, that is based on the possibility of freely specifying the dynamics of the elements of a formal modeling language in an open framework. This is obtained by the application of consolidated metamodeling foundations to the description of models, together with the concept of behavior as a bridge between formalism dynamics and solution techniques. In this paper the main concepts of the SIMTHESys approach are presented, together with a running example of how SIMTHESys copes with performance evaluation of multiformalism models.