@article {Longo2015280, title = {Dependability modeling of Software Defined Networking}, journal = {Computer Networks}, volume = {83}, year = {2015}, note = {cited By 6}, pages = {280-296}, publisher = {Elsevier}, abstract = {

Software Defined Networking (SDN) is a new network design paradigm that aims at simplifying the implementation of complex networking infrastructures by separating the forwarding functionalities (data plane) from the network logical control (control plane). Network devices are used only for forwarding, while decisions about where data is sent are taken by a logically centralized yet physically distributed component, i.e., the SDN controller. From a quality of service (QoS) point of view, an SDN controller is a complex system whose operation can be highly dependent on a variety of parameters, e.g., its degree of distribution, the corresponding topology, the number of network devices to control, and so on. Dependability aspects are particularly critical in this context. In this work, we present a new analytical modeling technique that allows us to represent an SDN controller whose components are organized in a hierarchical topology, focusing on reliability and availability aspects and overcoming issues and limitations of Markovian models. In particular, our approach allows to capture changes in the operating conditions (e.g., in the number of managed devices) still allowing to represent the underlying phenomena through generally distributed events. The dependability of a use case on a two-layer hierarchical SDN control plane is investigated through the proposed technique providing numerical results to demonstrate the feasibility of the approach. {\textcopyright} 2015 Elsevier B.V.

}, keywords = {Availability, Complex networks, Controllers, Degree of distributions, Distributed components, Electric network topology, Information dissemination, Markov processes, Networking infrastructure, Non-Markovian, Quality control, Quality of service, Random processes, Reliability, Reliability and availability, Software defined networking (SDN), Software reliability, Software-defined networkings, Stochastic models, Stochastic systems, Topology, Type expansions}, issn = {13891286}, doi = {10.1016/j.comnet.2015.03.018}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946489290\&doi=10.1016\%2fj.comnet.2015.03.018\&partnerID=40\&md5=b4f32c89d2b7b79fefcaf97082764960}, author = {Francesco Longo and Salvatore Distefano and Dario Bruneo and Marco Scarpa} } @proceedings {Distefano201377, title = {Investigating mobile crowdsensing application performance}, journal = {Proceedings of the 3rd ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (DIVANet), Co-located with the 16th ACM Int. Conf. on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)}, year = {2013}, note = {cited By 0; Conference of 3rd ACM Int. Symp. on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2013, Held in Conjunction with the 16th ACM Int. Conf. on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM 2013 ; Conference Date: 3 November 2013 Through 8 November 2013; Conference Code:101342}, pages = {77-83}, publisher = {ACM}, address = {Barcelona, Spain, 3-8 November 2013}, abstract = {

Mobile Crowdsensing (MCS) is an emerging distributed paradigm lying at the intersection between the Internet of Things and the volunteer/crowd-based approach. MCS applications are usually deployed on contributing nodes such as smart devices and mobiles, equipped by sensing resources that sample the physical environment and provide the sensed data, once filtered, aggregated and preprocessed, to the MCS application server. The MCS opportunistic approach unlocks new form of pervasive, participatory sensing applications, acquiring interests also in business contexts that call for adequate techniques and tools to drive architects and developers in MCS application design. Aim of this paper is to evaluate the performance of an MCS application though a stochastic model able to stochastically represent the overall MCS environment, thus providing a valid support to MCS application development. The Petri nets formalism is used due to its expressiveness and the capabilities to represent complex, dependent, non-Markovian, phenomena usually characterizing MCS environments. A specific MCS application is then evaluated to demonstrate the effectiveness of the proposed technique on a real case study. {\textcopyright} 2013 ACM.

}, keywords = {Application development, Application performance, Complex networks, crowdsensing, Design, Digital storage, Internet of Things (IOT), Participatory sensing applications, Performance, Petri nets, Physical environments, Stochastic models, Techniques and tools}, isbn = {9781450323581}, doi = {10.1145/2512921.2512931}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84889685098\&partnerID=40\&md5=15df22b992482173437dcd8080bbbc94}, author = {Salvatore Distefano and Francesco Longo and Marco Scarpa} }