@inbook { 11570_3134320, title = {Systems Modelling: Methodologies and Tools}, booktitle = {Systems Modeling: Methodologies and Tools}, volume = {VII}, year = {2019}, publisher = {Antonio Puliafito, Kishor S. Trivedi}, organization = {Antonio Puliafito, Kishor S. Trivedi}, address = {Cham}, keywords = {Modeling, Performance evaluation}, isbn = {978-3-319-92377-2} isbn = {978-3-319-92378-9} pages = {1{\textendash}7}, doi = {10.1007/978-3-319-92378-9_1}, url = {https://www.springer.com/la/book/9783319923772}, author = {Puliafito, Antonio and Trivedi, Kishor S.} } @proceedings {Merlino2017213, title = {Quantitative evaluation of Cloud-based network virtualization mechanisms for IoT}, journal = {ValueTools 2016 - 10th EAI International Conference on Performance Evaluation Methodologies and Tools}, year = {2016}, note = {cited By 0; Conference of 10th EAI International Conference on Performance Evaluation Methodologies and Tools, ValueTools 2016 ; Conference Date: 25 October 2016 Through 28 October 2016; Conference Code:127816}, pages = {213-216}, publisher = {Association for Computing Machinery}, address = {Taormina; Italy; 25-28 October 2016}, abstract = {

Integration of the Internet of Things (IoT) with the Cloud may lead to a range of different architectures and solutions. Our efforts in this domain are mainly geared towards making IoT systems available as service-oriented infrastructure. Under Infrastructure-as-a-Service (IaaS) scenarios, network virtualization is a core building block of any solution, even more so for IoT-focused Cloud providers. Enabling mechanisms are required to support virtualization of the networking facilities for IoT resources that are managed by the Cloud. This work describes an approach to network virtualization based on popular off-the-shelf tools and protocols in place of application-specific logic, acting as a blueprint in the design of the Stack4Things architecture, an OpenStack-derived framework to provide IaaS-like services from a pool of IoT devices. We quantitatively evaluate the underlying mechanisms demonstrating that the proposed approach exhibits mostly comparable performance with respect to standard technologies for virtual private networks, or at least good enough for the kind of underlying hardware, e.g., smart boards, whilst still representing a more flexible solution. Copyright {\textcopyright} 2016 EAI.

}, keywords = {Application specific, Clouds, Distributed computer systems, Infrastructure as a service (IaaS), Internet of thing (IOT), Internet of Things, Network architecture, network virtualization, OpenStack, Performance evaluation, Platform as a Service (PaaS), Quantitative evaluation, Reverse tunneling, Service-oriented infrastructures, Virtual private networks, Virtual reality, Virtualization}, isbn = {9781631901416}, doi = {10.4108/eai.25-10-2016.2266600}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021354856\&doi=10.4108\%2feai.25-10-2016.2266600\&partnerID=40\&md5=61d1e54a06f72746e6e5bd90c920b1c0}, author = {Giovanni Merlino and Francesco Longo and Salvatore Distefano and Dario Bruneo and Antonio Puliafito} } @article {Bruneo20153052, title = {Modeling and Evaluation of Energy Policies in Green Clouds}, journal = {IEEE Transactions on Parallel and Distributed Systems}, volume = {26}, number = {11}, year = {2015}, note = {cited By 0}, pages = {3052-3065}, publisher = {IEEE Computer Society}, abstract = {

Following the as-a-service philosophy, a cloud service provider offers computing utilities in the form of virtual resources instantiated on top of a physical infrastructure. In order to meet business requirements still providing high-quality services, performance evaluation needs to be carefully carried out with the aim of optimizing data center utilization and increasing user satisfaction. In this context, power efficiency plays a critical role pushing service providers towards the application of innovative green strategies. In this paper, we present an analytical framework, based on stochastic reward nets, that allows to evaluate different resource allocation policies in a green cloud. A use case is shown in order to illustrate the approach, modeling scattering and saturation allocation policies and comparing them to a purely physical data center scenario. A validation of the proposed model against the CloudSim framework is presented and several numerical results are provided, demonstrating the effectiveness of the approach as a powerful tool for a cloud service provider to perform well-informed decisions about theresource allocation policies to be enforced. {\textcopyright} 1990-2012 IEEE.

}, keywords = {Allocation policies, Business requirement, cloud computing, Cloud service providers, Distributed database systems, Green Computing, High quality service, Performance evaluation, Quality control, Resource allocation, Resource allocation policy, Stochastic reward nets, Stochastic systems}, issn = {10459219}, doi = {10.1109/TPDS.2014.2364194}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84944075521\&partnerID=40\&md5=ee939ccd602bffca9ab90abdfd8285d9}, author = {Dario Bruneo and Audric Lhoas and Francesco Longo and Antonio Puliafito} } @proceedings {Bruneo201384, title = {Analytical evaluation of resource allocation policies in green IaaS clouds}, journal = {Proceedings of the IEEE 3rd International Conference on Cloud and Green Computing (CGC) Co-located with the IEEE 3rd International Conference on Social Computing and Its Applications (SCA)}, year = {2013}, note = {cited By 1; Conference of 3rd IEEE International Conference on Cloud and Green Computing, CGC 2013, Held Jointly with the 3rd IEEE International Conference on Social Computing and Its Applications, SCA 2013 ; Conference Date: 30 September 2013 Through 2 October 2013; Conference Code:102391}, pages = {84-91}, publisher = {IEEE Computer Society}, address = {Karlsruhe, Germany, 30 September - 2 October 2013}, abstract = {

Cloud systems represent the new ICT frontier where computing utilities are offered in terms of virtual instances, following the so called as-a-service philosophy. Different commercial solutions have been already put in place but several aspects need to be faced in order to provide high-value services able to meet business requirements. In particular, performance evaluation plays a critical role being strictly related to data center optimization and user satisfaction. Moreover, the use of large data centers able to respond to the high service demand has increased the attention to power efficiency, thus calling for green solutions and energy-aware strategies that jointly consider environmental and economical aspects. In order to design powerful strategies able to meet the quality of service requirements still reducing the energy costs, performance analysis frameworks are needed. In this paper, we present an analytical model, based on stochastic reward nets, that is able to easily implement resource allocation strategies in a green infrastructure as-a-service cloud. The model is organized into layers that represent the virtual resource pool and the physical machines. Different allocation algorithms (i.e., scattering, saturation) have been implemented by properly managing the coordination between the model layers. Numerical results are provided that demonstrate the effectiveness of the proposed approach. {\textcopyright} 2013 IEEE.

}, keywords = {Analytical evaluation, cloud computing, Distributed computer systems, Green Computing, Performance analysis, Performance evaluation, Quality of service, Resource allocation, Resource allocation policy, Resource allocation strategies, Service requirements, Stochastic reward nets, Stochastic systems}, isbn = {9780769551142}, doi = {10.1109/CGC.2013.21}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84893329592\&partnerID=40\&md5=369f37efc4aabea38300336d819db864}, author = {Dario Bruneo and Audric Lhoas and Francesco Longo and Antonio Puliafito} } @proceedings {268, title = {Markovian agent modeling swarm intelligence algorithms in wireless sensor networks}, journal = {PERFORMANCE EVALUATION}, volume = {69}, year = {2012}, pages = {135{\textendash}149}, abstract = {

Wireless Sensor Networks (WSN) are large networks of tiny sensor nodes that are usually randomly distributed over a geographical region. The network topology may vary in time in an unpredictable manner due to many different causes. For example, in order to reduce power consumption, battery operated sensors undergo cycles of sleeping{\textendash}active periods; additionally, sensors may be located in hostile environments increasing their likelihood of failure; furthermore, data might also be collected from a range of sources at different times. For this reason multi-hop routing algorithms used to route messages from a sensor node to a sink should be rapidly adaptable to the changing topology. Swarm intelligence has been proposed for this purpose, since it allows the emergence of a single global behavior from the interaction of many simple local agents. Swarm intelligent routing has been traditionally studied by resorting to simulation. The present paper aims to show that the recently proposed modeling technique, known as Markovian Agent Model (MAM), is suited for implementing swarm intelligent algorithms for large networks of interacting sensors. Various experimental results and quantitative performance indices are evaluated to support this claim. The validity of this approach is given a further proof by comparing the results with those obtained by using a WSN discrete event simulator.

}, keywords = {Gradient-based routing, Markovian agents, Performance evaluation, Swarm intelligence, Wireless sensor networks}, doi = {10.1016/j.peva.2010.11.007}, author = {Dario Bruneo and Marco Scarpa and Andrea Bobbio and Davide Cerotti and Marco Gribaudo} } @proceedings {Bruneo2012277, title = {Modeling energy-aware cloud federations with SRNs}, journal = {32nd International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets)}, volume = {7400 Lecture Notes in Computer Science}, year = {2012}, note = {cited By 4; Conference of 32nd International Conference on Application and Theory of Petri Nets and Concurrency, Petri Nets 2011 ; Conference Date: 20 June 2011 Through 24 June 2011; Conference Code:94089}, pages = {277-307}, publisher = {Springer-Verlag}, address = {Newcastle upon Tyne, United Kingdom, 20-24 June 2011}, abstract = {

Cloud computing is a challenging technology that promises to strongly modify the way computing and storage resources will be accessed in the near future. However, it may demand huge amount of energy if adequate management policies are not put in place. In particular, in the context of Infrastructure as a Service (IaaS) Cloud, optimization strategies are needed in order to allocate, migrate, consolidate virtual machines, and manage the switch on/switch off period of a data centre. In this paper, we present a methodology based on stochastic reward nets (SRNs) to investigate the more convenient strategies to manage a federation of two or more private or public IaaS Clouds. Several policies are presented and their impact is evaluated, thus contributing to a rational and efficient adoption of the Cloud computing paradigm. {\textcopyright} 2012 Springer-Verlag.

}, keywords = {cloud computing, Computing paradigm, Data centres, Digital storage, Energy aware, Energy conservation, Management policy, Optimization strategy, Performance evaluation, Petri nets, Quality of service, Stochastic reward nets, Stochastic systems, Storage resources, Switch-on, virtual machines}, isbn = {9783642351785}, issn = {03029743}, doi = {10.1007/978-3-642-35179-2_12}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870045120\&partnerID=40\&md5=b1e106331e417cc512bcbc713cc582de}, author = {Dario Bruneo and Francesco Longo and Antonio Puliafito} } @proceedings {Bruneo2011, title = {Evaluating energy consumption in a cloud infrastructure}, journal = {2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)}, year = {2011}, note = {cited By 4; Conference of 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2011 ; Conference Date: 20 June 2011 Through 23 June 2011; Conference Code:86401}, publisher = {IEEE Computer Society}, address = {Lucca, Italy, 20-23 June 2011}, abstract = {

Cloud computing is a challenging technology that promises to strongly modify the way computing and storage resources will be accessed in the near future. Clouds may demand huge amount of energy if adequate management policies are not put in place. Optimization strategies are needed in order to allocate, migrate, consolidate virtual machines and manage the switch on/switch off period of a data center. In this paper, we present a modeling approach based on Stochastic reward nets to investigate the more convenient strategies to manage a federation of Clouds, having in mind the final goal to reduce the overall energy consumption. Several policies are presented and their impact is evaluated, thus contributing to a rational and efficient adoption of the Cloud computing paradigm. {\textcopyright} 2011 IEEE.

}, keywords = {cloud computing, Clouds, computer systems, Computing paradigm, Data centers, Energy utilization, Management policy, Modeling approach, Multimedia systems, Optimization strategy, Performance evaluation, Quality control, Quality of service, Stochastic reward nets, Stochastic systems, Storage resources, Switch-on, virtual machines, Wireless networks}, isbn = {9781457703515}, doi = {10.1109/WoWMoM.2011.5986479}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80052709389\&partnerID=40\&md5=9d12783598805d3b668813d0d5b70057}, author = {Dario Bruneo and Francesco Longo and Antonio Puliafito} }