@proceedings {Dautov2018792, title = {Pushing intelligence to the edge with a stream processing architecture}, journal = {Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017}, year = {2017}, note = {cited By 3; Conference of Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017 ; Conference Date: 21 June 2017 Through 23 June 2017; Conference Code:134517}, pages = {792-799}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, address = {Exeter, UK - 21-23 June 2017}, abstract = {

The cloud computing paradigm underpins the Internet of Things (IoT) by offering a seemingly infinite pool of resources for processing/storing extreme amounts of data generated by complex IoT systems. The cloud has established a convenient and widely adopted approach, where raw data are vertically offloaded to cloud servers from resource-constrained edge devices, which are only seen as simple data generators, not capable of performing more sophisticated processing activities. However, there are more and more emerging scenarios, where the amount of data to be transferred over the network to the cloud is associated with increased network latency, making the results of the computation obsolete. As various categories of edge devices are becoming more and more powerful in terms of hardware resources - specifically, CPU and memory - the established way of off-loading computation to the cloud is not always seen as the most convenient approach. Accordingly, this paper presents a Stream Processing architecture for spreading workload among a local cluster of edge devices to process data in parallel, thus achieving faster execution and response times. The experimental results suggest that such a distributed in-memory approach to data processing at the very edge of a computational network has a potential to address a wide range of IoT-related scenarios. {\textcopyright} 2017 IEEE.

}, keywords = {Apache NiFi, cloud computing, Cluster computing, Computational networks, Data handling, Edge computing, Green Computing, Hardware resources, Horizontal offloading, Internet of thing (IOT), Internet of Things, Memory architecture, Network architecture, Network latencies, Processing activity, Stream processing}, isbn = {9781538630655}, doi = {10.1109/iThings-GreenCom-CPSCom-SmartData.2017.121}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047094836\&doi=10.1109\%2fiThings-GreenCom-CPSCom-SmartData.2017.121\&partnerID=40\&md5=4e5a4b0eaffa179565af183066520cdf}, author = {Rustem Dautov and Salvatore Distefano and Dario Bruneo and Francesco Longo and Giovanni Merlino 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 {348, title = {A Sustainable Energy-Aware Resource Management Strategy for IoT Cloud Federation}, journal = {The first IEEE International Symposium on Systems Engineering (ISSE)}, volume = {IEEE First International Symposium on Systems Engineering 2015 Proceedings}, year = {2015}, month = {09/2015}, pages = {170-175}, publisher = {IEEE Computer Society}, address = {Roma, Italy}, abstract = {

The advent of both Cloud computing and Internet of Things (IoT) is changing the way to conceive distributed systems. Nowadays, we can talk about IoT Cloud to indicate a new type of distributed system consisting of a set of smart IoT devices or sensors interconnected with a remote Cloud infrastructure, platform, or software. Energy sustainability in IoT Cloud providers offers new tempting business opportunities for organizations, but at the same time it raises new challenges. In this paper, a flexible IoT Cloud federation energy management strategy is presented for optimizing the allocation of geographically localized smart sensors. We define the concept of IoT Cloud Federation as a mesh of IoT Cloud providers that are interconnected to provide a universal decentralized sensing and actuating environment where everything is driven by constraints and agreements in a ubiquitous infrastructure.\ In particular, a dynamic algorithm able to improve energy sustainability in a federated IoT Cloud ecosystem is discussed. In addition, we analyze a use-case driven strategy that allows both IoT Cloud providers and brokers to determine the paths to reach possible destination IoT devices in which computational resources should be dynamically migrated in order to push down the energy consumption due to IoT distributed applications.

}, keywords = {cloud computing, Energy Efficiency, Energy Sustainability, federation, Green Computing, Internet of Things, Virtual Sensors}, issn = {978-1-4799-1919-2}, author = {Maurizio Giacobbe and Antonio Celesti and Maria Fazio and Massimo Villari 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 {Bruneo20133, title = {Smart data centers for green clouds}, journal = {Proceedings of the 18th IEEE International Symposium on Computers and Communications (ISCC)}, year = {2013}, note = {cited By 3; Conference of 18th IEEE Symposium on Computers and Communications, ISCC 2013 ; Conference Date: 7 July 2013 Through 10 July 2013; Conference Code:103472}, pages = {3-8}, publisher = {IEEE Computer Society}, address = {Split, Croatia, 7-10 July 2013}, abstract = {

The key idea behind this work is the development of smart data centers able to monitor, control, and manage themselves through advanced analytics and management policies, collecting and analyzing real time data on their behavior. They also make adjustments to interdependent components across the physical infrastructure, in order to address changing business and technology needs. To this aim, we have designed and implemented a new framework for supporting green computing in the management of Cloud data centers. Through heterogeneous sensing devices deployed in the system, the framework is able to know the working state of the data center and to activate specific energy saving policies. We have evaluated the proposed solution through experiments on a real testbed implemented at the University of Messina. Experiments show interesting results thus proving real benefits deriving from the adoption of the proposed framework. {\textcopyright} 2013 IEEE.

}, keywords = {Cloud data centers, Clouds, Data centers, Experiments, Green Computing, Heterogeneous devices, Heterogeneous sensing, Inter-dependent components, Management policy, Physical addresses, sensing, Sensors}, isbn = {9781479937554}, issn = {15301346}, doi = {10.1109/ISCC.2013.6754914}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84897446370\&partnerID=40\&md5=72bf952928d560e009666d68ce6b0fc8}, author = {Dario Bruneo and Maria Fazio and Francesco Longo and Antonio Puliafito} }