Immersive Media Services (IMS) such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) are advancing with the development of Beyond $\mathbf{5 G}$ (B5G) and 6G networks. These services integrate three-dimensional elements into user environments through Head Mounted Displays (HMDs) and require stringent latency to ensure real-time interactivity and prevent cyber-sickness. However, traditional IMS architectures often face challenges due to their monolithic designs, which do not efficiently handle user mobility, network fluctuations, or network failures. This paper introduces REACH, a robust orchestration mechanism based on Genetic Algorithm (GA) with resilience support, improving reliability and adaptability in IMS deployments. REACH optimizes microservice chaining (MSC) by considering factors such as microservice sharing probabilities, the spatial distribution of services, and the computational capabilities of edge nodes to enhance network and computational resource efficiency. Our evaluations indicate that REACH substantially increases the instantiation acceptance ratio by up to 52.5% during failure events, significantly improving service continuity and quality in dynamic network environments.

Multi-User Augmented Reality (MUAR) is gaining popularity and enabling interactions collaboratively with mobile users in the virtual 3D world. In this way, decomposing MUAR into elements with specialized ordered Service Functions (SFs) to form a SF chain with decoupled source dynamically allows the SF distribution into multiple edge computing servers and executes SFs of MUAR services in parallel while improving its quality level. However, orchestrating distributed SFs at the network edges and at multiple mobile clients while minimizing latency and meeting resource-hungry requirements is still a research challenge. This paper proposes a mobility-aware SF chaining orchestration scheme for MUAR services called MSF. MSF improves the usage of processing, storage, and networking resources and reduces the latency of MUAR services by optimizing SF chaining in real-time and reinstantiating services into optimal routes. The results show that MSF outperforms state-of-the-art approaches regarding MUAR session acceptance ratio, CPU and bandwidth utilization, and latency in different mobile scenarios.

Immersive entertainment based on Multi-User Virtual Reality (MUVR) is gaining popularity to enable in-game interactions with multiple users. However, computing and network-intensive utilization of resources require changes in cloud-based and traditional monolithic (single machine) deployments towards distributed edge computing architecture to enable high-quality interaction. Decomposing MUVR into elements with specialized Service Functions (SF) in order and organizing it into a Service Function Chaining (SFC) requests orchestration enables MUVR to reuse frame processing between users and improves MUVR scalability. However, orchestrating distributed edge computing resources and multiple destination SFCs while minimizing delay and meeting resource requirements is a challenging task. This article proposes a multi-criteria SFC orchestration scheme for MUVR services, called MuSFiCO. MuSFiCO maps edge computing resources and instantiates SFCs on distributed servers based on delays threshold, CPU and memory resources, and bandwidth. We developed a constrained-based heuristic to minimize delay and compare it with the baseline monolithic deployment and cloud-based SFC algorithms. Results demonstrate the efficiency of MuSFiCO compared to other approaches in terms of latency, CPU, memory, bandwidth utilization, as well as orchestration decision-time.

In modern networks, edge computing will be responsible for processing and learning from the critical network- and user-generated data, such as wireless link usage, mobility information, application requests, and many others. The presence of Artificial Intelligence-based (AI) applications at the edge of the network will enable the network to predict necessary user behavior and its impact on network infrastructure, such as base station overloading. One of the main strategies for offloading users and base stations is to deploy UAV base stations, or flying base stations, which can dynamically provide service and connectivity. In this article, we introduce a framework for distributed learning over Multi-access Edge Computing (MEC), which manages data applications in a fully distributed setting across edge servers, thus reducing the cost of collecting user information in a centralized server. We couple the proposed distributed learning with a novel similarity metric for user trajectories, which can aggregate neural network models with similar costs as other model aggregation techniques. However, the aggregation technique can achieve much higher accuracy. Furthermore, we apply the proposed distributed learning scheme to manage and deploy flying base stations to areas that experience high demand or poor user connectivity, thus optimizing connectivity in terms of user satisfaction, delay, and network throughput.

Video-on-Demand (VoD) services create a demand for content orchestrator mechanisms to support Quality of Experience (QoE). Fog computing brings benefits for enhancing the QoE for VoD services by caching the content closer to the user in a multi-tier fog architecture, considering their available resources to improve QoE. In this context, it is mandatory to consider network, fog node, and user metrics to choose an appropriate fog node to distribute videos with QoE support properly. In this article, we introduce a content orchestrator mechanism, called of Fog4Video, which chooses an appropriate fog node to download video content. The mechanism considers the available bandwidth, delay, and cost, besides the QoE metrics for VoD, namely number of stalls and stalls duration, to deploy VoD services in the opportune fog node. Decision-making acknowledges periodical reports of QoE from the clients to assess the video streaming from each fog node. These values serve as inputs for a real-time Analytic Hierarchy Process method to compute the influence factor for each parameter and compute the QoE improvement potential of the fog node. Fog4Video is executed in fog nodes organized in multiple tiers, having different characteristics to provide VoD services. Simulation results demonstrate that Fog4Video transmits adapted videos with 30% higher QoE and reduced monetary cost up to 24% than other content request mechanisms.

Future mobile networks will enable the massive deployment of mobile multimedia applications anytime and anywhere. In this context, mobility management schemes, such as handover and proactive multimedia service migration, will be essential to improve network performance. In this article, we propose a proactive mobility management approach based on group user trajectory prediction. Specifically, we introduce a mobile user trajectory prediction algorithm by combining the Long-Short Term Memory networks (LSTM) with Reinforcement Learning (RL) to automate the model training procedure. We further develop a group user trajectory predictor to reduce prediction calculation overheads of users with similar movement patterns. To validate the impact of the proposed mobility management approach, we present a virtual reality (VR) service migration scheme built on the top of the proactive handover mechanism that benefits from trajectory predictions. Experiment results validate our predictor's outstanding accuracy and its impacts on enhancing handover and service migration performance to provide quality of service assurance.

Data mining models are frequently used to represent and summarize meaningful properties in data. However such models are usually not suitable for interactive data exploration and visualization. This paper proposes the use of multidimensional projection together with the Ubiquitous Self-Organizing Map algorithm (UbiSOM), a novel variant of the well-known self-organizing map algorithm that was specifically tuned for stream data-mining. The resulting high-dimensional projection system is then studied for interactive data analysis and visualization. A prototype was developed where, at each moment, the user can visualize the information from different perspectives. Direct interaction with the system during stream processing is possible both by changing the projection, by optimizing the projection view for maximizing variance or by filtering the incoming data series. Experiments in two distinct datasets show the importance and relevance of conjoining multidimensional data projection with UbiSOM.

Rapid expansion of the Internet and increasing demand for multimedia services fosters an immediate need for the development of new high-capacity networks capable of supporting these growing bandwidth requirements. The development of broadband transport networks resulted in the need for next generation switches/routers with high-speed interfaces and large switching capacity. The main part of every switching node is a switching fabric, which provides a connecting path between input and output lines. For high-performance switches and routers the Clos-network is very attractive because of its modular architecture and scalability. In packet switching systems packet dispatching algorithms were adopted to avoid packet contention, which may occur while a packet is being routed in a switching fabric. Different dispatching schemes for the buffered Clos-network switches were proposed in many papers. Most of them consist of request, grant and accept phases, and cannot work in the real environment. In this paper the application of static connection patterns to packet dispatching algorithms for the three-stage buffered Clos-network switches is proposed. The performance of two such algorithms, called static desynchronization (SD) and maximal matching static desynchronization (MMSD), is evaluated and compared with results obtained for CRRD (concurrent round-robin dispatching) and CMSD (concurrent master-slave round-robin dispatching) schemes. We would like to show that it is possible to use a very simple packet dispatching scheme and obtain an acceptable performance for a wide range of traffic load per input port.