The evolution in the generations of smart applications has led to great challenges in terms of providing low latency and high computing efficiency. One of the most important of these applications is for smart homes, through which various connected devices can be controlled by smart and efficient systems to achieve high service quality. In this paper, we propose a smart home controller based on fog computing, where home services are migrated from the cloud to the fog servers at the edge of the network. We propose an exact algorithm called Optimal Migration Algorithm (OMA) that allocates unified fog computing servers to different services. Moreover, to deal with large-scale networks, we propose an efficient algorithm called Efficient Migration Algorithm (EMA). The performance evaluation shows that the proposed optimization solutions are efficient in terms of migration cost, time, and end-to-end latency.

The density increasing in Radio Access Networks (RAN) caused the migration of traditional base stations to the cloud to meet huge traffic of end-users' demands. In this context, virtualization techniques can add more flexibility and programmability to scale in/out virtual storage, network and computing resources. However, Cloud-RAN (C-RAN) requires real-time processing and scheduling of its demands represented as service chains. In this paper, we formulate the joint assignment and scheduling problem in C-RAN using linear programming approach. Placement and scheduling algorithms allowing to allocate efficiently computing resources for C-RAN Virtual Network Functions (VNFs) with respect to the RAN services chaining are introduced and their behavior is quantified through real traces. We illustrate and highlight the feasibility and efficiency of our proposed algorithms through different scenarios in various considered network instances. Metrics such as cpu cores occupancy, network throughput, and successful subframe decoding rate are used to illustrate our algorithms' efficiency.

High Dynamic Unmanned Aerial Vehicles (UAVs) are introduced to assist V2X networking and communication that requires ultra low latency and safety requirements (ULLC). In this paper, we propose a Follow Me UAV (FMU) architecture that aids Vehicular Edge Computing for service offering. Then, a communication protocol is proposed and associated with placement, routing, and optimization algorithms in small and dense networks (OFMU and AFMU). We use deep learning techniques (LSTM and GRU) to predict the connected vehicles trajectory, then the results are used to feed the optimization models. Then, we clarify through Reinforcement Learning based implementations autonomous UAV path planning. Optimization approaches are implemented and evaluated under different quality and computing scenarios. Then, the models are quantified under UAV selection time and energy cost. Results prove the feasibility of the optimization algorithms and suggest the use of mobile UAV as low latency edge servers for service offering.

Service Function Chains (SFC) orchestration is necessary to optimize the use of computing resources and improve the performance of the overall virtualized functions in terms of system resources cost reduction and high quality. It requires intelligent joint chaining and placement algorithm due to the evident huge amount of traffic to be delivered to end customers of the network. In this paper, a global SFC architecture and an exact approach for finding the optimal SFC components instantiation(s) (OPC) are proposed. Then, a deep reinforcement learning (DRL) approach is formulated to deal with a huge number of SFC instances. Moreover, several scenarios are considered to quantify the behavior of OPC and DRL approaches. We compare their efficiency in terms of processing cost and orchestration time. Then, different deployment flavors are implemented and assessed. To study the algorithm’s behavior and to quantify the impact of the system, novel use cases are considered. Results prove the feasibility of the exact approaches in small network scale. Still, the DRL techniques act as an heuristic approaches for chain placement in dense networks.

The emergence of new vehicles generation such as connected and autonomous vehicles led to new challenges in the vehicular networking and computing managements to provide efficient services and guarantee the quality of service. The edge computing facility allows the decentralization of processing from the cloud to the edge of the network. In this paper, we design and propose an end-to-end, reliable and low latency communication architecture that allows the allocation of compute-intensive autonomous driving services, in particular autopilot, to shared resources on edge computing servers and improve the level of performance for autonomous vehicles. The reference architecture is used to design an Advanced Autonomous Driving (AAD) communication protocol between autonomous vehicles, edge computing servers, and the centralized cloud. Then, a mathematical programming approach using Integer Linear Programming (ILP) is formulated to model the autopilot chain resources Offloading at the network edge. Further, a deep reinforcement learning (DRL) approach is proposed to deal with dense Internet of Autonomous Vehicle (IoAV) networks. Moreover, several scenarios are considered to quantify the behavior of the optimization approaches. We compare their efficiency in terms of Total Edge Servers Utilization, Total Edge Servers Allocation Time, and Successfully Allocated Edge Autopilots.

With the emergence of the Internet of Things (IoT) applications, a huge amount of information is generated to help the optimization of operational cellular networks, smart transportation, and energy management systems. Applying Artificial Intelligence approaches to exploit this data seems to be promising. In this paper, we propose a dual deep neural network architecture. It is used to classify time series and to predict future data. It is essentially based on Long Short Term Memory (LSTM) algorithms for accurate time series prediction and on deep neural network, classifiers to classify input streams. It is shown to work on different domains (cellular, energy management, and transportation systems). Cloud architecture is used for IoT data collection and our algorithm is applied on real-time energy data for accurate energy classification and prediction.

The emerging of the internet of things (IoT) led to increasing the computation resources required to satisfy a large number of requests from the connected devices, for this the Cloud Computing (CC) allows the processing of requests in the cloud to guarantee the efficiency of services for end-users. The main problem of the current CC architecture is the latency in real-time applications such as video streaming, which require a distributed architecture to support the future generation of applications. The Mobile Edge Computing (MEC) provides a fully distributed architecture where a part of processing executed in the edge of network which supports the requirements of IoT applications. In this paper, we propose to use the connected devices as on-demand virtual edge servers to provide computation services close to endusers where each submitted task is divided into a set of sub-tasks, each one can be executed by any other device which is a part of the virtual edge server according to the available resources in the selected device. In this context, we have formulated the partitioned and the offloading problem in MEC environment using linear programming techniques. Optimal Partitioned and Offloading (OPO) algorithm that allocates network, storage and computing resources to user application sub-tasks with respect to MEC constraints and user quality requirements is modeled, implemented, and evaluated. Results show the feasibility and efficiency of the proposed algorithms.

The principal tenet of C-RAN is the softwarization of the base-band signal processing, which enables the sharing of computing resources among multiple radio heads. When the aggregate demand exceeds the processing capacity, a fraction of the radio packets is lost at PHY layer. Traditional computing resource allocation policies aim to minimize the packet loss rate. Dropping a PHY packet triggers a retransmission, unless the lost packet corresponds to the last available HARQ round, in which case the entirety of the radio resources spent on the multiple transmissions go to waste. This suggests that allocating computing resource accounting also for the HARQ transmission history may make a more efficient use of the bandwidth. We consider a simplified LTE uplink setting, and we measure the performance at the lower MAC layer (accuracy, goodput and average delay). We first compare the PHY-layer loss rate minimization and the cross-layer approaches using an ILP formulation. The cross-layer approach brings a tangible improvement, especially in accuracy. This suggests, for future work, that joint radio and computing resource allocation may further enhance spectral efficiency. We finally propose a probabilistic algorithm amenable to real-time operation which allows to mix strategies via parameter tuning, and we use it to explore the region of achievable goodput/accuracy trade-offs.

The Internet of Things (IoT) is a network of billion of objects. Data streaming over IoT network is a tedious task that requires intelligent flow management and steering. In this paper, we propose a Distributed Maximum Concurrent Flow (DMCF) algorithm to solve the problem of distributing massive IoT video/data to large consumers over IP/data-centric networks. We propose two approaches based on graph theories, and using reinforcement learning techniques. The proposed approaches are implemented and evaluated over different complex graphs. Results show that in large graphs, reinforcement learning methods outperform classical graph theoretic ones.

The emergence of a new generation of applications led to the appearance of new challenges that represent improvements in current communication technologies. For this, a new network paradigm's including edge computing that allows the process of data at the edge of the network. And the 5G network slicing that represents a new generation of communication increases the capacity of mobile networks by supporting the slicing technology that allows virtual “cutting” of a telecommunications network in several slices that provide high performance in terms of bandwidth and latency. Slice allocation and placement is an important networking optimization task that still painstakingly tune heuristics to get a sufficient solution. These algorithms use data as input and outputs near-optimal solutions. Thus, we are motivated by replacing this tedious process with the recent deep reinforcement learning algorithms. In this paper, we propose three approaches for Virtual Network Functions (VNFs) slices placement in edge computing (Integer linear programming (ILP), reinforcement learning (RL), and deep reinforcement learning (DRL)). Then they are implemented and evaluated. Several scenarios are considered to study the behavior of the algorithms and to quantify the impact of network size. The results show the feasibility and efficiency of the proposed techniques in terms of server utilization, placement time, and energy consumption.

Cloud RAN (C-RAN) is a very promising architecture for future mobile network deployment, where the cloud-centric approach is useful in improving total processing load. In this context, radio and baseband network functions processing pose interesting problems that we try to expose and solve in this paper. A novel architecture for C-RAN and a first modeling of the system are proposed. Furthermore, we study the impact of many radio parameters on the processing time. Moreover, a mathematical and a deep learning model are proposed and evaluated for processing time prediction. Results show the feasibility of the proposed approaches.

Information-Centric Networking (ICN) is very promising for Hadoop-based distributed computing systems, where the data-centric approach is advantageous in reducing the data retrieval latency as well as the network traffic for Hadoop services. Moreover, the inherent in-network caching and computing features in ICN relaxes the end-to-end connectivity between consumers and producers (this helps networking, computation, and power efficiency as Hadoop nodes will use ICN services). Yet, building such a complex system needs new definitions and mappings on the architecture side. It needs also a flattening of the components and an optimization relative to data flow and computation performance. These issues are presented in this paper. Optimal optimization algorithms are then proposed, implemented and evaluated to improve the overall network performance. Experiments demonstrate that ICN support of Hadoop is a feasible architecture and show to improve the performance of Hadoop systems and reduce the end-to-end consumer delay.

The Information-Centric Network (ICN) is very promising in the area of Complex Active Networks (CANs), where the data-centric approach is useful in reducing the data retrieval latency as well as the network traffic of active networking services. Also, the in-network caching and processing capabilities in ICN limits the massive data access to the data producers and so relaxes the need of continuous E2E connectivity between data producers and data consumers. In this paper, we present an ICN-based architecture in which ICN nodes provide processing/treatment capabilities and caching functions. Service placement algorithms (OPPA and HPPA) in CANs are proposed to choose optimal and near-optimal placement location for ICN nodes. We evaluated the algorithms with respect to several performance metrics and the obtained results show improvement in services consumption latency and network load. Furthermore, we propose a caching strategy that shows to stabilize the network load despite any increase in the number of consumer interests.

Virtual Content Delivery Network (vCDN) orchestration is necessary to optimize the use of resources and improve the performance of the overall SDN/NFV-based CDN function in terms of network operator cost reduction and high streaming quality. It requires intelligent and enticed joint SDN/NFV orchestration algorithm due to the evident huge amount of traffic to be delivered to end customers of the network. In this paper, a global vCDN architecture and an exact approach for finding the optimal path orchestration(s) and vCDN component instantiation(s) (OCPA) are proposed. Moreover, several scenarios are considered to quantify the OCPA behavior and to compare its efficiency in terms of caching and streaming cost, orchestration time, vCDN replication number, and other cost factors. Then, it is implemented and evaluated under different deployment flavors. Several scenarios are considered to study the algorithm's behavior and to quantify the impact of both network and system parameters.

Virtual Content Delivery Network (vCDN) migration is necessary to optimize the use of resources and improve the performance of the overall SDN/NFV-based CDN function in terms of network operator cost reduction and high streaming quality. It requires intelligent and enticed joint SDN/NFV migration algorithms due to the evident huge amount of traffic to be delivered to end customers of the network. In this paper, two approaches for finding the optimal and near optimal path placement(s) and vCDN migration(s) are proposed (OPAC and HPAC). Moreover, several scenarios are considered to quantify the OPAC and HPAC behaviors and to compare their efficiency in terms of migration cost, migration time, vCDN replication number, and other cost factors. Then, they are implemented and evaluated under different network scales. Finally, the proposed algorithms are integrated in an SDN/NFV framework.

Software Defined Networking (SDN) is based basically on three features: centralization of the control plane, programmability of network functions and traffic engineering. The network function migration poses interesting problems that we try to expose and solve in this paper. Content Distribution Network virtualization is presented as use case.

Proximity services discovery using LTE-based Device-to-Device (D2D) communications has been recently in the heart of the discussions about the advent of 5G networks. Meanwhile, their related security aspects are of a major concern especially when dealing with direct radio communications and large-scale deployment of D2D. Yet, existing security algorithms and solutions are not adapted to these emerging new types of communications. This paper is focused on D2D communications' security issues in both discovery and communication phases. First, we address the global security stakes and challenges in D2D. Then, a solution based on the Identity-Based Encryption (IBE) mechanism is proposed to secure the exchanged D2D messages during the discovery and communication phases. The proposed solution is discussed under two D2D use cases and is further used to introduce an efficient key management system for group communication. Finally, a security requirements analysis is presented in order to evaluate the scalability and efficiency levels of the proposed solution.