Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.

A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends

ReFIoV: a novel reputation framework for information-centric vehicular applications

In this article, a novel reputation framework for information-centric vehicular applications leveraging on machine learning and the artificial immune system (AIS), also known as ReFIoV, is proposed. Specifically, Bayesian learning and classification allow each node to learn as newly observed data of the behavior of other nodes become available and hence classify these nodes, meanwhile, the K-Means clustering algorithm allows to integrate recommendations from other nodes even if they behave in an unpredictable manner. AIS is used to enhance misbehavior detection. The proposed ReFIoV can be implemented in a distributed manner as each node decides with whom to interact. It provides incentives for nodes to cache and forward others’ mobile data as well as achieves robustness against false accusations and praise. The performance evaluation shows that ReFIoV outperforms state-of-the-art reputation systems for the metrics considered. That is, it presents a very low number of misbehaving nodes incorrectly classified in comparison to another reputation scheme. The proposed AIS mechanism presents a low overhead. The incorporation of recommendations enabled the framework to reduce even further detection time

Next-Generation SDN and Fog Computing: A New Paradigm for SDN-Based Edge Computing

In the last few years, we have been able to see how terms like Mobile Edge Computing, Cloudlets, and Fog computing have arisen as concepts that reach a level of popularity to express computing towards network Edge. Shifting some processing tasks from the Cloud to the Edge brings challenges to the table that might have been non-considered before in next-generation Software-Defined Networking (SDN). Efficient routing mechanisms, Edge Computing, and SDN applications are challenging to deploy as controllers are expected to have different distributions. In particular, with the advances of SDN and the P4 language, there are new opportunities and challenges that next-generation SDN has for Fog computing. The development of new pipelines along with the progress regarding control-to-data plane programming protocols can also promote data and control plane function offloading. We propose a new mechanism of deploying SDN control planes both locally and remotely to attend different challenges. We encourage researchers to develop new ways to functionally deploying Fog and Cloud control planes that let cross-layer planes interact by deploying specific control and data plane applications. With our proposal, the control and data plane distribution can provide a lower response time for locally deployed applications (local control plane). Besides, it can still be beneficial for a centralized and remotely placed control plane, for applications such as path computation within the same network and between separated networks (remote control plane)

Content storage and retrieval mechanisms for vehicular delay-tolerant networks

Vehicular delay-tolerant networks (VDTNs) were proposed as a novel disruptive network concept based on the delay tolerant networking (DTN) paradigm. VDTN architecture uses vehicles to relay messages, enabling network connectivity in challenging scenarios. Due to intermittent connectivity, network nodes carry messages in their buffers, relaying them only when a proper contact opportunity occurs. Thus, the storage capacity and message retrieving of intermediate nodes directly affects the network performance. Therefore, efficient and robust caching and forwarding mechanisms are needed. This dissertation proposes a content storage and retrieval (CSR) solution for VDTN networks. This solution consists on storage and retrieval control labels, attached to every data bundle of aggregated network traffic. These labels define cacheable contents, and apply cachecontrol and forwarding restrictions on data bundles. The presented mechanisms gathered several contributions from cache based technologies such as Web cache schemes, ad-hoc and DTN networks. This solution is fully automated, providing a fast, safe, and reliable data transfer and storage management, while improves the applicability and performance of VDTN networks significantly. This work presents the performance evaluation and validation of CSR mechanisms through a VDTN testbed. Furthermore it presents several network performance evaluations and results using the well-known DTN routing protocols, Epidemic and Spray and Wait (including its binary variant). The comparison of the network behavior and performance on both protocols, with and without CSR mechanisms, proves that CSR mechanisms improve significantly the overall network performance