Smart Connectivity is the next evolution in telecommunications, fully automated by AI. In this model, network services are designed and provisioned based on Service Level Agreements (SLAs). AI Native frameworks will control, manage, and optimize the AI/ML model pipelines that handle these service operations. This paper introduces a new intelligent orchestrator component, named NeuroPulse, that enables the integration of AI into the Virtual Network Functions (VNF) making them intelligent. This component monitors performance of deployed Virtual Network Intelligent Function (VNIFs), generates new configuration for VNIFs, and optimizes their performance. NeuroPulse is equipped with Few-Shot Profile Generator model that is capable of generating AI configurations aligned with the services’ SLA, and Performance Optimizer that attempts to suggest best previous model parameters for the generated configurations. The Few-Shot Profile Generator works in synergy with the Performance Estimator system. This estimator predicts the performance of generated profiles by analyzing historical data recorded by the monitoring system. The obtained results show that the NeuroPulse system can optimize the structure and the performance of the VNIFs to be aligned with the SLA terms agreed upon by the administrative domain and the service requester.

6G promises E2E cross domains continuous intelligence to optimize resource management and orchestration. However, state-of-the-art methods fall short in providing promised reliable and optimal resource management due to their inefficient proactive decision-making and planning capabilities. This paper proposes a novel Holistic Predictive Framework designed to enhance decision-making and achieve proactive multi-domain resource management. Our framework comprises of predictive, focus, and decision making elements, enabling exceptional proactive planning, and decisions-making based on a holistic vision of network's future. To select the best predictive and decisionmaking elements, various combinations of predictive Machine Learning (ML) and Reinforcement Learning (RL) algorithms were examined in our testbed. To demonstrate the superiority of our framework, we have conducted another test where our framework was compared with state-of-the-art solutions. The test results indicate that coupling the predictive element and attention-augmented decision making unit significantly improves the orchestrator's performance. Based on the result of both tests, our multi-domain orchestration solution, which exploits Latent ODE, outperforms all Cutting-Edge frameworks and is the best combination of the algorithms for our framework.

The rapid evolution towards 6G networks introduces new challenges in orchestrating services across distributed domains while ensuring sustainability goals, such as energy efficiency. Traditional scaling strategies focus on the number of network function instances without optimizing their placement based on energy consumption or resource usage. Addressing this gap, we propose a distributed and energy-efficient placement framework for scaled Network Function (NF) instances across multi-domain 6G infrastructures. Building upon a refined energy consumption model that accounts for computational and network-level power usage, we design a Graph Neural Network (GNN)-enhanced Deep Reinforcement Learning (DRL) agent to optimize placement decisions. The agent encodes the substrate topology and resource states to guide the selection of energy-efficient nodes during scaling operations. We implement and evaluate the framework in a realistic multi-domain scenario featuring fluctuating traffic patterns and heterogeneous node energy profiles. Results show that our approach reduces infrastructure energy consumption compared to a round-robin heuristic, while maintaining high placement success and efficient resource utilization. Among the DRL methods explored, Proximal Policy Optimization (PPO) achieved the best trade-off between placement stability, adaptability, and energy performance. These findings demonstrate the potential of GNN-based DRL agents for sustainable orchestration in future 6G networks.

With the rise of distributed clouds and Virtualized functions, Virtual Network Embedding (VNE) has become a key challenge in enabling network slicing. Several approaches exist in the literature to tackle such a problem; some of them (e.g., heuristics) converge quickly to a local minimum, while others are not explainable and, therefore, do not provide the necessary guarantees for their deployment in a real network (e.g., artificial intelligence-based techniques). In this paper, we propose an enhanced GRASP-based algorithm that efficiently explores the solution space using a broader candidate selection and deeper local search. The simulation results show the potential of the proposed method for solving services’ placement problems and its superiority over some heuristics in terms of placement success rate and the Revenue to the Cost (R/C) metric.

Wireless networks keep evolving to handle an increase of the number of users equipment and services. One of the main challenge is to enhance spectral efficiency as it lead to higher throughput and system capacity. Traditional scheduler, such as Maximum Signal-to-Noise Ratio (MaxSNR), Proportional Fair (PF) perform well in specific context, but can be outperformed in others. Moreover, there is many possible scheduler to try in order to find the best ones for diverse contexts. An approach that uses Artificial Intelligence (AI) becomes a conceivable solution. In this paper we introduce a new AI tool that selects and evaluates schedulers that enhance spectral efficiency for different contexts. Our approach is to train the AI model on several traffic load, to evaluate and select schedulers based on a general scheduling formula, resulting in an explainable model for traffic scheduling. After the training, the appropriate scheduler is used for each traffic load conditions. In addition, our tool detects terms of the formula that does not have a positive impact on enhancing spectral efficiency. Simulation results show that the obtained scheduler outperform state of the art algorithms such as RR, MaxSNR and PF.

Efficient network modeling is essential for resource optimization and network planning in next-generation large-scale complex networks. Traditional approaches, such as queuing theory - based modeling and packet-based simulators, can be inefficient due to the assumption made and the computational expense, respectively. To address these challenges, we propose an innovative energy-efficient dynamic orchestration of Graph Neural Networks (GNN) based model training and inference framework for context-aware network modeling and predictions. We have developed a low-complexity solution framework, QAG, that is a Quantum approximation optimization (QAO) algorithm for Adaptive orchestration of GNN-based network modeling. We leverage the tripartite graph model to represent a multi-application system with many compute nodes. Thereafter, we apply the constrained graph-cutting using QAO to find the feasible energy -efficient configurations of the GNN-based model and deploying them on the available compute nodes to meet the network modeling application requirements. The proposed QAG scheme closely matches the optimum and offers atleast a 50% energy saving while meeting the application requirements with 60% lower churn-rate.

Network virtualization enables 5G slicing, a technique for sharing physical resources among isolated slices managed by different actors. However, monitoring slices' performance is becoming challenging due to the significant network overhead associated with direct measurements. To overcome this problem, we propose using network tomography to estimate slices' delays in the network. In particular, we investigate the problem of finding the minimal combination of end-to-end simple monitoring paths needed to minimize the estimation error of the slices' delays in a network. We proposed a new genetic algorithm to identify the optimal monitoring paths required to achieve network to-mography and minimize their number. To improve the search for the optimal solution, we investigated both a fixed mutation approach and our proposed adaptive mutation approach. Our evaluations show the effectiveness of both approaches; however, the adaptive mutation method outperforms the fixed method by exploring new solutions and avoiding local minima, leading to faster convergence and better results.

The emergence of 6 G requires efficient management of heterogeneous networks and computational resources to achieve targeted end-to-end network automation, with slice orchestration as a key feature. Despite opportunities offered by the recent advances in network virtualization and distributed cloud infrastructures, these developments introduce complexity in the context of multi-domain networks. This paper presents a distributed horizontal scaling method that leverages deep reinforcement learning (DRL) to enhance network function orchestration (NFO) with intelligent scaling decisions, facilitating seamless cross-domain information exchange. Firstly, we develop a DRL agent designed to handle fluctuating traffic loads and generate scaling actions tailored for the considered network function (NF). The trained DRL agent is then integrated into a multi-domain message exchange scaling framework with traffic prediction capabilities. Moreover, a simulation testbed is developed to manipulate multi-domain topologies, customize network slices, and enable precise per-slice and per-domain scaling decisions. Our DRL-based solution outperforms the Horizontal Pod Autoscaling (HPA) heuristic used by Kubernetes, improving resource utilization and reducing data rate losses.

In the dynamic field of 5G network monitoring, the ability to generalize monitors' placement across a network is crucial for comprehensive coverage. This study introduces the use of the Relational Graph Convolutional Network (RGCN) model to meet this challenge. We conducted a comparative analysis between the RGCN and a traditional Neural Network (NN) across two network topologies, considering every possible node configuration within these topologies. Our findings indicate that the RGCN model, once trained on a specific node pair, exhibits superior generalization ability and accuracy. It consistently transfers its learning to changes in monitors' placement and accurately estimates links delays beyond its initial training monitors. Unlike the NN, which showed significant limitations in generalizing monitors' placement and high error rates. This paper not only demonstrates the effectiveness of the RGCN model in generalizing the monitors' placement problem but also paves the way for its broader application in dynamic network monitorinf contexts.

With the increased demands for 5G networks and the limited radio resources, providing high spectral efficiency, low delay, low energy consumption, and other Key Performance Indicators (KPIs) is a challenging task. Extensive research has been conducted to propose efficient solutions for specific objectives and contexts. Although these solutions (often heuristic-based) are highly effective in specific contexts, their performances diminish when applied in different conditions. This implies difficulties in adapting to environmental variations and/or changes in objectives. In order to overcome this problem, we propose an approach employing reinforcement learning to dynamically derive the formula for a scheduler that can be adapted to any context and objective. The proposed solution is validated with a Proof of Concept (PoC), which highlights the Artificial Intelligence (AI) ability to identify the adequate scheduler to optimize spectral efficiency in different traffic loads contexts.

The emergence of beyond 5G networks poses new challenges for network slicing orchestration. In particular, the distribution of orchestration mechanisms across multiple domains is a challenging problem, as independent entities must coordinate in a lightweight manner to maintain slice requirements. This paper presents a machine learning-driven horizontal scaling approach that uses inter-domain communication to achieve more accurate scaling decisions. We propose a collaborative orchestration approach based on the message-passing paradigm. Orchestration actions are performed using a Horizontal Pod Autoscaling (HPA) algorithm on each domain independently according to the required and allocated resources. We have developed a simulation testbed based on OMNeT++, allowing the creation and manipulation of multi-domain topologies and customized network slices. We show that exploiting inter-domain communication through message exchanges enables more accurate scheduling decisions than non-communicative orchestration, reducing data rate losses and enabling more efficient resource utilization.

Despite recent advancements in terms of network performance, end users still face slow web browsing situations, which can have a range of causes, such as a congested Wi-Fi, a bad wireless signal, or a loaded network or end host. It is thus crucial to monitor the network and troubleshoot the specific causes of slow web browsing, as this benefits end users, operators, and internet service providers alike. Various tools attempting to actively troubleshoot the network through the injection of probes exist. However, these tools are, on the one hand, expensive to run and, on the other hand, not general enough to be able to identify the specific cause of web browsing slowness. This paper addresses the problem by proposing a new lightweight passive measurement solution capable of transforming the web performance measurements collected from within the browser into indicators of network performance anomalies. We validate our solution by emulating a controlled network environment with manually injected anomalies; then, we leverage the measurement data available within the browser to build a predictive model that uses a random forest classifier to correctly classify the causes of web browsing performance degradation, with an accuracy of over 95%. This implies that one can build on our solution to propose a tool, in the form of a browser extension, that can be used in the wild to monitor the network and shed light on its anomalies by solely relying on a regular user’s web activity.

Optimizing networks to meet user needs has been a longstanding goal for the various key players in the network sector. To this end, a large number of studies have addressed the case of classifying network traffic into a set of activities (e.g., streaming) and applications (e.g., Spotify). Nonetheless, the fast-paced growth of the digital market has favored the advent of new consuming habits such as the simultaneous performance of multiple activities. This concept is referred to as multi-activity situations or media multitasking. Conceiving solutions that can cope with these emerging consuming patterns may enable network operators and service providers to better adapt their network management solutions and commercial plans. In this paper, we propose a novel approach that can deal with a challenging scenario comprising both single-activity and multi-activity situations. The proposed approach pre-processes a network trace over a time-window and then determines to which situation type it belongs. Furthermore, it identifies the type of the activities being performed and the applications being used (e.g., chat on Facebook & streaming on Spotify). Our experiments highlighted that our solution is able to achieve a satisfactory level of performance despite the complexity of the scenario that we target. Indeed, our obtained results are comparable to state-of-the-art techniques addressing less challenging scenarios that involve only single-activity situations.

Network slicing, a key component of post-5G networks, has brought virtual network embedding (VNE) to the forefront of networking research. However, existing VNE approaches are limited by their consideration of static network topologies, failing to account for the dynamic nature of real-world networks. This limitation becomes particularly problematic in the face of link failures or topology modifications, rendering these approaches ineffective. To address this, we propose a new service placement strategy that remains effective even when the network topology changes. Furthermore, we introduce several service migration strategies and thoroughly investigate their effectiveness. Our results demonstrate the adaptability of our proposed strategy, which leverages graph neural networks, in handling link failures without necessitating relearning. This adaptability underlines the potential of our approach to significantly enhance the robustness and flexibility of service migration in VNE, thereby contributing to the evolution of network slicing in post-5G networks.

Network slicing is a key component of 5G-and-beyond networks but induces many questions related to an associated business model and its need to be regulated due to its difficult co-existence with the network neutrality debate. We propose in this paper a slicing model in the case of heterogeneous users/applications where a service provider may purchase a slice in a wireless network and offer a “premium” service where the improved quality stems from higher prices leading to less demand and less congestion than the basic service offered by the network owner, a scheme known as Paris Metro Pricing. We obtain thanks to game theory the economically-optimal slice size and prices charged by all actors. We also compare with the case of a unique “pipe” (no premium service) corresponding to a fully-neutral scenario and with the case of vertical integration to evaluate the impact of slicing on all actors and identify the “best” economic scenario and the eventual need for regulation.

To ensure low latency, service providers are increasingly turning to edge computing, pushing services and resources from the Cloud to the Edge of the network, as close as possible to users. However, since video and image processing applications are particularly computationally intensive, their deployment is typically based on distributed provisioning between the Edge and the Cloud, which can increase the risk of failure when relying on unreliable networks. In this work, we proposed the algorithm RAP-G (Reliability-Aware service Placement with Genetics), which considers the reliability of network links and distributes services between the Cloud and the Edge using a genetic algorithm (GA). We have also developed a new variant of the first-fit algorithm called RF2 (Reliability-Aware First-Fit) that considers reliability within a reasonable time. The performance of the RAP-G algorithm was evaluated and compared with the RF2 algorithm. The experimental results show the importance of considering reliability in service delivery and the superiority of RAP-G.

The unavoidable transition from rigid dedicated hardware devices towards flexible containerized network services, introduced by Network Function Virtualization (NFV), brings novel opportunities while presenting several new challenges. Indeed, meeting the expectations of NFV in post-5G networks depends on the efficient placement of the services. The online placement of network services, demanding strict end-to-end latency requirements, with restricted computing resources presents a challenging problem which is worth investigating. We propose a Branch-and-Bound search approach for finding optimal placements of the network services by applying several cost functions to maximize the service acceptance. Extensive evaluations have been carried out, and the results confirm significant improvements when we consider a fair distribution of the resources on the edge.

Due to the increase in the number of Internet of Things (IoT) devices in recent years, managing and supporting the diversity of services is becoming more difficult. Network Slicing will be the solution, in which the network slices are tailored to the requirements of the services. In this paper, network slicing is investigated in LoRaWAN networks using the Heuristic-Deep Q-Network (H-DQN) solution that manages the network resource allocation. We propose an intra-service allocation based on the deep Q-Network (DQN) algorithm by allocating virtual resource blocks to services. In addition, the intra-service allocation is based on a heuristic algorithm that assigns the transmission probability to the LoRa nodes of each service for each block in a way to maximizes the Packet Delivery Rate (PDR) of the network while ensuring that the priority of services is maintained. Simulation results show that the proposed approach improves the PDR, and ensures prioritization among services.

Network traffic classification is an active research field that acts as an enabler for various applications in network management and cybersecurity. Numerous studies from this field have targeted the case of classifying network traffic into a set of single-activities (e.g., chatting, streaming). However, the proliferation of internet services and devices has led to the emergence of new consuming patterns such as multi-tasking that consists in performing several activities simultaneously. Recognizing the occurrence of such multi-activity situations may help service providers to design quality of service solutions that better fit users' requirements. In this paper, we propose a framework that is able to recognize multi-activity situations based on network traces. Our experiments showed that our solution is able to achieve promising results despite the complexity of the task that we target. Indeed, the obtained multi-activity detection performance is equivalent or often surpasses state-of-the-art techniques dealing with only a single activity.

With the rise of network virtualization, network slicing is becoming a hot research topic. Indeed, network operators must deal with capacity-limited resources while insuring an extreme availability of services. Several approaches exist in the literature to tackle such a problem, some of them converge quickly to a local minimum, while others are not explainable and therefore do not provide the necessary guarantees for their deployment in a real network. In this context, we propose a new approach for Virtual Network Embedding (VNE) based on the Greedy Adaptive Search Procedure (GRASP). Using the GRASP meta-heuristic ensures the robustness of the solution to changing constraints and environments. Moreover, the proposed realistic approach allows a more efficient and directed exploration of the solution space, in opposition to existing techniques. The simulation results show the potential of the proposed method for solving services' placement problems and its superiority over existing approaches.

Network slicing is a key component of 5G-and-beyond networks, requiring to define a business model for resource allocation. We consider a model with a Service Provider (SP) that may purchase a slice in a wireless network, in order to offer a “premium” service where the improved quality stems from higher prices leading to less demand and less congestion than the basic service offered by the network owner, a scheme known as Paris Metro Pricing. One optimization problem for the SP is the choice of how much resource to allocate to that slice. We also compare with the case of a unique “pipe” (no premium service) and with the case of vertical integration to evaluate the impact of slicing on all actors and identify the “best” economic scenario.

The advanced 5G and 6G mobile network generations offer new capabilities that enable the creation of multiple virtual network instances with distinct and stringent requirements. However, the coexistence of multiple network functions on top of a shared substrate network poses a resource allocation challenge known as the Virtual Network Embedding (VNE) problem. In recent years, this NP-hard problem has received increasing attention in the literature due to the growing need to optimize resources at the edge of the network, where computational and storage capabilities are limited. In this demo paper, we propose a solution to this problem, utilizing the Algorithm Selection (AS) paradigm. This selects the most optimal Deep Reinforcement Learning (DRL) algorithm from a portfolio of agents, in an offline manner, based on past performance. To evaluate our solution, we developed a simulation platform using the OMNeT++ framework, with an orchestration module containerized using Docker. The proposed solution shows good performance and outperforms standalone algorithms.

The number of vehicles in developed countries has grown more rapidly than available road capacity, resulting in increased congestion, air pollution, and more accidents. A recent UN report predicts that the increasing size of cities and levels of population mobility will mean 2.9 billion vehicles on the road in cities alone by 2050. To mitigate the consequences of this increase without dramatically increasing the number of built roads, novel methods to better utilise existing road capacity are required. To that end, this paper introduces COALITION, a cognitive radio-enabled probabilistic offloading of congested lanes, as an innovative solution to efficiently handle traffic congestion in urban areas. This solution builds upon and improves the performance of our previous work, named CRITIC, and makes use of Electric Connected and Autonomous Vehicles (ECAVs) features to maximize the usage of road capacity through opportunistic exploitation of under-utilized reserved lanes while fostering the use of electric vehicles to support carbon neutral transportation objectives. Simulation results have proven the effectiveness of COALITION and its potential impact in real-world scenarios.

The inevitable transition from physical dedicated hardware devices towards lightweight containerized reusable software modules with Network Function Virtualization (NFV) introduces countless opportunities while presenting several unprecedented challenges. Satisfying NFV expectations in post-5G networks heavily depends on the efficient placement of network services. In this paper, after modeling the placement problem and proposing the exact resolutions using Integer Linear Programming (ILP) and Column Generation (CG), we propose our deterministic placement solution, capable of obtaining optimal results with the scalability of a heuristic-grade approach. Our method is organized as a Branch and Bound (BnB) structure, applying Artificial Intelligence (AI) search strategies (especially A*) to address the problem of network service placement. We believe that it is suitable for a range of applications in online placement scenarios, whether we concentrate on the quality of the results or on the strict time constraints. We are interested in the popular objective of Service Acceptance (SA) maximization and have carried out several extensive evaluations. The obtained results confirm the effectiveness of our solution.

Monitoring network performance becomes crucial today since it allows content providers to ensure a good quality of their services by identifying the root causes of service degradation. Also, it gives the end-user a better understanding of the performance they have (state of the networks). A widely used monitoring technique involves performing measurements from within the browser in an effort to capture the network status as close as possible; we talk about Web-based network monitoring. Many Web measurement tools have recently been proposed, however, most of these tools either have a high computational cost or exaggeratedly consume data. In this paper, we propose a lightweight solution able to estimate the underlying network status accurately and perform Web troubleshooting in order to detect anomalies. We develop and implement a distributed system that collects measurements at both levels: browser and network. Then, we build an original network monitoring framework based on Bayesian Gaussian Mixture Models (BGMM) coupled with an algorithm to detect in real time the occurrence of anomalies. We follow a browser-based passive measurement and data-driven approach to derive our inference models, which leads to an efficient Web browsing troubleshooting solution.

IoT and video streaming are the main driving applications for digital data generation today. The traditional way of storing and processing data in the Cloud cannot satisfy many latency critical applications. This is why Fog computing emerged as a continuum infrastructure from the Cloud to end-user devices. Misplacing data in such an infrastructure results in high latency, and consequently increases the penalty for Internet Service Providers (ISPs) incurred by violating the service level agreement (SLA). In past studies, two issues have been investigated separately: the IoT data placement and the streaming cache placement. However, both placements rely on the same Fog distributed storage system. In this paper, we address those issues in a unique model with the aim to minimize the penalty for ISPs incurred by the SLA violation and maximize storage resources usage. We subdivided each Fog node storage space into a storage part and a cache part. First, our model consists in placing IoT data in the storage part of Fog nodes, and then placing streaming data in the cache part of these nodes. The novelty of our model is the flexibility it offers for managing the cache volume, which can, adaptively, spill on the free part dedicated to IoT data. Experiments show that using our model makes it possible to reduce the streaming data penalty of the ISP’s SLA violation by more than 47% on average.

The new emerging technologies enable the appearance of the 5G system and beyond that offers a plethora of services and target new verticals. One of these verticals is the large-scale Internet of Things (IoT) that is expected to be used everywhere in our daily lives. The traffic would likely be increased due to these emerging verticals. To overcome such challenges, network slicing and softwarization will play a crucial role in addressing these requirements and ensuring service level agreements (SLAs). Thus, there is a need to provide efficient and flexible network slice management mechanisms to handle the hurdles that come with the emerging industrial verticals. This paper focuses on network slicing in LoRa networks. We suggest a centralized coalition game-based network slicing strategy to manage LoRa nodes efficiently. According to the K-means clustering algorithm, the proposed solution is deployed within clustered players to maximize reliability while ensuring the SLA of the LoRa slices. Simulation results clearly show that our proposed approach improves Packets' Success Rate (PSR), Network Energy Consumption (NEC), and guarantees prioritization between slices.

Network slicing, also known as the virtual network embedding (VNE) problem, is an NP-hard optimization problem. Compared to traditional approaches, the methods relying on deep reinforcement learning yield better performance without exhibiting issues such as stacking at local minima and/or solutions’ space exploration limits. These algorithms present, however, different performances according to the employed approach, and the problem to be treated, resulting in robustness problems. To overcome these limits, we propose the adoption of the best algorithm, from a selection of learning strategies, in terms of reward and sample efficiency at each time step. The proposed strategy acts as a meta-algorithm that brings more robustness to the network by dynamically selecting the best solution for a specific scenario. Our solution proved its efficiency and managed to dynamically select the best algorithm in terms of the best acceptance ratio of the deployed services and outperform all the standalone algorithms.

Network slicing is one of the building blocks in Zero Touch Networks. It mainly consists in a dynamic deployment of services in a substrate network. However, the Virtual Network Embedding (VNE) algorithms used generally follow a static mechanism, which results in sub-optimal embedding strategies and less robust decisions. Some reinforcement learning algorithms have been conceived for a dynamic decision, while being time-costly. In this paper, we propose a combination of deep Q-Network and a Monte Carlo (MC) approach. The idea is to learn, using DQN, a distribution of the placement solution, on which a MC-based search technique is applied. This improves the solution space exploration, and achieves a faster convergence of the placement decision, and thus a safer learning. The obtained results show that DQN with only 8 MC iterations achieves up to 44% improvement compared with a baseline First-Fit strategy, and up to 15% compared to a MC strategy.

The significant deployment of LoRaWAN networks is increasingly questioning its ability to handle massive numbers of IoT devices and its ability to support service differentiation. The few existing attempts to implement service differentiation suffer from a lack of scalability and do not meet the qualitative criteria of the services, since without admission control there is no way to restrain the devices from transmitting. In this paper, we present a scalable probabilistic approach that not only enables an efficient sharing of LoRaWan access networks between different services/slices, but more importantly allows achieving the objectives of the supported services through the integration of an admission control. Since the derivation of devices' repartition probabilities is a very complex problem, we propose an evolutionary algorithm to derive them efficiently. The obtained results clearly show the ability of the proposed solution to efficiently utilize the scarce radio resources, while achieving the qualitative objectives of the prioritized services.

Network slicing remains one of the key technologies in 5G and beyond 5G networks (B5G). By leveraging SDN and NVF techniques, it enables the coexistence of several heterogeneous virtual networks (VNs) on top of the same physical infrastructure. Despite the advantages it brings to network operators, network slicing raises a major challenge: Resource allocation of VNs, also known as the virtual network embedding problem (VNEP). VNEP is known to be an NP-Hard problem. Several heuristics, meta-heuristics and Deep Reinforcement Learning (DRL) based solutions were proposed in the literature to solve it. Regarding the first two categories, they can provide a solution for large scale problems within a reasonable time, but the solution is usually suboptimal, which leads to an inefficient utilization of the resources and increases the cost of the allocation process. For DRL-based approaches and due to the exploration-exploitation dilemma, the solution can be infeasible. To overcome these issues, we combine, in this work, deep reinforcement learning and relational graph convolutional neural networks in order to automatically learn how to improve the quality of VNEP heuristics. Simulation results show the effectiveness of our approach. Starting with an initial solution given by the heuristics our approach can find an amelioration, with an improvement in the order of 35%.

Network Function Virtualization (NFV) has been a significant shift from traditional dedicated hardware devices towards reusable software modules running over lightweight virtualized environments. While it brings many promising opportunities, it introduces several unprecedented complexities that need further considerations. Efficient placement of services is essential for achieving NFV expectations. We propose a highly reliable solution for systematically placing network services, touching the optimal results while maintaining the scalability, making it suitable for online scenarios with strict time constraints. We organized our solution as a Branch and Bound search structure, which leverages Artificial Intelligence (AI) search strategies (Especially A-Star) to address the placement problem, following the popular objective of Service Acceptance (SA). Extensive empirical analysis has been carried out and the results confirm significant improvements.

Over the last few years, networks' infrastructures are experiencing a profound change initiated by Software Defined Networking (SDN) and Network Function Virtualization (NFV). In such networks, avoiding the risk of service degradation increasingly involves predicting the evolution of metrics impacting the Quality of Service (QoS), in order to implement appropriate preventive actions. Recurrent neural networks, in particular Long Short Term Memory (LSTM) networks, already demonstrated their efficiency in predicting time series, in particular in networking, thanks to their ability to memorize long sequences of data. In this paper, we propose an improvement that increases their accuracy by combining them with filters, especially the Fast Fourier Transform (FFT), in order to better extract the characteristics of the time series to be predicted. The proposed approach allows improving prediction performance significantly, while presenting an extremely low computational complexity at run-time compared to classical techniques such as Auto-Regressive Integrated Moving Average (ARIMA), which requires costly online operations.

Network Slicing (NS) is a key technology that enables network operators to accommodate different types of services with varying needs on a single physical infrastructure. Despite the advantages it brings, NS raises some technical challenges, mainly ensuring the Service Level Agreements (SLA) for each slice. Hence, monitoring the state of these slices will be a priority for ISPs. However, due to the high measurements overhead, it is generally forbidden to directly measure the performance of all of these slices. To overcome this limitation, network tomography is a promising solution, consisting of a set of methods of inferring unmeasured network metrics using end-to-end measurements between monitors. In this work, we focus on inferring the additive metrics of slices such as delays or logarithms of loss rates. We model the inference task as a regression problem that we solve using neural networks. In our approach, we train the model on an artificial dataset. This not only avoids the costly process of collecting a large set of labeled data but has also a nice covering property useful for the procedure's accuracy. Moreover, to handle a change on the topology or the slices we monitor, we propose a solution based on transfer learning in order to find a trade-off between the quality of the solution and the cost to get it. Simulation results with both, emulated and simulated traffic show the efficiency of our method compared to existing ones in terms of both accuracy and computation time.

Monitoring the status of network slices is a priority for network operators to ensure that SLAs are not violated. To overcome the limitations of direct slices’ monitoring, network tomography (NT) is seen as a promising solution. NT-based solutions require constraining monitoring traffic to follow specific paths, which we can achieve by using segment-based routing (SR). This allows deploying customized probing scheme, such as cycles’ probing. A major challenge with SR is, however, the limited length of the monitoring path. In this paper, we focus on the complexity of that task and propose MonGNN, a standalone solution based on Graph Neural Networks (GNNs) and genetic algorithms to find a trade-off between the quality of monitors’ placement and the cost to achieve it. Simulation results show the efficiency of our approach compared to existing methods.

The placement of Virtual Network Function - Forwarding Graphs (VNF-FGs) is one of the basic operations in the networks of the future. Being NP-hard, several heuristics and metaheuristics have been proposed. However, these approaches are inefficient due to the need to recalculate the solution at each service placement. In this paper, we adapt one of the most advanced approaches in Deep Reinforcement Learning (DRL), in order to improve exploration by generalizing the neural network calculating action values. We also propose an evolutionary algorithm to evolve these neural networks in order to discover better ones, which also avoids getting stuck in local minima. In order to avoid going through the almost innumerable number of infeasible solutions, we propose a heuristic, which combined with our DRL, makes it possible to guarantee the feasibility of the solutions and therefore to make the placement much more efficient. The simulation results we obtained confirm the quality of the solutions obtained as well as the superiority of the proposed solution over the existing one.

Web browsing remains one of the dominant applications of the internet, so inferring network performance becomes crucial for both users and providers (access and content) so as to be able to identify the root cause of any service degradation. Recent works have proposed several network troubleshooting tools, e.g, NDT, MobiPerf, SpeedTest, Fathom. Yet, these tools are either computationally expensive, less generic or greedy in terms of data consumption. The main purpose of this work is to leverage passive measurements freely available in the browser and machine learning techniques (ML) to infer network performance (e.g., delay, bandwidth and loss rate) without the addition of new measurement overhead. To enable this inference, we propose a framework based on extensive controlled experiments where network configurations are artificially varied and the Web is browsed, then ML is applied to build models that estimate the underlying network performance. In particular, we contrast classical ML techniques (such as random forest) to deep learning models trained using fully connected neural networks and convolutional neural networks (CNN). Results of our experiments show that neural networks have a higher accuracy compared to classical ML approaches. Furthermore, the model accuracy improves considerably using CNN.

The ability to monitor web and network performance becomes crucial to understand the reasons behind any service degradation. Such monitoring is also helpful to understand the relationship between the quality of experience of end users and the underlying network performance. Many troubleshooting tools have been proposed recently. They mainly consist of conducting active network measurements from within the browser. However, most of these tools either lack accuracy, or perform measurements to a limited set of servers. They are also known to introduce non-negligible overhead onto the network. The objective of this paper is to propose a new approach based on passive measurements freely available from within the web browser, and to couple these measurements to deep learning models to estimate the latency and bandwidth metrics of the underlying network without injecting any additional measurement traffic. We develop and implement our approach, and compare its estimation accuracy with the best known web-based network measurement techniques available nowadays. We follow a controlled experimental approach to derive our inference models. The results of our study show that our approach can give a very good accuracy compared to others, its accuracy is even higher than most standard techniques, and very close to the rest.

Resource allocation of 5G network slices is one of the most important challenges for network operators. It can be formulated using the Virtual Network Embedding (VNE) problem, which was and remains an active field of studies, also known because of its NP-hardness. Owing to its complexity, several heuristics, meta-heuristics and Deep Learning-based solutions have been proposed. However, these solutions are inefficient either due to their slowness or to not taking into account the structure of data which results in an inefficient exploration of the solutions space. To overcome these issues, in this work we unveil the potential of Graph Convolutional Neural (GCN) networks and Deep Reinforcement Learning techniques in solving the VNE problem. The key point of our approach is modeling of the VNE problem as an episodic Markov Decision Process which is solved in a Reinforcement Learning fashion using a GCN-based neural architecture. The simulation results highlight the efficiency of our approach through an increased performance over time, while outperforming state-of-art solutions in terms of the services' acceptance ratio.

Nowadays, multimedia streaming traffic reaches 71% from the mobile data traffic over the world and most of the multimedia services use Dynamic adaptive streaming over HTTP (DASH) to adjust video delivery to the dynamic network environment and achieve higher user Quality of Experience (QoE) levels. Moreover, 90% of the video traffic is consumed by smart devices equipped with multiple network interfaces (Wifi, 3G, and 4G) known as multi-homed devices. In this paper, we propose a new DASH-parallel streaming solution, which exploits the diversity of access network connections to improve video quality over DASH protocol. Experimental Results show that our proposed approach improves the perceived QoE in different network conditions, without requiring further energy expenditure.

During the last decade, Internet Service Providers (ISPs) infrastructure has undergone a major metamorphosis driven by new networking paradigms, namely: Software Defined Networks (SDN) and Network Function Virtualization (NFV). The upcoming advent of 5G will certainly represent an important achievement of this evolution. In this context, static (planning) or dynamic (on-demand) caching resources placement remains an open issue. In this paper, we propose a new technique to achieve the best trade-off between the centralization of resources and their distribution, through an efficient placement of caching resources. To do so, we model the cache resources allocation problem as a multi-objective optimization problem, which is solved using Greedy Randomized Adaptive Search Procedures (GRASP). The obtained results confirm the quality of the out-comes compared to an exhaustive search method and show how a cache allocation solution depends on the network's parameters and on the performance metrics that we want to optimize.

In this paper we will explore novel ways of utilizing inter-vehicle and vehicle to infrastructure communication technology to achieve a safe and efficient lane change manoeuvre for Connected and Autonomous Vehicles (CAVs). The need for such new protocols is due to the risk that every lane change manoeuvre brings to drivers and passengers lives in addition to its negative impact on congestion level and resulting air pollution, if not performed at the right time and using the appropriate speed. To avoid this risk, we design two new protocols, one is built upon and extends an existing protocol, and it aims to ensure safe and efficient lane change manoeuvre, while the second is an original lane change permission management solution inspired from mutual exclusion concept used in operating systems. This latter complements the former by exclusively granting lane change permissions in a way that avoids any risk of collision. Both protocols are being implemented using computer simulation and the results will be reported in a future work.

Mobile video traffic accounted for more than half of all mobile data traffic over the past two years. Due to the limited bandwidth, users demand for high-quality video streaming becomes a challenge, which could be addressed by exploiting the emerging diversity of access network and adaptive video streaming. In this paper, a network selection algorithm is proposed for Dynamic Adaptive Streaming over HTTP (DASH), the famous international standard on video streaming, to enhance the received video quality to a "multi-homed user" equipped with multiple interfaces. A Multi-Armed Bandit (MAB) heuristic is proposed for a dynamic selection of the best interface at each step. While the Adaptive Bitrate Rules (ABR) used in DASH allow the video player client to dynamically pick the bit rate level according to the perceived network conditions, at each switching step a quality degradation may occur due to the difference in network conditions of the available interfaces. This paper aims to close this gap by (i) designing a MAB algorithm over DASH for a multi-homed user, (ii) evaluating the proposed mechanism through a test-bed implementation, (iii) extending the classic MAB model and (iv) discussing some open issues.

Network Function Virtualization (NFV) and service orchestration simplify the deployment and management of network and telecommunication services. The deployment of these services requires, typically, the allocation of Virtual Network Function - Forwarding Graph (VNF-FG), which implies not only the fulfillment of the service's requirements in terms of Quality of Service (QoS), but also considering the constraints of the underlying infrastructure. This topic has been well-studied in existing literature, however, its complexity and uncertainty of available information unveil challenges for researchers and engineers. In this paper, we explore the potential of reinforcement learning techniques for the placement of VNF-FGs. However, it turns out that even the most well-known learning technique is ineffective in the context of a large-scale action space. In this respect, we propose approaches to find out feasible solutions while improving significantly the exploration of the action space. The simulation results clearly show the effectiveness of the proposed learning approach for this category of problems. Moreover, thanks to the deep learning process, the performance of the proposed approach is improved over time.

One of the requirement of 5G is to support massive number of connected devices, considering many use-cases such as IoT and massive Machine Type Communication (MTC). While this represents an interesting opportunity for operators to grow their business, it will need new mechanisms to scale and manage the envisioned high number of devices and their generated traffic. Particularity, the signaling traffic, which will overload the 5G core Network Function (NF) in charge of authentication and mobility, namely Access and Mobility Management Function (AMF). The objective of this paper is to provide an algorithm based on Control Theory allowing: (i) to equilibrate the load on the AMF instances in order to maintain an optimal response time with limited computing latency; (ii) to scale out or in the AMF instance (using NFV techniques) depending on the network load to save energy and avoid wasting resources. Obtained results via computer system indicate the superiority of our algorithm in ensuring fair load balancing while scaling dynamically with the traffic load.

New bandwidth-intensive and time-constrained services in 5G networks combined with network function virtualization is pushing network operators to deploy distributed cloud infrastructures at the edge of the network. Allocating resources in capacity-limited infrastructures raises new challenges, which have not really been so far considered in the cloud literature. In this context, we investigate placement and offloading strategies of constrained services. We set design principles of future distributed edge clouds in order to meet application requirements. We precisely introduce a costless distributed resource allocation algorithm, named CLOSE, which considers local information only. We compare via simulations the performance of CLOSE against those obtained by using mechanisms proposed in the literature, notably the Tricircle project within OpenStack. It turns out that the proposed distributed algorithm yields better performance while requiring less overhead.

The upcoming mobile core network, which will be based on Virtual Network Functions (VNF), will face an increase of data traffic on both data and control planes. This is due to the increase of the number of connected devices and the newly 5G supported-services like IoT, Connected Health Care etc. Therefore dynamic and accurate scalability techniques should be envisioned in order to answer the needs, in term of resource provisioning, without degrading the Quality Of Service (QoS) already offered by hardware based core networks. Although provisioning new resources is easier as it is a matter of software deployment, the strategy to use (when to scale and how much to scale) remains complex. In this paper we propose scaling techniques based on neural networks to forecast the upcoming load. Hence scheduling the resource provisioning should be in a manner that all the needed resources will be deployed and active when the load increases. In the same way, it will scale-in the unneeded resources when the traffic load decreases. The proposal is tested via discrete event simulations using a traffic load dataset provided by a Network Operator. The results show clearly the robustness of our proposal compared to a threshold-based scaling technique.

Beyond Content Delivery Networks (CDNs), Network Operators (NOs) are developing caching capabilities within their own network infrastructure, in order to face the rise in data consumption and to avoid the potential congestion at peering links. These factors explain the enthusiasm of industry and academics around the Content-Centric Networking (CCN) concept and its in-network caching feature. Many contributions focused these last years on improving the caching performance of CCN. In this paper, we propose a very versatile model capable of modeling the most efficient caching strategies. We first start by representing a single generic cache node. We then extend our model for the case of a network of caches. The obtained results are used to derive, in particular, the cache hit probability of a content in such caching systems. Using a discrete event simulator, we show the accuracy of the proposed model under different network configurations.

SDN controllers placement in TelCo networks are generally multi-objective and multi-constrained problems. The solutions proposed in the literature usually model the placement problem by providing a mixed integer linear program (MILP). Their performances are, however, quickly limited for large sized networks, due to the significant increase in the computational delays. In order to avoid the inherent complexity of optimal approaches and the lack of flexibility of heuristics, we propose in this paper a genetic algorithm designed from the NSGA II framework that aims to deal with the controller placement problem. Genetic algorithms can, indeed, be both multi-objective, multi-constraints and can be designed to be computed in parallel. They constitute a real opportunity to find good solutions to this category of problems. Furthermore, the proposed algorithm can be easily adapted to manage dynamic placements scenarios. The goal chosen, in this work, is to maximize the clusters average connectivity and to balance the control's load between clusters, in a way to improve the networks' reliability. The evaluation results on a set of network topologies demonstrated very good performances, which achieve optimal results for small networks.

To address the challenges raised by the Internet usage evolution over the last years, the Content-Centric Networking (CCN) has been proposed. One key feature provided by CCN to improve the efficiency of content delivery is the in-network caching, which has major impact on the system performance. In order to improve caching effectiveness in such systems, studying the functioning of CCN in-network storage is required. In this paper, we propose MACS, a Markov chain-based Approximation of CCN caching Systems. We start initially by modeling a single cache node. Afterwards, we extend our model to the case of multiple nodes. A closed-form expression is then derived to define the cache hit probability of each content in the caching system. We compared the results of MACS to those obtained with simulations. The conducted experiments show clearly the accuracy of our model in estimating the cache hit performance of the system.

We focus on QoE-optimized video delivery in a wireless heterogeneous network setting, where users equipped with multi-interface devices access a Dynamic Adaptive Streaming over HTTP (DASH) video service. We provide an Integer Linear Programming (ILP) formulation for the problem of optimal joint video quality and network interface selection and a heuristic algorithm to solve it, shown via simulation and testbed experiments to achieve near-optimal performance in terms of Quality of Experience (QoE), with reduced execution time. We further design a video delivery architecture based on the emerging Mobile Edge Computing (MEC) standard, where our video quality and network selection functionality is executed as a MEC application. Notably, our architecture transparently operates with standard DASH clients.

Machine Type Communications (MTC) come up with substantial revenue growth for Mobile Network Operators (MNO), but they represent at the same time the most important challenge they are facing. In fact, a massive number of MTC devices performs simultaneously the Random Access (RA), which causes severe congestion and reduces the RA success probability. To control the Radio Access Network (RAN) overload and alleviate the congestion between MTC devices, 3GPP developed the Access Class Barring (ACB) procedure that depends on an access probability called the ACB factor. In this paper, we, first, present a simple fluid model of MTC devices' random access. This model is, then, used to derive a novel adaptive regulator of the ACB factor that in contrast with previous existing contributions, which generally rely on heuristics. The main advantages of the proposed approach are twofold. First, the proposal is fully compliant with the standard while it reduces significantly the computation and the signaling overheads. Second, it provides an efficient mean to regulate adaptively the ACB factor as it guarantees having an optimal number of MTC devices accessing concurrently to the RAN. The obtained results based on simulations show clearly the robustness of the proposed approach, and its superiority compared to existing work.

With the diversity of wireless network accesses, new opportunities are offered to leverage network overload by wisely distributing traffic over the less congested networks. Following this observation, a number of studies have addressed the issue of the optimal interface selection to maximize the network performance. In order to address this problem, we propose, in this paper, a general model describing the interface selection process in heterogeneous network environments. The model is, then, used to derive a scalable controller, which can assist in steering dynamically the traffic to the most appropriate network access while blocking the residual traffic in a way to avoid the network congestion. In contrast with existing mechanisms, which generally rely on heuristic approaches, the proposed mechanism allows to compute network access probabilities based on linear optimal control theory. It also presents the advantage of a seamless integration with the Access Network Discovery and Selection Function (ANDSF). Simulation results sort out that the proposed scheme prevents the network congestion and demonstrates the effectiveness of the controller design, which can maximize the network resources' allocation by converging the network workload to the targeted network occupancy.

The volume of mobile multimedia traffic is fast-growing, challenging the radio and backhaul network infrastructure and calling for alternative content dissemination schemes. To improve user experience and reduce infrastructure load, we exploit implicit social relationships among users and take into account content popularity, proposing push-based prefetching mechanisms which take advantage of the caching and mobile ad hoc networking capabilities of user devices. We use Bloom Filters as summaries of user caches, and design mechanisms to estimate the social distance between users and the popularity of content items, which drive our algorithms. Our simulation-based evaluation shows that our scheme brings caching performance improvements in an order of 10% in terms of absolute cache hit ratio in most of the cases studied, and from 3% to 82% in terms of normalized cache hit ratio gain.

Machine-to-Machine (M2M) communications are expected to be one of the major drivers of future cellular networks, due to a plethora of services provided to operators and consumers. This leads to an explosively growth of simultaneous M2M arrivals, and then a bursty random access attempts that causes a severe random access congestion in addition to terminals' synchronization issues. In this paper, we proposed a novel implementation of the Access Class Barring (ACB) scheme, that mainly consists of dynamically adapting the ACB factor according to the network's overload conditions. Simulation results show that the proposed algorithm outperforms the existing solutions by improving significantly the access's success probability while minimizing radio resources' underutilization.

The Internet of Things (IoT) and particularly Machine-to-Machine (M2M) communications are considered as major enablers for future smart cities' initiatives. While offering a wide range of applications and services, supporting such devices constitutes, however, one of the most important challenges to be faced by Network Operators (NO). Indeed, the expected huge number of devices requesting to connect to the network at the same time may result in severe congestion in the access network with a high risk of congestion collapse. Different schemes were proposed in the literature to solve the congestion problem by regulating the M2M devices' opportunities of transmission. Nonetheless, as revealed in this paper, these schemes turn out to be ineffective in case of heavily congested M2M networks. In fact, in such a condition, the unpredictable and increasingly accumulated number of devices cannot be blocked. This augments the risk of M2M devices' synchronized access, which may result in a congestion collapse. We focus in the following on showing the impacts of this phenomenon while highlighting its main roots. The paper also investigates different key directions for a more efficient management of M2M devices.

The massive deployment of Machine-to-machine (M2M) communications may overwhelm the cellular network by imposing strong constraints on the Radio Access Network (RAN). As the base station cannot accurately get the exact number of M2M arrivals, it cannot really predict the overload status. Consequently, a better estimation of this number would efficiently help to overcome the risk of congestion. In this paper, we proposed a novel fluid model for M2M communications, which allows gaining an enhanced understanding of the dynamics of such systems. The provided analysis of the model was used to devise a new method to estimate accurately the number of M2M devices. We proposed, then, a novel implementation of the ACB process, which dynamically computes the ACB factor according to the network's overload conditions while includes a corrective action adapting the controller action based on the mismatch existing between the computed and the targeted mean load. The simulation results show that the proposed algorithms allow improving considerably the estimation of the number of M2M devices' arrivals, while outperforming existing techniques.

With the explosion of mobile data traffic as well as mobile devices' capability of connecting simultaneously to different access networks, we consider the need to evolve from legacy networks towards Fixed-Mobile Converged (FMC) networks. In FMC architectures, access network selection becomes an issue when mobile devices are under the coverage of distinct technologies. However, a bad selection may lead to network congestion and quality of experience degradation for the end user. In order to deal with this problem, we model and analyze the interface selection procedure using the control theory in FMC architecture. Based on our model, we design a scalable controller, which can help in directing the selection decision of mobile devices in a way to optimize the network resource utilization. Numerical results demonstrate that the number of mobile devices in each access network can converge to the target network occupation, which optimizes network resource utilization while preventing the network overload.

The forecast dramatic growth, of the number of Machine-to-Machine (M2M) communications, challenges the traditional networks of Mobile Network Operators (MNO). In fact, a large number of devices may attempt simultaneously to access the base station, which may result in severe congestions at the random-access channel (RACH) level. To alleviate such congestion while regulating the M2M devices' opportunities to transmit, the Access Class Barring (ACB) process was proposed. In this article, we proposed a novel implementation of the ACB mechanism in the context of multiple M2M traffic classes. Based on a scheduling algorithm, we have applied a PID controller to adjust dynamically multiple ACB factors related to each class category, guaranteeing a number of devices around an optimal value that maximizes the Random Access (RA) success probability. The obtained results demonstrate the efficiency of the proposed mechanism by increasing the success probability and minimizing radio resources' underutilization with respect to each class priority.

The SDNs promise is to provide flexibility, programmability and scalability, while reducing the infrastructure costs to Networks Operators (NO) by centralizing and softwarizing the control plane. Nonetheless, this centralization process goes against the distribution of the control that prevailed, until now, in telecommunication networks. Despite the hopes it raises, it brings new issues mainly related to the scalability and the reliability of this kind of architectures. Drawing on SDN's concepts, this paper's purpose is to build an innovative network architecture that is: programmable, flexible, scalable and aware of the services' SLA. To achieve this goal, we proposed a new architecture called DICES, which includes several concepts. Firstly, a basic controller is integrated as a generic function on the highest layer of all the network elements. Secondly, the controllers instances are activated and deployed thanks to a network orchestrator entity according to the requested SLA. Thirdly, the network services are composed and modeled using Petri nets, and validated before their deployment and execution on the controller level.

Machine-type-Communication (MTC) is seen as a major service in next generation cellular mobile networks. However, the forecasted very large number of MTC devices may overload the RAN (Radio Access Network) part of the network, which may impact Non-MTC communications. Group paging is considered as one of the most efficient mechanisms proposed to alleviate the problem of the RAN overload. In this paper, we introduce a new solution to improve the performance of the current group paging method and overcome its disadvantages. The proposed solution is intended for MTC devices in the RRC CONNECTED OUT OF SYNC state, in which MTC devices have an RRC context without being synchronized with the network. Numerical results demonstrate that the proposed solution highly improves the performance of existing group paging mechanisms.

This paper proposes LOCARN: an alternative network architecture providing a packet connectivity layer, which is able to self-adapt its routing paths to both the effective traffics fluctuations and network resources changes. Moving close to a global maximization of available resources usage and assuming high resiliency under failures, this radical architecture focuses on architectural components coupling simplicity and plug-and-play guidance. Through analysis and computer simulation, several performance metrics focusing on scalability are evaluated.

Video services are being adopted widely in both mobile and fixed networks. For their successful deployment, the content providers are increasingly becoming interested in evaluating the performance of such traffic from the final users' perspective, that is, their Quality of Experience (QoE). For this purpose, subjective quality assessment methods are costly and can not be used in real time. Therefore, automatic estimation of QoE is highly desired. In this paper, we propose a no-reference QoE monitoring module for adaptive HTTP streaming using TCP and the H.264 video codec. HTTP streaming using TCP is the popular choice of many web based and IPTV applications due to the intrinsic advantages of the protocol. Moreover, these applications do not suffer from video data loss due to the reliable nature of the transport layer. However, there can be playout interruptions and if adaptive bitrate video streaming is used then the quality of video can vary due to lossy compression. Our QoE estimation module, based on Random Neural Networks, models the impact of both factors. The results presented in this paper show that our model accurately captures the relation between them and QoE.

One of the most important problems posed by cellular-based machine type communications is congestion. Congestion concerns both the radio access network and the mobile core network, impacting both the user data and the control planes. In this paper, we address the problem of congestion in machine type communications. We propose a congestion-aware admission control solution that selectively rejects signaling messages from MTC devices at the radio access network following a probability that is set based on a proportional integrative derivative controller reflecting the congestion level of a relevant core network node. We evaluate the performance of our proposed solution using computer simulations. The obtained results are encouraging. In fact, we succeed in reducing the amount of signaling, to reach a target utilization ratio of resources in the core network.

Minimizing energy consumption is becoming more and more crucial in today's mobile terminals communications. Reducing its use pass necessarily by exploiting low power design techniques and by adopting novel energy-aware applications and protocols. The focus in this paper mainly concerns the Voice over IP over Wireless Local Area Network (VoWLAN) application, which is beyond doubt the most popular application in mobile terminals. The real-time nature of this application makes the conventional approaches inefficient, as they conserve energy by either: (i) switching the wireless device from the active mode to the sleep mode when no data is buffered for transmission or reception, which completely block the mobile terminal in the active mode (i.e. waste of energy), or (ii) forcing the switching to the sleep mode, which is clearly a detrimental behavior for such delay-constrained application. The idea of the proposed paper is to wisely determine the optimal schedule between the Sleep and Wakeup periods based on the application requirements. Hence, we propose in the following a quality of experience (QoE)-aware protocol, maximizing the sleep mode duration, for VoIP application. The performance of the proposed protocol is performed through computer simulation. The obtained results clearly demonstrate the superiority of the protocol in conserving energy while keeping the QoE at a desired level.

ABSTRACT Different security measures have emerged to encounter various Internet security threats, ensuring a certain level of protection against them. However, this does not come without a price. Indeed, there is a general agreement that high security measures involve high amount of resources, ultimately impacting the perceived Quality of Service (QoS). The objective of this paper is to define a framework, dubbed QoS2, that provides means to find a tradeoff between security requirements and their QoS counterparts. The QoS2 framework is based on the multiattribute decision‐making theory. The performance of the QoS2 framework is evaluated through computer simulations. A use‐case considering worm e‐mail detection is used in the performance evaluation. Copyright © 2012 John Wiley & Sons, Ltd.

DVB-T2 is offering a new way for broadcasting value-added services, like HDTV and 3D TV, to either fix or mobile end users. Thanks to the advances made in digital signal processing, and specifically in channel coding, DVB-T2 brings a new flexibility in services' broadcasting with an increased transfer capacity of 50%, in contrast with the first generation of the DVB-T standard. On the other hand, SVC video coding is an emerging technique that uses scalability to encode video content in a hierarchical video streams (i.e. layers). Indeed, the SVC supports three types of video scalability: spatial, temporal and quality; which allow to handle users' heterogeneity in term of capacity and bandwidth. In this paper, we first propose to support SVC over DVB-T2 networks, by associating the layering architecture of both technology, in order to tackle users' mobility. This association allows mobile receivers with good physical channel to decode all the SVC layers and benefit from high video quality. Meanwhile, users with worst channel condition can at least decode the base layer and benefit from acceptable video quality. Secondly, we introduce a novel QoE-based adaptive mechanism for SVC layers decoding. The proposed approach selects dynamically the number of layers to decode, at the receiver side, so as to maximize the users' perceived quality. Simulation results show clearly the enhancement achieved by the proposed solution in term of user Quality of Experience (QoE).

In the last decade, both mobile and multimedia communications have experienced unequaled rapid growth and commercial success. However, transmitting multimedia flows over wireless Ad hoc network remains an extremely challenging issue due to the limited battery lifetime of the wireless nodes. The focus, of this paper, is to design a new efficient protocol optimizing the energy consumption when transmitting video streams. We propose to exploit the SVC coding to adapt dynamically the received video quality to the instantaneous wireless nodes' characteristics. This is achieved through determining the number of the transmitted/received enhancements layers of an SVC video based on the wireless node context. The proposed solution also considers the routing aspects to guarantee to destinations the requested QoS. In order to evaluate the performance of our scheme, we have carried out several sets of simulation experiments. Our results indicate that our proposal outperforms the conventional approach by increasing the overall network lifetime while maintaining a high perceived video quality.

Along with recent Internet security threats, different security measures have emerged. Whilst these security schemes ensure a level of protection against such threats, they sometimes have significant impact on perceived Quality of Service (QoS). There is thus need to retrieve ways for an efficient integration of security requirements with their QoS counterparts. In this paper, we devise a Quality of Protection framework that tunes between security requirements and QoS using a multi-attribute decision making model. The performance of the proposed approach is evaluated and verified via a use case study using computer simulations.

Wireless mesh networking is a promising, cost effective and efficient technology for realizing backhaul networks supporting high quality services. In such networks, multicast data are transmitted blindly without any mechanism protecting data from loss, ensuring data reception, and optimizing channel allocation. The multicast services may undergo, then, very high data loss ratio which is exacerbated with the number of hops. In this paper, we propose a Reliable Multicast Distribution System (RMDS) to optimize multicast packets transmission in bridged networks. Relying on a modification of the IGMP snooping protocol, RMDS enables reliable services provisioning support in common wireless mesh networks. In particular, RMDS only exploits the local knowledge of a particular node to compute the multicast tree, which significantly reduces the signalling overhead in comparison with network layer and overlay solutions. Simulation results elucidate that RMDS optimizes resources' allocation by reducing significantly the network load, the media access delay and the data drop rate compared to the classical approach, which is based on the combination of spanning tree algorithm and IGMP snooping protocol.

Exploiting the Adaptive Coded Modulation (ACM) mechanism leads to a more spectrum-efficiency since the transmission parameters are dynamically adjusted to accommodate the channel conditions. This improvement is, however, limited when used in satellite environments due to the time-varying nature of the channel conditions. This indeed make harder for the transmitter to match instantaneously the current network conditions with the appropriate ACM parameters. The high delays entailed by accurate network condition measurement and end-to-end reporting, and the feedback propagation are a major hurdles to overcome. In this paper we propose to use channel prediction, instead of using the instantaneous channel state feedback, as a mean to combat the counterproductive effects caused by the feedback latency. Based on the predicted values, we propose a new modulation and code rates (MODCOD) selection algorithm, which considers the measurement impairment by introducing some margins allowing the selection of more robust MODCOD. Further, we propose a sliding window technique as a mean to combat the detrimental effects of the frequent MODCOD switching and the entailed oscillations in the system performance. Simulation results elucidate that the proposed scheme allows a more efficient network resources' utilization while maintains the Bit Error Rate (BER) within an acceptable level.

This paper introduces a robust buffer occupancy-based connection admission control (CAC) mechanism to alleviate both uplink and downlink congestions in converged IP and broadcasting networks. The scheme also ensures a fair share of downlink bandwidth among competing satellite terminals (subnetworks) in the event of congestion. The proposed scheme is dubbed Weighted Fair CAC (W-FCAC). It accepts or rejects connections based on an adaptive fuzzy-based approach. The use of the fuzzy-based mechanism is for the purpose of overcoming issues related to instantaneous link capacity assessment, flow characterization and the associated high computational complexity, and use of traffic descriptors for new flows. Additionally, the adaptive fuzzy logic makes the proposed CAC approach robust to traffic dynamics. These features make the scheme highly suitable for DVB-S2 environments where the link capacity frequently fluctuates due to the adaptive coding/modulation of the physical layer during noisy periods. Simulation results elucidate that the proposed W-FCAC scheme prevents downlink congestion and fairly allocates network resources among satellite terminals. It also minimizes the frequency of congestion events while maintaining efficient utilization of network resources.

Exploiting antenna directionality provides significant improvements in terms of spatial reuse, in comparison to omnidirectional antennas, leading to higher network capacity. However, this improvement is highly correlated to the presence of the hidden terminal and deafness problems. In this paper we propose to handle the sensed noise and the arrival of corrupted packets events in a way to address these issues. This is achieved by special settings to the so-called Directional NAV, initially proposed to solve the exposed terminal problem, and the introduction of a special directional CTS control packet, sent to identified deaf nodes, as an invitation to resend a new RTS packet. The simulation results elucidate the effectiveness of the proposed features in addressing both hidden terminal and deafness problems, while increasing significantly the network performance.

Recently, many QoS architectures have been proposed to handle efficiently multi-service flows in IEEE 802.16 networks. However, these architectures have several weaknesses as they present some scalability issues and donpsilat consider the wireless nature of such networks (e.g. variable link capacity). Moreover, the proposed approaches fail in providing efficient admission control (AC) procedure to tackle congestions at both uplink and downlink channels. In this paper, we introduce new modules in both subscriber station (SS) and base station (BS), allowing more efficient handling of multi-service flows. We particularly focus on the design of a probabilistic and self-configuring AC algorithm, which prevents from uplink and downlink congestions while guaranteeing QoS to rtPS and nrtPS flows. Simulation results show that the proposed admission control protocol highly improves the management of underlying wireless resources, allowing therefore network operators to accept more QoS-enabled services.

This paper introduces a buffer occupancy-based admission control mechanism aimed to counter link congestion while fairly sharing the bandwidth in converged IP and broadcasting networks. The proposed connection admission control (CAC) scheme favors fairness and downlink bandwidth sharing between different served satellite terminals (sub-networks) in presence of congestion events. Our CAC is intended to overcome major obstacles such as: instantaneous link capacity (available bandwidth) assessing, high computational complexity in flows characterization, and use of traffic descriptors with new flows. This makes the scheme more suitable for DVB-S2 environments where the link capacity may vary as consequence of physical layer adaptive coding/modulation during noisy periods. Simulations results show that the proposed system can avoid uplink and downlink congestion and share fairly the bandwidth between satellite terminals. Further, our CAC minimize the probability of congestion events while still achieving high resources utilization.

Diffserv over MPLS networks is a widely accepted approach to considerably improve networks' ability to support delay-sensitive applications such as voice over IP. In such networks, over-provisioning and careful admission control are still needed, although insufficient to ensure guaranteed QoS performance. Increased end-to-end loss rates and delays experienced by a service are mostly due to one or few congested switches along the label switched path "LSP" while the other routers are in relaxed conditions. In this paper, we tackle this issue by extending the traditional local management of congestions into a cooperative process involving all switches along the service path at network operator's scale. Going from AQM limitation, we propose a network self-managing framework that dynamically re-adjusts switches' parameters throughout the LSP, at the point where congestion would most likely occur. In this way, the prospective congestion impact is absorbed through balancing the routers aggressiveness without reconsidering other traffic engineering strategies (e.g., re-routing decision). While considering QoS guarantees along a given service path, network loss ratio is reduced. This obviously allows network operators to further exploit theirs underlying resources by accepting more QoS-enabled services.

With the steadily growing synergy between existing heterogeneous networks, the wireless LAN appears as the de-facto wireless access network in the end-to-end multimedia services distribution chain. Unlike in the traditional wired multi-hop networks (Internet) where congestions increase persistently both delays and losses, wireless packet losses are often location- and time-varying. Particularly, WLAN communication is characterized by high bit error rates that translates into tight loss dependency. The loss process may rapidly shift between different loss correlations levels, resulting in poor forward error correction (FEC) recovery capabilities. In this paper, we address this issue by providing a combined loss model to accurately characterize the wireless loss distribution features. We use control theory guided parameter tuning in order to urge the convergence of the loss models towards seizing the instantaneous loss distribution trends. Finally, we derive a new loss-specific QoS metrics for new FEC block allocation scheme.

Recently, many active queue management (AQM) algorithms have been proposed to address performance degradations of end-to-end congestion control. However, these AQM algorithms present weaknesses for stabilizing delays in heavily loaded networks. In this paper, we describe a novel adaptive fuzzy control algorithm to improve best effort TCP/IP networks performance. Compared to traditional AQM algorithms (RED, PID and others), our proposal avoids buffer overflows/underflows, and minimizes packet dropping. We propose an on-line adaptation mechanism that captures fluctuating network conditions, while classical AQM algorithms require static tuning. The algorithm stability is mathematically proven. Simulation results show that for the same link utilization, our fast adaptive fuzzy controller provides better performance than RED and PID.