This proposal plans the activity for the second year of the (two-year) "Vehicular Fog for energy-efficient QoS mining and dissemination of multimedia Big Data streams" (V-Fog) project. The latter aims at defining, designing and validating integrated resource-management and data-mining distributed self-adaptive algorithms for vehicular Fog networks. The final goal is the energy-efficient support of real-time Big Data streaming Future Internet Applications, such as multimedia human activity recognition and infotainment interactive services (e.g., VTube). For this purpose, the actual (still unexplored) transport capabilities promised by the novel paradigm of multipath-TCP will be investigated, while a cognitive approach will be pursued for the integrated design of the V-Fog architecture. The project is motivated by the consideration that the delivering cloud-supported real-time Big Data streaming services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues, while mining large volume of data. To this purpose, Fog computing (FC) is an emerging and still unexplored paradigm that aims at distributing small-size self-powered networked data centers (e.g., Fog nodes) between resource-rich remote Clouds and resource-limited smartphone-equipped VCs, in order to perform context/content aware energy-efficient data mining and dissemination.
Overall, main goals of the V-Fog2 project are to complete the design and validate in SW the related algorithms already developed during the FIRST year, specifically:
a) the distributed and adaptive real-time algorithms for the QoS mining of environmental multimedia Big Data streams;
b) the minimum-bandwidth algorithms for the QoS energy-efficient migration of Virtual Machines (VMs) to/from Fog nodes over multipath-TCP connections;
c) the adaptive Machine Learning-based mining algorithms for the real-time context-aware detection and classification of sensor-acquired human activities of specific social interest.
5) INNOVATIVE PROJECT OUTPUTS AND POTENTIAL TECHNO-SOCIAL IMPACT
The V-Fog technological platform is expected to quickly foster the development of Fog-supported Future Internet Applications, that are considered a world-wide huge business opportunity (Fig.2). In particular, V-Fog paradigm is expected to have relevant technological impact on a number of novel families of latency and energy-sensitive technologies, such as Smart WSNs, Big Data Stream Computing and Intelligent Vehicular Transportation Systems. It is foreseen that the Fog related outputs could move from the research cycle to prototype cycle in the next years (Fig.2). Regarding the social impact, intelligent and energy saving V-Fog inspired platforms are capable to provide more robust and higher QoS and, then are expected to increase the users trust in machine-based and real-time critical vehicular applications. Specifically, the proposed V-Fog platform would enable to quickly acquire a full awareness about unpredictable hazards and vehicular situations, in order to promptly activate appropriate countermeasures. Overall, the V-Fog2 proposal is perfectly aligned with the initiatives lastly launched by the national MISE for the pervasive utilization of ultra-broadband Internet-assisted user applications.
The achievement of the aforementioned targets implies that the V-Fog2 project will continue research on NOVEL energy-efficient solutions in the relevant fields addressed during the FIRST year.
A brief description of the fully (or partially) completed and ongoing tasks (Table1) is given below
T1.1- Optimizing networking-computing resource scheduling for fog-to-vehicle data dissemination
T1.1 has been completed during the FIRST year of the project. Specifically, it dealt with the design and energy-vs.-delay performance evaluation of a distributed scheduler for the minimum-energy joint management of the virtualized networking-plus-computing resources at the FNs. Results on T1.1 can be found in [24]-[29].
T1.2 - QoS live migration of VMs over multipath TCP vehicular connections
The SECOND year of the research activity planned for the T1.2 task will focus on the design of migration-aided virtualized Fog architectures for the support of Social IoT vehicular applications. Due to their complementary features, it is expected that the integration of the Social Internet of Things (SIoT) and Fog Computing (FC) pillar paradigms can foster a large number of computing and networking-intensive vehicular applications that leverage inter-thing social relationships. Hence, the SECOND year research activity of T1.2 will focus on the integration of SIoT and FC into a novel paradigm, the Social Fog of IoT (SoFT) paradigm. Specifically, T1.2 will investigate on SoFT most significant application opportunities and it will design the corresponding clone-based virtualized architecture and its main resource-management functions. Its main expected feature is that it will merge the physical things at the IoT layer and their virtual clones at the Fog layer into a unified Peer-to-Peer (P2P) cyber-physical overlay network of social clones. According to the results already obtained during the FIRST year of the project [30]-[32], live migration of VMs will provide the service function for enabling the mobility-induced inter-Fog migration of the virtual clones over the built-up overlay virtual network. The performance of a small-scale prototype will be simulated by comparisons with state-of-the-art virtualization-free technological platforms that rely only on "ad hoc" Device-to-Device (D2D) vehicular communication
T1.3 - Bandwidth and delay efficient data searching in Fog-supported vehicular Content Delivery Networks
Due to the growing interest for multimedia contents by vehicular users, designing bandwidth and delay-efficient distributed algorithms for data searching over vehicular "ad hoc" P2P content Delivery Networks (CDNs) is a topic of current interest. By acting as nearby proximate data centers for the served peers, FNs may perform vehicular content caching, in order to avoid frequent replication of the same contents and duplication of peer-generated query messages and save bandwidth. Motivated by this consideration, the goal of the T1.3 is twofold: (i) it aims at designing the main building blocks of a hybrid Fog-supported P2P architecture for vehicular content delivery, that it will exploit the topological information locally available at the serving FNs to speed up the data searching operations performed by the served vehicular peers; (ii) it aims at developing a bandwidth and delay-efficient, distributed and adaptive probabilistic search algorithm that relies on a suitable version of the Q-learning distributed reinforcement algorithm for the adaptive discovery of peer-to-peer and peer-to-fog minimum-hop routes. Extensive performance comparisons will then be carried out with some state-of-the-art searching algorithms. T1.3 will cover the SECOND year of the V-Fog2 project