@proceedings {Bruneo2016, title = {Deploying advanced services in the $\#$SmartME infrastructure}, journal = {2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016}, year = {2016}, note = {cited By 0; Conference of 2nd IEEE International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016 ; Conference Date: 7 September 2016 Through 9 September 2016; Conference Code:124837}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, address = {Bologna; Italy; 7-9 September 2016}, abstract = {

Smart Cities can be viewed as a complex ecosystem where thousands of {\textquoteright}things{\textquoteright} (e.g., smart objects, vehicles, mobiles) interact with people in order to provide innovative services. Following this view, the $\#$SmartME project aims at creating an horizontal Cloud-based framework enabling developers and users to manage such a complex ecosystem, remotely controlling things as well as virtualizing their functions implementing a provisioning model to scatter the application logic on top of the involved smart objects and to choose with fine granularity which specific tasks to delegate to centralized Cloud infrastructure. After one year from the project kickoff, we present the main architecture by highlighting how advance services can be deployed. {\textcopyright} 2016 IEEE.

}, keywords = {$\#$SmartME, Arduino, Clouds, Ecology, Ecosystems, IaaS, OpenStack, smart cities, Stack4Things}, isbn = {9781509011315}, doi = {10.1109/RTSI.2016.7740633}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006001490\&doi=10.1109\%2fRTSI.2016.7740633\&partnerID=40\&md5=76b3333938151fc341a6f3994a693da8}, author = {Dario Bruneo and Francesco Longo and Giovanni Merlino and Antonio Puliafito and Salvatore Distefano} } @proceedings {Bruneo2016, title = {An IoT Testbed for the Software Defined City Vision: The $\#$SmartMe Project}, journal = {2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016}, year = {2016}, note = {cited By 2; Conference of 2nd IEEE International Conference on Smart Computing, SMARTCOMP 2016 ; Conference Date: 18 May 2016 Through 20 May 2016; Conference Code:122466}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, address = {St. Louis; United States; 18-20 May 2016}, abstract = {

To kickstart the process of morphing Messina into a {\guillemotleft}smart{\guillemotright} city, an explicit mission for the crowdfunded $\#$SmartME project, it is essential to set up an infrastructure of smart devices embedding sensors and actuators, to be scattered all over the urban area. An horizontal framework coupled with the Fog computing approach, by moving logic toward the {\guillemotleft}extreme{\guillemotright} edge of the Internet where data needs to be quickly elaborated, decisions made, and actions performed, is a suitable solution for data- intensive services with time-bound constraints as those usually required by citizens. This is especially true in the context of IoT and Smart City where thousands of smart objects, vehicles, mobiles, people interact to provide innovative services. We thus designed Stack4Things as an OpenStack-based framework spanning the Infrastructure-as-a-Service and Platform-as-a-Service layers. We present some of the core Stack4Things functionalities implementing a Fog computing approach towards a run- time {\guillemotleft}rewireable{\guillemotright} Smart City paradigm, by outlining node management and contextualization mechanisms, also describing its usage in terms of already supported and developed verticals, as well as a specific example related to environmental data collection through $\#$SmartME. {\textcopyright} 2016 IEEE.

}, keywords = {$\#$SmartME, Arduino, Clouds, Computation theory, IaaS, Infrastructure as a service (IaaS), Internet of Things, OpenStack, Platform as a Service (PaaS), smart cities, Stack4Things}, isbn = {9781509008988}, doi = {10.1109/SMARTCOMP.2016.7501678}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979570937\&doi=10.1109\%2fSMARTCOMP.2016.7501678\&partnerID=40\&md5=c7d8b8c0b0cd880d9c781770a5721acc}, author = {Dario Bruneo and Salvatore Distefano and Francesco Longo and Giovanni Merlino} } @article {Merlino201516314, title = {A smart city lighting case study on an OpenStack-powered infrastructure}, journal = {Sensors}, volume = {15}, number = {7}, year = {2015}, note = {cited By 0}, pages = {16314-16335}, publisher = {MDPI AG}, abstract = {

The adoption of embedded systems, mobile devices and other smart devices keeps rising globally, and the scope of their involvement broadens, for instance, in smart city-like scenarios. In light of this, a pressing need emerges to tame such complexity and reuse as much tooling as possible without resorting to vertical ad hoc solutions, while at the same time taking into account valid options with regard to infrastructure management and other more advanced functionalities. Existing solutions mainly focus on core mechanisms and do not allow one to scale by leveraging infrastructure or adapt to a variety of scenarios, especially if actuators are involved in the loop. A new, more flexible, cloud-based approach, able to provide device-focused workflows, is required. In this sense, a widely-used and competitive framework for infrastructure as a service, such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks to fit the bill, replacing current application-specific approaches with an innovative application-agnostic one. This work thus describes the rationale, efforts and results so far achieved for an integration of IoT paradigms and resource ecosystems with such a kind of cloud-oriented device-centric environment, by focusing on a smart city scenario, namely a park smart lighting example, and featuring data collection, data visualization, event detection and coordinated reaction, as example use cases of such integration. {\textcopyright} 2015 by the authors; licensee MDPI, Basel, Switzerland.

}, keywords = {AMQP, Ceilometer, CEP, Clouds, CoAP, Coordination reactions, data visualization, embedded systems, IaaS, Infrastructure as a service (IaaS), IoT, Lighting, Meteorological instruments, Mobile devices, MOM, OpenStack, REST, smart cities}, issn = {14248220}, doi = {10.3390/s150716314}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84940184863\&partnerID=40\&md5=6b5fc8b27ed3943f0529cb3323f22e88}, author = {Giovanni Merlino and Dario Bruneo and Salvatore Distefano and Francesco Longo and Antonio Puliafito and Adnan H. Al-Anbuky} } @article {Longo2015235, title = {Towards the future internet: The RESERVOIR, VISION cloud, and CloudWave experiences}, journal = {International Journal of High Performance Computing and Networking}, volume = {8}, number = {3}, year = {2015}, note = {cited By 0}, pages = {235-247}, publisher = {Inderscience Enterprises Ltd.}, abstract = {

The future internet is evolving as an internet of services, things and infrastructure, and the European Commission is supporting research projects that are contributing to its evolution. Among the others, RESERVOIR, VISION Cloud and CloudWave are three interesting examples of how infrastructures and data can be managed in an effective way and how applications can adapt to the infrastructure, and the infrastructure to the services requested, in order to improve the overall quality of the service provided. From our experience in these three successful projects, we show in this paper how they try to address the challenges described above, providing an important boost to making the future internet vision concrete. We also discuss our view of the future internet where sensors, actuators and objects are part of the cloud. Copyright {\textcopyright} 2015 Inderscience Enterprises Ltd.

}, keywords = {Adaptation, Clouds, European Commission, Future internet, IaaS, Information management, Internet, Internet of Services, IoT, Overall quality, Sensors}, issn = {17400562}, doi = {10.1504/IJHPCN.2015.071260}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84940376740\&partnerID=40\&md5=8cfa4a88d00a48cee9ec543c43d7a425}, author = {Francesco Longo and Dario Bruneo and Massimo Villari and Antonio Puliafito and Eliot Salant and Yaron Wolfsthal} } @proceedings {Merlino201521, title = {Stack4Things: Integrating IoT with OpenStack in a Smart City context}, journal = {Proceedings of the 2014 International Conference on Smart Computing Workshops (SMARTCOMP Workshops)}, year = {2014}, note = {cited By 1; Conference of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014 ; Conference Date: 5 November 2014; Conference Code:111083}, pages = {21-28}, publisher = {IEEE Computer Society}, address = {Hong Kong, China, 5 November 2014}, abstract = {

As the adoption of embedded systems, mobiles and other smart devices keeps rising, and the scope of their involvement broadens, for instance in the enablement of Smart City-like scenarios, a pressing need emerges to tame such complexity and reuse as much tooling as possible without resorting to vertical ad-hoc solutions, while at the same time taking into account valid options with regards to infrastructure management, and other more advanced functionalities. In this sense, a widely used and competitive framework for Infrastructure as a Service such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks like fitting the bill. This work thus describes rationale, efforts, and results so far achieved, for an integration of IoT paradigms and resource ecosystems with such a kind of Cloud-oriented environment, by focusing on a Smart City scenario, and featuring data collection and visualization as example use cases of such integration. {\textcopyright} 2014 IEEE.

}, keywords = {AMQP, Ceilometer, CEP, Clouds, CoAP, data visualization, embedded systems, IaaS, Infrastructure as a service (IaaS), Internet of Things, IoT, Meteorological instruments, MOM, OpenStack, REST, smart cities}, isbn = {9781479964475}, doi = {10.1109/SMARTCOMP-W.2014.7046678}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84925651440\&partnerID=40\&md5=d36947c633a2c7b011bffa40aa32db9f}, author = {Giovanni Merlino and Dario Bruneo and Salvatore Distefano and Francesco Longo and Antonio Puliafito} } @article {Ghosh20131216, title = {Modeling and performance analysis of large scale IaaS clouds}, journal = {Future Generation Computer Systems - Elsevier}, volume = {29}, number = {5}, year = {2013}, note = {cited By 10}, pages = {1216-1234}, abstract = {

For Cloud based services to support enterprise class production workloads, Mainframe like predictable performance is essential. However, the scale, complexity, and inherent resource sharing across workloads make the Cloud management for predictable performance difficult. As a first step towards designing Cloud based systems that achieve such performance and realize the service level objectives, we develop a scalable stochastic analytic model for performance quantification of Infrastructure-as-a-Service (IaaS) Cloud. Specifically, we model a class of IaaS Clouds that offer tiered services by configuring physical machines into three pools with different provisioning delay and power consumption characteristics. Performance behaviors in such IaaS Clouds are affected by a large set of parameters, e.g., workload, system characteristics and management policies. Thus, traditional analytic models for such systems tend to be intractable. To overcome this difficulty, we propose a multi-level interacting stochastic sub-models approach where the overall model solution is obtained iteratively over individual sub-model solutions. By comparing with a single-level monolithic model, we show that our approach is scalable, tractable, and yet retains high fidelity. Since the dependencies among the sub-models are resolved via fixed-point iteration, we prove the existence of a solution. Results from our analysis show the impact of workload and system characteristics on two performance measures: mean response delay and job rejection probability. {\textcopyright} 2012 Elsevier B.V. All rights reserved.

}, keywords = {Analytic modeling, Analytical models, Clouds, CTMC, Fixed-point iterations, IaaS, Infrastructure as a service (IaaS), Performance, Provisioning, Stochastic models, Stochastic systems, Submodels}, issn = {0167739X}, doi = {10.1016/j.future.2012.06.005}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84887062784\&partnerID=40\&md5=5aa7bb3aa9d27ba52b585a03501c8e18}, author = {Rahul Ghosh and Francesco Longo and Vijay K. Naik and Kishor S. Trivedi} }