@conference {8597889, title = {A Deep Reinforcement Learning Approach For Data Migration in Multi-Access Edge Computing}, booktitle = {2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K)}, year = {2018}, month = {Nov}, pages = {1-8}, keywords = {5G, 5G mobile communication, cloud computing, computational resources, data migration, Deep Reinforcement Learning, deep reinforcement learning approach, distributed services, Edge computing, Keras machine learning, Keras machine learning framework, learning (artificial intelligence), Long Term Evolution, LTE, MEC scenarios, Multi-access Edge Computing, multiaccess edge computing, network performance, OMNeT++/SimuLTE simulator, parameter settings, push data, Quality of service, Reinforcement learning, Resource allocation, resource utilization, Servers, SimuLTE}, doi = {10.23919/ITU-WT.2018.8597889}, author = {F. D. Vita and D. Bruneo and A. Puliafito and G. Nardini and A. Virdis and G. Stea} } @proceedings {486, title = {An Energy-Aware Brokering Algorithm To Improve Sustainability In Community Cloud}, journal = {Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2017)}, volume = {1}, year = {2017}, month = {04/2017}, pages = {166-173}, publisher = {Institute for Systems and Technologies of Information, Control and Communication (INSTICC)}, address = {Porto, Portugal}, abstract = {

Cloud computing is a paradigm for large scale distributed infrastructures, platforms or software services which represents a hot topic in Information Technology (IT) recently in both industrial and academic areas. Its use is motivated by the possibility to promote a new economy of scale in different contexts. Along with the well-known public, private and hybrid Cloud models, the Community Cloud is an emerging concept based on a deployment model in which a Cloud infrastructure allows a specific community of consumers to share interests, goals and responsibilities. It can be owned and managed by the community, by a third party, or a combination of them. In such scenario, new low-carbon strategies at Cloud sites are necessary to allow those latter to reduce the consumption in presence of a massive exploitation of IT services. Therefore, balancing performances with both sustainability and cost saving concepts is a challenge. In this paper, we present a low carbon strategy designed to make the best choice in resources allocation, based on sustainability, availability and costs. The proposed energy-aware Brokering Algorithm (eBA) allows to push down carbon dioxide emissions through the Community Cloud ecosystem, by running instances at the most convenient sites.

}, keywords = {cloud computing, Community Cloud, Energy-aware Brokering, Green Cloud, Low Carbon, Resource allocation, Sustainability, Virtualization}, issn = {978-989-758-241-7}, doi = {10.5220/0006300201660173}, url = {http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=8XfUdAkq8Pk=\&t=1}, author = {Maurizio Giacobbe and Marco Scarpa and Riccardo Di Pietro 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} }