Architectures and Algorithms for Content Delivery in Future Networks

Traditional Content Delivery Networks (CDNs) built with traditional Internet technology are less and less able to cope with today’s tremendous content growth. Enhancing infrastructures with storage and computation capabilities may help to remedy the situation. Information-Centric Networks (ICNs), a proposed future Internet technology, unlike the current Internet, decouple information from its sources and provide in-network storage. However, content delivery over in-network storage-enabled networks still faces significant issues, such as the stability and accuracy of estimated bitrate when using Dynamic Adaptive Streaming (DASH). Still Implementing new infrastructures with in-network storage can lead to other challenges. For instance, the extensive deployment of such networks will require a significant upgrade of the installed IP infrastructure. Furthermore, network slicing enables services and applications with very different characteristics to co-exist on the same network infrastructure. Another challenge is that traditional architectures cannot meet future expectations for streaming in terms of latency and network load when it comes to content, such as 360° videos and immersive services. In-Network Computing (INC), also known as Computing in the Network (COIN), allows the computation tasks to be distributed across the network instead of being computed on servers to guarantee performance. INC is expected to provide lower latency, lower network traffic, and higher throughput. Implementing infrastructures with in-network computing will help fulfill specific requirements for streaming 360° video streaming in the future. Therefore, the delivery of 360° video and immersive services can benefit from INC. This thesis elaborates and addresses the key architectural and algorithmic research challenges related to content delivery in future networks. To tackle the first challenge, we propose algorithms for solving the inaccuracy of rate estimation for future CDNs implementation with in-network storage (a key feature of future networks). An algorithm for implementing in-network storage in IP settings for CDNs is proposed for the second challenge. Finally, for the third challenge, we propose an architecture for provisioning INC-enabled slices for 360° video streaming in next-generation networks. We considered a P4-enabled Software-Defined network (SDN) as the physical infrastructure and significantly reduced latency and traffic load for video streaming

An Architecture for Provisioning In-Network Computing-Enabled Slices for Holographic Applications in Next-Generation Networks

Applications such as holographic concerts are now emerging. However, their provisioning remains highly challenging. Requirements such as high bandwidth and ultra-low latency are still very challenging for the current network infrastructure. In-network computing (INC) is an emerging paradigm that enables the distribution of computing tasks across the network instead of computing on servers outside the network. It aims at tackling these two challenges. This article advocates the use of the INC paradigm to tackle holographic applications' high bandwidth and low latency challenges instead of the edge computing paradigm that has been used so far. Slicing brings flexibility to next-generation networks by enabling the deployment of applications/verticals with different requirements on the same network infrastructure. We propose an architecture that enables the provisioning of INC-enabled slices for holographic-type application deployment. The architecture is validated through a proof of concept and extensive simulations. Our experimental results show that INC significantly outperforms edge computing when it comes to these two key challenges. In addition, low jitter was maintained to preserve the hologram's stability

Energy Efficient Virtual Network Embedding in Virtualized Wireless Sensor Networks

In traditional Wireless Sensor Networks (WSNs) the applications are embedded in sensor nodes, making them application-oriented, domain-specific devices. Virtualization is a promising approach to allow several applications to use the physical resources of the same deployed WSN. Given that WSN sensor nodes hold limited available energy, efficient allocation and utilization of resources become important. In this paper, we study the problem of virtual network embedding in virtualized WSNs in a static setting. Our objective is to minimize the overall energy consumption by embedding the application virtual network requests onto the physical WSN substrate network. We formulate the problem as an Integer Linear Programming (ILP), accounting for latency as the service-level agreement (SLA). We then propose our so-called E2NE heuristic to achieve a near-optimal solution in a computationally efficient manner. Our results show that embedding virtual sensor networks in a virtualized WSN leads to more energy efficiency than embedding the same networks in traditional WSN without any support for virtualization. This is largely due to the fact that more physical sensors need to be activated in traditional WSN because each physical sensor can run one and only one task at a time

SCORING: Towards smart collaborative computing, caching and networking paradigm for next generation communication infrastructures

The unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees