@proceedings {Puliafito201897, title = {Companion fog computing: supporting things mobility through container migration at the edge}, journal = {Proceedings - 2018 IEEE International Conference on Smart Computing, SMARTCOMP 2018}, year = {2018}, note = {cited By 0; Conference of 4th IEEE International Conference on Smart Computing, SMARTCOMP 2018 ; Conference Date: 18 June 2018 Through 20 June 2018; Conference Code:138285}, pages = {97-105}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, address = {Taormina, Italy - 18-20 June 2018}, abstract = {

Due to their intrinsic resource constraints, the mobile Internet of Things (IoT) devices are not able to provide intensive services by just relying on their own facilities. Fog Computing effectively helps overcome this hurdle. Indeed, it extends the Cloud toward the network edge, distributing resources and services of computing, storage, and networking close to the end devices. This topological proximity is the key enabler of several advantages that are essential in many emerging ICT domains. Nonetheless, the mobility of an IoT device compromises such benefits as it increases the topological distance to the serving Fog node. Therefore, the Fog service has to be migrated in order to be always close enough to the served IoT device. We name this Companion Fog Computing (CFC), since the Fog service behaves as a {\textquoteright}companion{\textquoteright} of the correspondent application on the mobile device. In this paper, we present a Fog Computing Platform that performs stateful container (i.e., Fog service) migrations in order to enable CFC. Specifically, we introduce a CFC model from which we derive a reference architecture comprising all the functionalities required in a platform to make migration decisions and carry them out. Moreover, we demonstrate the soundness of the proposed reference architecture by discussing a proof-of-concept implementation based on the Stack4Things (S4T) platform, and we report a set of conducted experiments to show the feasibility of stateful container migrations. {\textcopyright} 2018 IEEE.

}, keywords = {Carrier mobility, Computing platform, containers, Docker, Fog, Fog computing, Internet of Things, Migration, Mobile devices, Network architecture, Proof of concept, Reference architecture, Resource Constraint, Topological distance, Topological proximity, Topology}, isbn = {9781538647059}, doi = {10.1109/SMARTCOMP.2018.00079}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051489867\&doi=10.1109\%2fSMARTCOMP.2018.00079\&partnerID=40\&md5=4a8069123a5f19d5e842a86b2e9163f4}, author = {Carlo Puliafito and Enzo Mingozzi and Carlo Vallati and Francesco Longo and Giovanni Merlino} } @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} } @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} } @proceedings {Merlino2015268, title = {Enabling mechanisms for Cloud-based network virtualization in IoT}, journal = {IEEE World Forum on Internet of Things, WF-IoT 2015 - Proceedings}, year = {2015}, note = {cited By 3; Conference of 2nd IEEE World Forum on Internet of Things, WF-IoT 2015 ; Conference Date: 14 December 2015 Through 16 December 2015; Conference Code:119271}, pages = {268-273}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, address = {Milan (Italy)}, abstract = {

As part of a wider effort in integrating Internet of things (IoT) with the Cloud under the guise of infrastructure to be provided as-a-Service, network virtualization plays an essential role, both as an enabler of Infrastructure-as-a-Service scenarios and as a basic building block of the solution for the IoT-focused Cloud provider. Virtualization of the networking facilities for Cloud-managed IoT resources needs mechanisms to deal with the inherent complexity. This work outlines an implementation-agnostic approach to such a problem, reflected in our evolving Stack4Things architecture, derived from OpenStack, and implemented starting from such codebase, by leveraging also a choice of modern tooling and protocols. A specific use case and the discussion that follows are provided to frame the benefits of this strategy. {\textcopyright} 2015 IEEE.

}, keywords = {Basic building block, Cloud providers, Clouds, Complex networks, Infrastructure as a service (IaaS), Inherent complexity, Internet, Internet of Things, Internet of Things (IOT), Network architecture, network virtualization, OpenStack, Virtual reality, Virtualizations, WebSocket}, isbn = {9781509003655}, doi = {10.1109/WF-IoT.2015.7389064}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964453184\&doi=10.1109\%2fWF-IoT.2015.7389064\&partnerID=40\&md5=555a2a5aad4f3af24fac04fc0e4a8280}, author = {Giovanni Merlino and Dario Bruneo and Salvatore Distefano and Francesco Longo and Antonio Puliafito} }