Innovative holographic applications such as holographic concerts have recently emerged. They are expected to play an important role in the Metaverse. Hybrid Network Function Virtualization (NFV) / IN-Network Computing (INC) network infrastructures are needed to provision them as recently shown in the literature. INC is an emerging technology that aims to distribute the computational workload across the network by placing computational tasks on programmable devices (e.g., routers or switches). However, the integration of INC in existing infrastructures does face significant management and orchestration challenges. Although the ETSI Management and Orchestration (MANO) architectural framework designed for 5G facilitates application provisioning in networks that are NFV enabled, it lacks support for networks that are INC enabled. Therefore it is necessary to have a new MANO architecture in order to provision applications which have both NFV and INC components. This paper proposes a hybrid NFV-INC MANO architecture for provisioning holographic applications in hybrid NFV/INC environment. The proposed architecture is an extension of the ETSI NFV MANO. It will certainlyplay an important role in 6G since many applications foreseen for 6G will have the same stringent requirements as holographic applications. It is evaluated through a proof of concept prototype. The following tools were used for the prototype: Open Source MANO (OSM) and Mininet emulator.

The increasing demand for ultra-low latency and high bandwidth in emerging applications, such as virtual reality gaming and telesurgery, is challenging current network infrastructures. In-Network Computing (INC) has emerged as a promising solution to these challenges by optimizing network performance, reducing congestion, and minimizing both latency and bandwidth usage. As 6G networks strive to deliver unprecedented connectivity and ultra-low latency, INC is poised to become an integral part of future network architectures. However, a significant gap exists in the literature concerning a comprehensive model for describing INC components, which is crucial for effective INC provisioning. To address this gap, we propose the In-Network Computing Ontology (INCO), a domain-independent, ontology-based model designed to describe and discover INC components in a centralized repository. Our model covers both the functional and non-functional specifications of INC components. Furthermore, we introduce a semantic matchmaking algorithm that uses the INCO model to automatically discover and select the most relevant INC components from the repository based on user requests. Experimental simulations validate our approach, demonstrating the effectiveness of the semantic matchmaking algorithm, particularly regarding response time and consistency.

Amidst the emergence of immersive applications, such as, the metaverse, Virtual Reality (VR), Augmented Reality (AR), and Holography, it is clear that substantial enhancements to our existing internet infrastructure are imperative. Fulfilling the stringent Quality of Service (QoS) requirements—which include ultra-low latency, high bandwidth, and optimal frame refresh rates—hinges on the seamless integration of Communication, Caching, and Computing, collectively referred to as the "3C". These elements must be interwoven within the network fabric. Our study presents a novel approach to optimize these 3C resources within a network framework that incorporates Edge and Cloud computing, and In-Network Computing (INC). We propose a resource allocation solution tailored to networks enabled by INC. We aim to efficiently manage the distribution of Service Function Chains and the storage of relevant data for immersive applications. Given the inherent complexity of the tackled problem, we propose two solutions: a Particle Swarm Optimization (PSO)-based meta-heuristic and a simpler, yet effective, greedy heuristic. Our comprehensive simulations, grounded in realistic VR scenarios, validate the effectiveness of the proposed solution, which not only enhances resource efficiency and reduces operational costs but also guarantees high refresh rates and maintains a Motion-To-Photon latency under 22 ms.

The emergence of a new class of enhanced multimedia services has pushed network operators to support innovative network services while meeting end-to-end Quality of Service requirements and maintaining profitability. Recent works have hailed Network Function Virtualization (NFV) as a cost-effective enabling technology for novel service delivery in 5G networks. Over time, the association between these NFV-based services and multi-domain networks has grown. As the market competition posed by third-party operators such as virtual operators and service providers has intensified, profitability has become a crucial factor in resource allocation issues. In this paper, we formulate the problem of multi-domain network slicing in dynamic market environments and investigate the effects of these variables on service placement. Due to the NP-hardness of the problem, we employ a node ranking-based algorithm to determine optimal slice entry points in order to maximize profits while meeting end-user QoS requirements. In terms of slice placement acceptance rate and profit growth, numerical results demonstrate the superior performance of our proposed solutions.

As the World is still facing the COVID-19 pandemic, several researchers and industry players have proposed technological solutions to help fight the pandemic and pave the way for post-pandemic era precautions. In this matter, the potential benefits of remote health monitoring have been brought back to the spotlight. Indeed, with current advances in wireless communications, core network virtualization, and computing architectures as enablers, consistently guaranteeing the stringent quality-of-service (QoS) requirements of remote health monitoring, e.g., ultra-low latency, may be achievable. Notably, the fog computing (FC) paradigm has been advocated as a potential solution for remote health monitoring. However, the unreliability of fog nodes in FC networks is a critical aspect often overlooked despite its significant impact on vital latency requirements. This paper proposes a reliable fog-based remote health monitoring framework operating under uncertain fog computing conditions. Specifically, we formulate the problem of assigning tasks of remote sensors attached to patients to their adequate applications deployed in fog nodes aiming to maximize the number of satisfied tasks with respect to the fog nodes’ availability and communication latency constraints. Due to the problem’s NP-hardness, we leverage a differential evolution-based algorithm enhanced by reinforcement learning to deploy applications in fog nodes. Numerical results demonstrate the superior reliability performance of our proposed solution, in terms of the average success ratio of tasks, compared to benchmarks. Specifically, our simulations show up to 60 % performance improvement compared to benchmarks in specific scenarios. Moreover, by investigating the impact of several key parameters, we identify a design trade-off between the number of fog nodes and the latter’s intrinsic failure rates.

The advent of a new breed of enhanced multimedia services has put network operators into a position where they must support innovative services while ensuring both end-to-end Quality of Service requirements and profitability. Recently, Network Function Virtualization (NFV) has been touted as a cost-effective underlying technology in 5G networks to efficiently provision novel services. These NFV-based services have been increasingly associated with multi-domain networks. However, several orchestration issues, linked to cross-domain interactions and emphasized by the heterogeneity of underlying technologies and administrative authorities, present an important challenge. In this paper, we tackle the cross-domain interaction issue by proposing an intelligent and profitable auction-based approach to allow inter-domains resource allocation.

In recent years, fog computing has increasingly become popular with the advent of Internet of Things (IoT) applications characterized by strict Quality of Service (QoS) requirements. To deploy applications, applications are typically decomposed into services then embedded with fog nodes. However, an overlooked aspect in container placement strategies is the heterogeneous inter-container network communication technologies and their impact on application performances in fog networks. We propose and evaluate in this paper, a near optimal genetic algorithm based container placement strategy that takes into account Remote Direct Memory Access as well host and overlay mode for inter-container communication to ensure application response time requirements.

Value-added services (e.g., overlaid video advertisements) have become an integral part of today's content delivery networks (CDNs). To offer cost-efficient, scalable, and more agile provisioning of new value-added services in CDNs, network functions virtualization paradigm may be leveraged to allow implementation of fine-grained services as a chain of virtual network functions (VNFs) to be placed in CDN. The manner in which these chains are placed is critical as it both affects the quality of service (QoS) and provider cost. The problem is however, very challenging due to the specifics of the chains (e.g., one of their end-points is not known prior to the placement). We formulate it as an integer linear program and propose a cost efficient proactive VNF placement and chaining algorithm. The objective is to find the optimal number of VNFs along with their locations in such a manner that the cost is minimized while QoS is met. Apart from cost minimization, the support for large-scale CDNs with a large number of servers and end-users is an important feature of the proposed algorithm. Through simulations, the algorithm's behavior for small-scale to large-scale CDN networks is analyzed.

In the era of Big data, cloud storage services have become the option of choice to store and share data thanks to their cost-effectiveness and seemingly limitless capacity. The increasing success of these services is driving cloud providers to further improve their storage management systems in order to offer more stringent guarantees on data availability and access time. However, despite recent efforts towards this goal, existing solutions have largely overlooked the heterogeneity of the workloads and the underlying storage components in terms of failure rates, capacity and I/O speed. To fill this gap, we present in this paper a heterogeneity-aware data management scheme (dubbed Heron) based on a genetic algorithm that takes into consideration disk heterogeneity to satisfy SLA requirements in terms of access time and availability and minimizes costs in terms of data migration, storage and energy consumption. Through realistic simulations, we show that Heron significantly improves data availability and access time and ensures minimal storage costs and data migration overhead compared to heterogeneity-oblivious solutions.