@conference {9066039, title = {A Mininet-Based Emulated Testbed for the I/Ocloud}, booktitle = {2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)}, year = {2019}, month = {Dec}, pages = {277-283}, abstract = {Considering the proliferation of smart devices connected to the Internet, typically going under the aegis of Internet of Things (IoT), a trend has arisen to promote the Cloud paradigm as a suitable management system for such a complex environment. In this context, an effort to extend the OpenStack ecosystem to make it able to support the management of the IoT infrastructure has been made by virtue of the I/Ocloud approach, leading up to its reference implementation, the Stack4Things (S4T) middleware. S4T provides a set of suitable capabilities and features to make the (remote) IoT devices able to join an edge-based IaaS/PaaS Cloud. In the interest of enhancing the S4T middleware scalability and explore new capabilities in particular, ones related to Fog and Edge paradigms, it is becoming a must to test new features in practice at a low financial cost and particular constraints for instance, number/type of devices, network conditions, etc. For this purpose, the use of network emulation tools is a practical and suitable approach. In this paper, we present an integration between the S4T middleware and an emulation tool namely Containernet. Through the integration approach, we model network conditions (e.g., latency, bandwidth, packet loss) and devices (in forms of containers) using Containernet, and we manage the devices (i.e., containers) by means of S4T.}, keywords = {aegis, cloud computing, Cloud paradigm, complex environment, Containernet, containers, Edge computing, Edge/Fog computing, Emulation, input-output programs, Internet of Things, IoT, IoT devices, IoT infrastructure, low financial cost, middleware, Mininet, mininet-based, model network conditions, network emulation tools, OpenStack, OpenStack ecosystem, reference implementation, S4T middleware scalability, smart devices, Stack4Things middleware, suitable management system, Task analysis, Tools, virtualisation}, doi = {10.1109/MSN48538.2019.00060}, author = {Zakaria Benomar and D. Bruneo and F. Longo and G. Merlino and A. Puliafito} } @conference {8784023, title = {Towards trustless prediction-as-a-service}, booktitle = {2019 IEEE International Conference on Smart Computing (SMARTCOMP)}, year = {2019}, month = {June}, pages = {317-322}, keywords = {application program interfaces, Blockchain, cloud computing, cloud provider infrastructure, Computational modeling, Cryptography, Deep Learning, deep learning models, deep neural network models, inference model, inference service, infrastructure acquisition, Machine learning, malicious behaviors, neural nets, neural networks, operation costs, peculiar threat models, prediction API provider, prediction API providers, prediction APIs, prediction-as-a-service, Predictive models, Protocols, security of data, self-hosting costs, software-as-a-service business model, Tendermint}, issn = {null}, doi = {10.1109/SMARTCOMP.2019.00068}, author = {G. Santhosh and F. De Vita and D. Bruneo and F. Longo and A. Puliafito} } @conference {8421336, title = {A Deep Learning Approach for Indoor User Localization in Smart Environments}, booktitle = {2018 IEEE International Conference on Smart Computing (SMARTCOMP)}, year = {2018}, month = {June}, pages = {89-96}, keywords = {Buildings, cloud computing, Deep Learning, deep learning approach, Global Positioning System, Indoor environments, indoor localization, indoor navigation, indoor user localization, indoor user location, integral part, learning (artificial intelligence), Machine learning, radionavigation, smart environments, smart services, telecommunication computing, TensorFlow, user position, Wi-Fi fingerprint, Wireless communication, Wireless fidelity, wireless LAN}, doi = {10.1109/SMARTCOMP.2018.00078}, author = {F. De Vita and D. Bruneo} } @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} } @conference {8726749, title = {Extending Openstack for Cloud-Based Networking at the Edge}, booktitle = {2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)}, year = {2018}, month = {July}, pages = {162-169}, abstract = {Cloud-controlled virtual networking at the edge can be considered a critical feature, highly in demand in the IoT infrastructure management domain. Fostering the vision of the Cloud as a suitable control surface for IoT, an integration between the two ecosystems is achievable at a genuinely Infrastructure-as-a-Service level. This work thus describes an integration design between an IoT-centric infrastructure Cloud framework, already capable of limited networking functionality, and Neutron, the networking subsystem belonging to the core services of the OpenStack platform. Design considerations and trade-offs are detailed in the paper.}, keywords = {Bridges, Cloud, cloud computing, Cloud-controlled virtual networking, control surface, critical feature, Edge computing, IaaS, infrastructure management domain, Infrastructure-as-a-Service level, integration design, Internet of Things, IoT, IoT-centric infrastructure Cloud framework, Linux, network topology, network virtualization, networking functionality, networking subsystem, Neutron, Neutrons, OpenStack, OpenStack platform, Topology, Virtualization}, doi = {10.1109/Cybermatics_2018.2018.00058}, author = {Zakaria Benomar and D. Bruneo and S. Distefano and K. Elbaamrani and N. Idboufker and F. Longo and G. Merlino and A. Puliafito} }