Sustainability is becoming a central objective for cloud and edge infrastructures, particularly in telecommunications, where large-scale deployments increase energy costs and environmental impact. This paper evaluates WebAssembly (Wasm) as a next-generation execution layer, examining its ability to combine high performance with improved resource efficiency. Leveraging our proposed open-source WasmPulse benchmarking framework, we compare Wasm with containers and bare-metal across artifact size, startup time, networking, and resource usage. The results show that while bare-metal remains the most efficient, Wasm achieves performance close to containers while offering distinct sustainability benefits: up to 98% faster startup, significantly smaller artifacts, and more stable memory scaling under load. These advantages reduce computational overhead and resource consumption, enabling efficient and rapid service deployment.Obtained results position WebAssembly as a lightweight, performant, and sustainable execution model, with strong potential for telco-grade cloud-native infrastructures.

Virtual Try-On (VTO) technology, powered by Augmented Reality (AR) and Artificial Intelligence (AI), is reshaping online retail with immersive, real-time user experiences. However, the demand for high-quality, real-time VTO interactions introduces computational and latency challenges. Offloading computations to edge devices, enabled by 5 G networks, offers a promising solution to reduce end-to-end (E2E) latency and support multi-user environments. This paper proposes an optimized VTO platform tailored for edge deployment, featuring a novel knowledge distillation scheme for efficient 3D hand reconstruction. Our knowledge distillation approach reduces the model size by up to 70 % compared to the teacher model, achieving close visual accuracy in simpler tasks, such as flat hand reconstruction, ensuring it meets the quality standards of VTO applications. Experimental results demonstrate that our edge-optimized VTO architecture effectively balances quality and latency, supporting scalable, real-time interactions and advancing VTO technology for next-generation e-commerce applications.

As mobile networks advance, so do the use cases and quality demands they must meet, such as achieving “seven nines” reliability. Meeting these demands requires implementing intelligent and autonomous networks. Observability plays a crucial role in enabling data-driven Orchestration within this context. This paper presents Telco-Observer, an innovative solution tightly integrated with Management & Orchestration systems, designed to collect and provide efficient access to telemetry data across distributed cloud native edge environments. To the best of our knowledge, no existing solutions fully incorporate Observability at the Telco edge while supporting efficient Orchestration. Our performance evaluation demonstrates that Telco-Observer incurs minimal overhead while collecting a broad spectrum of telemetry signals and providing fast data access.

Virtual Try-On (VTO) technologies are becoming a cornerstone of immersive retail experiences in the Metaverse. However, delivering real-time, high-fidelity interactions at scale remains a significant challenge, especially for Artificial Intelligence (AI) based applications that demand substantial computational resources. While edge servers effectively reduce latency and provide greater computational power than constrained User Equipment (UE), they face scalability issues under heavy multiuser loads.In this paper, we propose a novel scheme called Hybrid-Edge-VR4Fit. It is a hybrid offloading framework designed for scalable, low-latency VTO in 5G-edge environments. The system enhances responsiveness by leveraging edge inference to refine subsequent locally processed frames, reducing offloading frequency without compromising quality of service. We then introduce a dynamic offloading strategy modeled as a finite congestion game. Each UE autonomously adjusts its behavior based on real-time edge server and 5G network conditions to balance latency and quality trade-offs. Extensive experimental evaluations demonstrate that our proposal significantly reduces server congestion and queuing delays while maintaining visual fidelity, enabling responsive VTO services across diverse user densities.

The advent of 5G has introduced complex network architectures with stringent Quality of Service (QoS) needs, requiring robust observability solutions for telco operators. This paper proposes 5GC-Tracer, a novel non-intrusive distributed tracing architecture specifically designed for cloud-native 5G Core Network (5GC). Leveraging eBPF technology, our proposal enables application-centric implicit context propagation without requiring any code instrumentation, thus minimizing operational overhead. We conducted real-world testing during the Paris 2024 Paralympic games, demonstrating the effectiveness of 5GC-Tracer in generating and collecting traces from a 5GC system. The experimental results highlight a high success rate in trace collection with low overhead (latency, CPU, memory), and our statistical approach significantly accelerates the localization of anomalies related to QoS degradation across various 5GC network functions.

Observability has become a crucial aspect in cloud native environments, allowing for the measurement of internal states in IT systems. With the emergence of 5G and the anticipation of 6G technologies, the significance of observability in mobile networks has been further emphasized. This paper presents, 5GC-Analyser, a novel approach to understanding the functionality of the 5G core network (5GC) through statistical analyses. By exploring the statistical relationships and causalities among various 5GC microservices and their metrics, our 5GC-Analyser solution, enables the selection of appropriate techniques for online observability. Leveraging these analyses, our framework provides a comprehensive understanding of the intricate dynamics within the 5GC, facilitating improved network management and optimization strategies. We validate our solution in a real-world 5G scenario by simulating end users and replicating conditions observed in a real Orange's 5G gNodeBs (gNB). The obtained results pave the way for future work in proactive detection of performance degradation in the 5G core network. This research bridges the gap between theory and practice, offering valuable insights for network operators and researchers in the field of 5G technology.

The convergence of 5G and Edge computing has revolutionized the technology landscape, ushering in a new era of innovative use cases and accelerating the realization of an intelligent, fully connected digital world at an unprecedented pace. However, the demanding and ever-changing specifications of these emerging applications present significant challenges in terms of orchestration, requiring innovative solutions and efficient management strategies to ensure optimal performance and resource utilization. The mobility of end-users is a critical factor expected to significantly impact Edge operations. As a result, Edge-enabled 5G systems face the daunting task of tracking moving users and meeting their Quality of Experience (QoE) demands simultaneously, which presents a formidable technical challenge that must be addressed for the realization of a truly seamless and ubiquitous user experience. In this paper, we put forward a novel solution based on Reinforcement Learning to optimize the relocation of applications between Edge Hosts while meeting their demands in terms of latency and resource requirements. Our proposed solution aims to achieve a balance between two key objectives: maintaining the Quality of Service (QoS) of Edge Applications and optimizing the utilization of infrastructure resources. By achieving this balance, we can ensure that Edge-enabled 5G systems operate efficiently, with minimal resource wastage and maximal QoS satisfaction for end-users. The intensive simulations and evaluation results demonstrate its effectiveness in terms of QoS assurance and resource load bnlancing.

As 5G Cellular Vehicle-to-Everything (C-V2X) technology takes the lead in V2X communication, it opens the possibility for telecommunication service providers to offer Vehicle-to-Network (V2N) services using their existing 5G network infrastructure. To enhance the security of 5G V2N services, in this paper we propose a novel collaborative V2X misbehavior detection system. This system would safeguard the V2X application servers (V2X ASs), deployed in the 5G edge network, from any malicious V2X position manipulation attacks. Our proposal includes two enhanced machine learning models. The first model utilizes historical data to conduct On-Road Plausibility Checks (ORPC), while the second model builds upon the first by enabling collaboration among edge detection nodes through the sharing of attack ratios for each vehicle. Our proposed models were tested using extensive 5G core-network emulations, yielding excellent results. The first model achieved a notable accuracy improvement from 73% to 91%, while the second model further enhanced the accuracy to an impressive 95%.

As the main goal of connected autonomous vehicles' communications is to improve the traffic safety and save lives, any design of a resource allocation scheme must consider the stringent requirements of these applications in terms of latency and reliability for a dynamic environment. For this, 5G cellular networks suitably address these challenges. This paper proposes a new mechanism for the telco operator to adapt the physical (PHY) layer configuration for efficient radio resource management in 5G New Radio (NR) based system. To tackle this issue, we propose to adjust the PHY layer numerology configuration by fine-tuning it with a Radio-Aware Adaptive PHY Layer Configuration (RA-APC) algorithm in order to maximize the efficiency of radio resource management by using the Effectively Transmitted Packet (ETP) value. Extensive simulations show that our proposal RA-APC achieves strong improvements in terms of ETP, reliability and latency while considering safety and non-safety traffic scenarios.

The convergence of 5G and Edge computing is fostering the development of innovative use cases making the dream of a fully connected, intelligent digital world almost true. However, the stringent requirements coupled to the high dynamicity of these new applications make their orchestration extremely challenging. Specifically, the mobility of end users will undeniably impact Edge operations. Indeed, the Edge-enabled 5G systems need to provide the capability to follow moving users while respecting their latency requirements. To achieve such a goal, we propose, in this paper, an innovative solution optimizing the relocation of applications between Edge clusters while respecting their requirements in terms of latency and resources requests. In doing so, our solution is capable of ensuring the QoS of Edge applications while optimizing the infrastructure resource consumption. Performance evaluation in real platform put forward the efficiency of our solution in terms of (i) QoS assurance, (ii) resource load balancing and, (iii) convergence time.

5G Cellular Vehicle-to-Everything (C-V2X) is expected to become the dominant technology to enable Cooperative Intelligent Transport System (C-ITS) applications. In this paper we address the problem of detecting falsified vehicle positions sent by misbehaving vehicles targeting C-ITS application servers over 5G networks. We propose a novel security system as a 5G application function. It is based on machine learning and integrated with the 5G core network to monitor, detect and prevent potential misbehavior. Based on extensive network simulations utilizing 5G network emulator, our proposal achieves very good performances, accurately reported 99% of misbehaving vehicles and scored an 86% detection rate on the messages' level.

The emergence of 5G Cellular Vehicle-to-Everything (C-V2X) has made it the predominant technology for enabling Vehicle-to-Everything (V2X) communications. As a result, this has created an opportunity for telecommunications service providers to leverage their pre-existing 5G network infrastructure, enabling them to provide Vehicle-to-Network (V2N) services. In this paper, we propose a new approach that enhances the security of 5G V2N services through the implementation of a Federated Learning V2X misbehavior detection system within the 5G core network. The proposed system aims to protect V2X application servers (V2X ASs) that are located in 5G edge networks against potential V2X attacks while leveraging the privacy and scalability advantages of Federated Learning. Our proposed model is compared, using extensive emulations, to other centralized and distributed approaches, achieving excellent results, which makes it feasible for deployment. Our proposal achieved a notable accuracy of 98.4%, while scoring an impressive 99.3% precision and 96.9% detection rate.

Telco players are accelerating their adoption of cloud native technologies. Indeed, the migration of traditional communications applications to microservice-based architectures will facilitate the development of new network services while providing a high level of granularity. However, cloud native comes with new operational challenges. Indeed, effective network service management requires novel solutions for fine-grained monitoring and tracking of widely distributed cloud native network functions. In this paper, we put forward 5GC-Observer, our proposed framework for the observability of cloud native 5G network services. 5GC-Observer leverages the eBPF technology to track network traffic circulating between 5G network functions and report telemetry data. This demo makes use of our open-source platform, Towards5GS, to implement a real cloud native 5G network on top of Kubernetes. Finally, we develop a statistical technique which leverages the collected telemetry data to detect 5G end-users’ Quality of Service degradation, based on real access network information collected from Orange’s gNB (gNodeB) located in Paris-Orly airport.

Telco stakeholders are developing a deeper understanding of cloud native technologies and adopting them faster than few years ago. It is undeniable that migrating legacy telco applications to microservice-based architectures accelerates and facilitates the development of new network services while offering a high level of granularity. However, cloud native raises new operational challenges. In order to achieve an efficient management of network services, new solutions are required to monitor and track widely distributed cloud native network functions while considering their specificity. In this paper, we propose an innovative framework, 5GC-Observer, for the observability of cloud native 5G network services. To the best of our knowledge, no such a solution has been found to date. To achieve its goal, 5GC-Observer relies on the eBPF technology to monitor the network traffic circulating between the 5G core components and report telemetry data. Besides, we leverage a statistical method to detect Quality of Service degradation based on reported telemetry data. Such an approach highlights the richness of the data acquired by our solution and its capability to detect unexpected network-related anomalies. The latter are not detectable through standard observability solutions. Performance evaluation shows that our solution generates low overhead while giving insight into the 5G core system and its internal and external exchanges.

Network slicing is a key enabler in the Next Generation 5G Radio Access Network (RAN) to build the RAN-as-a-Service concept. Cloud-RAN, Network Function Virtualization, Software Defined Network and RAN functional splits are the main pillars expected to be integrated to provide the required flexibility. One of the major concerns is to efficiently allocate RAN resources for slices, while supporting multiple use-cases with heterogeneous Quality-of-Service (QoS) requirements. Current related work is adopting radio resource allocation scheme by considering a cell-centric deployment approach for slice embedding. However, to achieve greater flexibility and fine-grained tunable resource utilization, we believe that the deployment scheme should be integrated in the slice design. In this paper, we go a step further and propose a RAN slicing approach with customized deployment scheme on user basis. As the corresponding optimization problem is NP-Hard, we propose a low-cost and efficient heuristic algorithm for RAN Slice allocation based on the Particle Swarm Optimization approach. Our proposal jointly harnesses radio, processing and link resources at user level tailored to the QoS requirements, while customizing efficiently the underlying physical RAN resource usage.

Low latency and high-reliability communications for applications' flows is one of the main 5G cellular network objective, which is especially relevant for connected autonomous vehicles. However, efficient wireless resource allocation is a complex. To address this problem in this paper, we propose to adapt the physical (PHY) layer numerology configuration by fine-tuning it with Adaptive PHY Layer Configuration (APC) algorithm in aim to maximize the Effective Transmitted Packet (ETP). Besides, we propose an adaptive scheme to maximize the expected packet serving rate while avoiding the starvation phenomenon of low priority Logical Channels (LC). Based on extensive simulations, results show that our proposal achieves good performance in terms of ETP maximization and starvation minimization of low priority LCs.

For years, Cloud infrastructures have driven the network services growth, providing increased scalability, enhanced performance and cost-effective resources. Nevertheless, the emergence of demanding 5G use cases poses several challenges that today’s Cloud infrastructures, despite their agility, struggle to overcome. In this context, Edge Computing has emerged to cope with Cloud’s shortcomings. This augmented architecture provides nearby resources, performing analytics tasks and data storage. However, it raises new challenges in terms of application design and management. In this perspective, the orchestration is the cornerstone that will enable the exploitation of edge infrastructures and the life-cycle management of multi-component applications. In this paper, we address the orchestration of edge-native applications issue in a practical manner. Specifically, we design and implement a multi-cluster orchestrator optimizing the provisioning of geo-distributed applications while considering the underlying edge infrastructure. To validate our approach, we evaluate the orchestration of our Towards5GS, an open-source edge-native 5G network service. Based on extensive experiments, the results obtained show that our solution achieved good results in terms of i) deployment time and ii) migration time.

Data offloading based on Device-to-Device (D2D) can support congestion-prone cellular networks in the face of traffic growth. In this paper, we tackle the design of offloading systems that are aware of the energy limitation of the complementary D2D network. We propose a novel heuristic scheme, named HERRA, using a parametric three-stage method that includes possible variations on the employed strategies in each stage. Performance evaluation, using network simulations in our extended NS-3, shows that HERRA outperforms the related work in the matter of convergence time. As a result of massive speedups, up to six orders of magnitude, HERRA scales very well in denser topologies at the price of having some performance gaps, particularly in terms of packet loss.

Cloud native paradigm gained momentum during the last few years fostering its adoption in 5G architecture. In this context, a new generation of network functions called Cloud native Network Functions (CNFs) has seen the light of day. However, despite their numerous advantages in terms of lightness and portability, CNFs raise new issues not solved yet. Specifically, these fine-grained services require an efficient orchestration system able to automate their lifecycle management while considering the stringent QoS’s requirements. In this paper, we propose a novel 5G CNFs orchestration framework addressing both IT and Network resource provisioning. Both qualitative and quantitative studies are conducted to validate our solution using the auto-scaling use-case. Our extensive experimentations show that our proposal achieves good performances in terms of: i) deployment time, ii) upgrade time, iii) packet loss rate, and iv) resource allocation balancing.

The evolution of the Internet of Things (IoT) is driving an extraordinary growth of traffic and processing demands, persuading 5G players to change their infrastructures. In this context, Fog computing emerges as a potential solution, providing nearby resources to run IoT applications. However, the Fog raises several challenges which hinders its adoption. In this article, we consider thereconfiguration problem, i.e., how to dynamically adapt the placement of IoT applications running on the Fog, depending on application needs and evolution of resource usage. We propose and evaluate a series of reconfiguration algorithms, based on both online scheduling and online learning approaches. Through an extensive set of experiments in a realistic testbed, we demonstrate that the performance strongly depends on the quality and availability of information from both Fog infrastructure and IoT applications. This information mainly concerns the application’s resource usage (estimated by the user during the design of the application) and the availability of resources in the infrastructure (collected by commercial off-the-shelf monitoring tools). Finally, we show that a reactive and greedy strategy, which relies on this additional information, can overcome the performance of state-of-the-art online learning algorithms, even in a scenario with inaccurate information.

Network Function Virtualization (NFV) has already proven its efficiency to deploy networking services in large-scale. Recent advances of cloud-native applications may bring new advantage by deploying and implementing Virtual Network Function (VNFs) as cloud-native Containers rather than virtual machines. Beside remarkable advantages such as lower overhead and faster running, microservices (cloud-native containers) intend to save costs while increasing the service agility. To this end, in this paper we extend consolidated state-of-the-art tools and technologies developed in two domains cloud-native applications and Network Function Virtualization (NFV). The proposed framework chains services provisioned across Kubernetes and Contiv/VPP domains and using containers. Our orchestration framework chain services across distributed CNFs. Furthermore, we propose K -TS scheme to load balance the traffic over services replicas. K -TS is based on Ketama Consistent hashing algorithm. Experimental simulations show very good results for both the service chaining framework in term of QoS satisfaction such as: packet error rate, throughput satisfaction and jitter.

In this paper, we optimize the 5G D2D communications while considering the multicast flows and the bandwidth OFDMA resource allocation. We propose new optimized routing and resource allocation algorithm based on column- -generation. To do that, new problem formulation based on graph theory is propounded. Based on extensive network simulation in NS-3 environment, we show that our proposal achieves good performances in terms of reliability, latency, and scalability.

5G will provide a flexible and programmable infrastructure, allowing different networks to share the same access network. A way to respond to the diverse service requirements of 5G while reducing both CAPEX and OPEX is to adopt cloud-native architectures. In this context, micro-services software design, the corner stone of cloud-native architecture, seems to be ideal for 5G. However, despite its several advantages, micro-services raise new challenges which slow its adoption down in the NFV ecosystem. Indeed, steering the expected 5G traffic between cloud-native network function is extremely challenging and is still under-investigated. In this paper, we address the service function chaining (SFC) in micro-service based network function virtualization (NFV) ecosystem from the view of the traffic steering. Specifically, we design and implement a cloud-native SFC framework offering efficient traffic steering mechanisms while considering the network state of the underlying NFV infrastructure. In this context, an optimized network-aware load balancing strategy is proposed. Based on extensive experiments, the results obtained show that our strategy achieved good results in terms of i) end-to-end latency and ii) deployment time.

Network slicing is proposed as a new paradigm to serve the plethora of 5G services on a shared infrastructure. Within this context, a Radio Access Network (RAN) slice is considered as the proportion of physical spectrum resources to be served to third parties. Interestingly, 3GPP standardized options of RAN processing dis-aggregation into network functions while enabling their placement whether in distributed or centralized locations. The adoption of an end-to-end RAN slicing raises new challenges related to the allocation efficiency of joint radio, link and computational resources. To deal with the stringent latency requirements of 5G services, we propose, in this paper, a Deep Learning based approach for User-centric end-to-end RAN Slice Allocation scheme. It can decide in real-time, to jointly allocate the amount of radio resources and functional split for each end- user. Our proposal satisfies end-user's requirements in terms of throughput and latency, while minimizing the infrastructure deployment cost.

In this paper, we address the optimal design of scalable offloading schemes based on the LTE-D2D standard to offload intracellular traffic, unicast and multicast, over multihop D2D networks of cooperative User-Equipments. Specifically, we deal with the problem of joint routing and OFDMA resource allocation that underlies such schemes. Considering crowded-platform use-cases, we propose a novel path-based ILP formulation in which a routing tree is formulated in terms of its constituent paths. Moreover, to boost scalability, we propose a sub-optimal solution method, named JRW-D2D-CG, based on the column-generation framework with a pricing problem. Based on extensive network simulation in NS-3 environment, we demonstrate that our proposal achieves good performances in terms of reliability, latency, and scalability.

Next Generation 5G Radio Access Network (NGRAN) is envisioned to integrate the slicing approach to build a flexible network supporting diverse use-cases with customized architectures, features and services. RAN processing functional splits have been standardized to add new deployment design capabilities and enhance cost efficiency. A further challenge consists in how to meet the multitude use-case's requirements while considering different design models in the physical infrastructure. Current related works are tackling the slice embedding problem from a cell-centric perspective. However, to achieve greater flexibility and better resource utilization, a user-centric approach should be more exploited. In this paper, we propose a SLICE-HPSO scheme that jointly harnesses radio, processing and link resources at the user level to build multiple user slices on top of the physical infrastructure. Our proposal is tailored to different user quality-of-service requirements and to the diverse functional splits resource requests. SLICE-HPSO is in compliance with the 3GPP and optimizes further the heterogeneous resource usage while meeting the scalability requirement.

5G RAN aims to evolve new technologies spanning the Cloud infrastructure, virtualization techniques and Software Defined Network capabilities. Advanced solutions are introduced to split the functions of the Radio Access Network (RAN) between centralized and distributed locations. Such paradigms improve RAN flexibility and reduce the infrastructure deployment cost without impacting the user quality of service. We propose a novel functional split orchestration scheme that aims at minimizing the RAN deployment cost, while considering the requirements of its processing network functions and the capabilities of the Cloud infrastructure. With a fine grained approach on user basis, we show that the proposed solution optimizes both processing and bandwidth resource usage, while minimizing the overall energy consumption compared to i) cell-centric, ii) distributed and iii) centralized Cloud-RAN approaches. Moreover, we evaluate the effectiveness of our proposal in a 5G experimental prototype, based on Open Air Interface (OAI). We show that our solution achieves good performance in terms of total deployment cost and resolution time.

In recent years, Fog computing paradigm has received the attention of academic and industrial communities. By offering nearby computational, storage and network resources, this new architecture deals with the explosion of IoT (Internet of Things) traffic while responding to the stringent requirements of new applications. Unfortunately, as of today, there is a lack of practical solutions to enable the exploitation of this novel paradigm. To deal with this shortcoming, this demo gives an insight into FITOR, our proposed orchestration system for IoT applications in Fog. Our solution makes use of both Grid5000 [1] and FIT/IoT-LAB [2] to build a realistic fog environment. FITOR is responsible for the orchestration of micro-service based IoT applications while making use of a holistic monitoring of the fog infrastructure.

To this day, the Internet of Things (IoT) continues its explosive growth. Nevertheless, with the exceptional evolution of traffic demand, existing infrastructures are struggling to resist. In this context, Fog computing is shaping the future of IoT applications. It offers nearby computational, networking and storage resources to respond to the stringent requirements of these applications. However, despite its several advantages, Fog computing raises new challenges which slow its adoption down. Hence, there is a lack of practical solutions to enable the exploitation of this novel concept. To deal with this shortcoming, we propose FITOR, an orchestration system for IoT applications in the Fog environment. This solution builds a realistic Fog environment while offering efficient orchestration mechanisms. In order to optimize the provisioning of Fog-Enabled IoT applications, FITOR relies on O-FSP, an optimized fog service provisioning strategy which aims to minimize the provisioning cost of IoT applications, while meeting their requirements. Based on extensive experiments, the results obtained show that O-FSP optimizes the placement of IoT applications and outperforms the related strategies in terms of i) provisioning cost ii) resource usage and iii) acceptance rate.

In this paper, we design and implement an SDN-based architecture Pgm-RAN which provides an effective representation of the radio network state at different network levels. Thanks to its programmability, Pgm-RAN enables the implementation of real-time RAN control algorithms in a modular fashion within a Cloud-RAN environment. To demonstrate the effectiveness of Pgm-RAN and assess its applicability, we design and implement a control plane application that ensures an enhanced joint Remote Radio Head assignment and physical radio resource allocation denoted by enhanced-DPS. To do so, we formulate the problem as Integer Linear Program while taking into consideration the mobile users requirements and the radio environment conditions. The problem is then resolved in a polynomial time leveraging linear relaxations and Branch & Cut algorithm. Both experimental and simulation results are provided to prove the effectiveness of our framework and to gauge the performance of enhanced-DPS. It is worth noting that our experimental platform relies on two main building blocks: i) Open Air Interface platform and ii) extended version of FlexRAN SDN controller.

To support the drastically increasing traffic generated by devices at the edge of the network, 5G players are urged to rethink their infrastructure design. Unfortunately, conventional Cloud infrastructures struggle to adapt to the huge volume of traffic. In this context, Fog computing has been developed to bridge Cloud data centers and edge devices servicing a multitude of heterogeneous devices. These nearby nodes offer analytics and data storage capabilities increasing considerably the capacity of the infrastructure. However, provisioning IoT applications on such a heterogeneous infrastructure, while meeting their stringent requirements is extremely challenging. In this paper, we study the Fog service provisioning issue in a practical manner. In this regard, we propose a novel strategy, which we call GO-FSP. GO-FSP optimizes the placement of IoT application components while coping with their strict performance requirements. To do so, we first propose an Integer Linear Programming (ILP) formulation for the IoT application provisioning problem. The latter targets to minimize the deployment cost while ensuring a load balancing between heterogeneous devices. Then, a GRASP-based approach is proposed to achieve the aforementioned objectives. Finally, we make use of the FITOR orchestration system to evaluate the performance of our solution under real conditions. Obtained results show that our scheme outperforms the related strategies.

5G radio access networks are expected to leverage the Cloud environment for building a cost effective network infrastructure. Advanced mechanisms with functional split are introduced to split the RAN functionalities into centralized and distributed locations. This novelty has brought more flexibility to RAN deployment but is still conditioned to the radio resource availability. In this paper, we propose a new orchestration framework for joint radio and functional split scheme on user basis. Specifically, we address the orchestration of heterogeneous resources in a multi-sited Cloud-RAN infrastructure. The key idea behind our proposal is to optimize jointly the functional split and End-to-End resource allocation in order to achieve an enhanced throughput satisfaction and a low deployment cost. Results show that our approach E2E-US optimizes the heterogeneous resource usage. Indeed, the appropriate user radio load and functional split are dynamically and jointly selected, which outperforms cell centric functional split approaches.

In this paper, we address the scalability issue in designing LTE-D2D-based offloading schemes. We propose a scalable method, named JRW-D2D-SC, that offloads the intracellular unicast/multicast traffic using a side-links network of User Equipments (UEs). The devised scheme does this while keeping the control plane inside the LTE-A base station (eNB). By selecting relays and allocating OFDMA resources, the eNB manages to reroute the unified-model traffic from sources to destinations alleviating the cellular infrastructure from the data-plane overhead. To increase the utility of the LTE-D2D relaying network, the eNB solves the routing and the resource block allocation problem simultaneously. Like its counterpart in our previous work, JRW-D2D-SC addresses factors that limit the spectrum reuse and other LTE-D2D limitations such as half-duplex operation and contiguity in resource block allocations. However, we base our proposal on a novel formulation for the problem. The scheme employs an algorithm based on the Branch-and-Cut method to solve the resulted Mixed-Integer Linear Problem (MILP). In doing so, JRW-D2D-SC is more scalable than its counterpart in the previous work and can handle more dense deployments of UEs which is typical in the targeted crowded-platform scenarios: such as in stadiums, waiting-halls in airports and train stations. Based on our home-grown LTE-D2D module for NS-3, extensive network simulations demonstrate that JRW-D2D-SC maintains the same performance metrics or better in small-scale deployments of the LTE-D2D relays while being able to extend the advantage of the offloading system to large-scale deployments.

The next 5G infrastructure is expected to serve a multitude of services with heterogeneous requirements, which might be potentially managed by multiple Mobile Virtual Network Operators (MVNOs) that share the same network infrastructure. The new emerging technologies, such as i) Software Defined Networking (SDN), ii) Network Function Virtualization (NFV) and iii) Network Slicing, where physical resources are partitioned and allocated in an isolated manner to a set of services or to MVNOs according to a specific Service Level Agreement (SLA), are seen as the key enabling approaches to fulfill the diversity of requirements of 5G services in a cost-effective manner. In this paper, we design and prototype a network slicing solution, which we have developed in a Cloud-RAN (C-RAN) infrastructure based on the Open Air Interface (OAI) platform and FlexRAN SDN controller. The aim of our work is to validate the feasibility of the prototype in handling the creation and configuration of network slices on-demand, taking into account some requirements that are elaborated from SDN-based slicing applications. By means of emulations, we show that our prototype reacts well to the inputs coming from the SDN application and is finally capable of providing isolation among multiple slices in a dynamic fashion.

Network slicing is considered a key technology for the upcoming 5G system, enabling operators to efficiently support multiple services with heterogeneous requirements, over a common shared infrastructure. In this demo, we present a prototype for managing network slices in the Cloud Radio Access Network (C-RAN), which is considered the reference network architecture for 5G. Our prototype deals with the spectrum slicing problematic and aims at efficiently sharing the bandwidth resources among different slices, while considering their requirements. The prototype makes use of the Open Air Interface (OAI) platform and a specific Software Defined Network (SDN) controller, known as FlexRAN. By using real smart-phones, we run experiments on stage to validate the feasibility of the prototype in configuring multiple slices on-demand, driven by the input of a northbound application.

The massive adoption of Cloud technology in mobile access networks has driven the operators and vendors to work together in order to make Radio Access Network (RAN) ecosystem more agile. In this context, the virtualization of network functions is the cornerstone of a successful Network Function Virtualization (NFV) environment. However, the stringent requirements of RAN functions make their deployment in a Cloud infrastructure more complex and prone to performance issues. In this respect, this paper puts forward a novel approach that adopts an agile orchestration of fine-grained RAN network functions in order to achieve higher flexibility and improve performances. Specifically, we address the orchestration of baseband processing network functions in a multi-sited Cloud infrastructure. To do so, we propose a user-centric solution, denoted UCS-CRAN, that optimizes the split of the baseband units, while considering both the requirements of its processing network functions and the capabilities of Cloud infrastructure. Based on extensive simulations, the results show that our proposal optimizes both processing and bandwidth usage while minimizing the energy consumption compared to cell-centric, distributed and centralized Cloud-RAN approaches.

5G aims to maximize the data rate and to handle the billions of video, voice, data and IoT flows. For this reason, the macro-cells will be very congested and may fail to satisfy the end-users. In this context, data off loading scheme is conceived to route intra-cell traffic among the D2D-enabled user equipments reusing wireless uplink resources and thus increasing the overall spectral efficiency. In this paper, we address the joint routing and OFDMA resource allocation problem in D2D network. To do so, first we formulate the problem as Mixed Integer Linear Programming. The model takes into account factors that limit spectrum reuse as well as other LTE-D2D technology constraints such as: half-duplex operation and contiguity in resource block allocations. Then, we propose a novel scheme named Joint Routing and Wireless allocation in D2D communications (JRN-D2D) which is based on the branch-and-cut algorithm. In order to gauge the effectiveness of our proposal, we implement the standard LTE- D2D protocol stack, including our scheme JRN-D2D, in the NS-3 network simulator. The results obtained are very promising in terms of reliability, ratio of admitted D2D flows and latency in comparison to other basic one-sided optimal strategies including an interference-aware heuristic scheme.

An offloading scheme based on LTE-D2D is pro­posed in this paper to route the intracellular multicast traffic via a network of D2D-enabled User Equipments (UEs). The latter are ready to cooperate under the control of the eNodeB to carry and deliver the traffic. In doing so, the UEs reuse uplink resources granted by the eNodeB and thus, increasing the overall spectral efficiency while reducing the traffic load on the eNodeB. In this paper, we address the joint multicast routing and OFDM resource allocation problem in the D2D network to accomplish the offloading task. To do so, first we formulate the problem as an Integer Linear Programming (ILP) model which takes into account factors that limit spectrum reuse in addition to other LTE-D2D limitations: half-duplex operation and contiguity in resource block allocations. Then, we propose a novel scheme named Joint Multicast Routing and Wireless allocation in D2D communications (JRW-D2D-MC). The devised scheme consists of two-stage algorithm which, first, performs a pre-admittance filtering of flows that can be routed considering the current state of the network. Then, it makes use of the branch-and-cut method to solve the reduced ILP model. To evaluate effectiveness of our proposal, we implement the LTE-D2D standard in a network simulator NS-3. The results are very good in terms of flow-acceptance rate and latency.

To support the drastically increasing data demands, Internet giants are urged to rethink their data center design. Unfortunately, the conventional wired data centers struggle to resist to the huge volume of traffic. In this regard, we investigate a radically new methodology by augmenting the wired Data Center Network (DCN) with wireless communication (60 GHz technology). Heretofore, only few researches have dealt with the optimization of multi-hop communications in such Hybrid DCN (HDCN) infrastructures. In this paper, we address the joint routing and channel allocation issue for batched flow requests within HDCN. We propose a novel strategy named Joint Batch Routing and Channel Assignment Heuristic for HDCN (JBH-HDCN). To do so, we first formulate the problem based on an advanced Multi-Commodity Flow model with interference constraints. Then, we propose i) a heuristic-based solution to find the best sequence to process the batched flow requests, and ii) an advanced Dijkstra algorithm to jointly route and assign channels. We assess the performances of our solution JBH- HDCN under real conditions, within Cisco's MSDC infrastructure, using both: i) Altoona Facebook's DCN workload and ii) uniform traces. To do so, a full protocol stack is implemented within QualNet simulator and extensive simulations are conducted. Obtained results show that our scheme outperforms the related strategies.

The massive adoption of Cloud services has led to the explosion of traffic transiting over the Cloud infrastructure. Such an impressive evolution of data demand will inevitably be the catalyst of Operator infrastructure transformation. In this context, Software Defined Networking (SDN) is the technology that is shaping the future of carriers' networks. SDN considerably reduces the complexity of managing the network infrastructure while providing tremendous computational power compared to legacy devices. In this paper, we address the resource allocation issue in Wide Area Networks (WAN) while considering the requested QoS. To do so, we design an SD-WAN architecture to enhance the network resources allocation and hence improve the QoS of distributed applications. We formulate first the path computation problem as an Integer Linear Program while taking into consideration both network application requirements and the network occupation status. The problem is then resolved in a polynomial time leveraging the Branch-and-Cut algorithm. Results obtained with our experimental platform, show that the proposed SD-WAN framework outperforms the most prominent related solutions in terms of applications' satisfaction level and consumption of network's resources.

Global IP traffic is forecast to triple by 2020 to reach 2.3 ZB per year. Such an explosion will inevitably be the catalyst of Operator infrastructure transformation. In this context, SDN is the technology that is shaping the future of carriers' networks. It offers the opportunity to implement more powerful control algorithms. In this perspective, we put forward a SD-WAN architecture to enhance the network resources allocation and hence improve the QoS of distributed applications. The main idea is to take profit from the accurate network view provided by the controller to optimize the flows routing in WAN environments. To do so, we formulate the path computation problem as an Integer Linear Program by taking into consideration both network application requirements and the network occupation status. The problem is then resolved in a polynomial time leveraging the branch-and-cut algorithm. Results obtained based an experimental platform show that our ONOS SDN framework outperforms the most prominent related work solutions in terms of network consumption and applications satisfaction level.

The explosion of traffic demands within data centers accentuates the conventional infrastructures ossification. Unfortunately, the traditional tree based architectures struggle to resist and suffer from congestion and wiring complexity which deeply deteriorate the network performance. In aim to address the QoS degradation, we put forward a Hybrid Data Center Network (HDCN) architecture based on CISCO's Massively Data Center (MSDC) model. Specifically, it combines both the wired (Fiber and/or Gigabit Ethernet) and wireless communications based on 60 GHz band (IEEE 802.11ad). Note that few related work have dealt with the optimization of multi-hop communications in HDCN infrastructures and hence the problem is still open. In this paper, we tackle the challenge of jointly i) routing and ii) allocating wireless channels for intra data centers flows, while considering beamforming antennas. For that, we propose a new strategy named Joint Routing and Channel Allocation Algorithm (JRCA-HDCN). To do so, first the problem is formulated as a Minimum Weight Perfect Matching. Then, our resolution is based on the Blossom algorithm. JRCA-HDCN aims to maximize the throughput of intra-HDCN communication over the wireless and/or wired infrastructure. Based on extensive simulations, conducted in QualNet simulator while considering the full protocol stack, the obtained results show that our proposal outperforms the related prominent strategies in terms of i) end-to-end delay, ii) throughput and iii) spectrum spatial reuse.

Data centers are dealing with a rich panoply of applications which are distributed across thousands of servers. In this context, Cloud providers seek to maximize their revenue by meeting the tremendous traffic demand. Unfortunately, the conventional wired infrastructures struggle to resist to such a traffic explosion and new innovative techniques are required. In this respect, we put forward a Hybrid Data Center Network architecture based on the CISCO's Massively Scalable Data Center model, that leverages both the wired (Fiber/Ethernet) and wireless (IEEE 802.11ad) infrastructures. In this paper, we address the problem of batch-routing jointly to spectrum allocation of intra-data center communication flows. To do so, first, we formulate the problem based on Multi-Commodity Flow problem while considering interference constraints. Then, we propose a new strategy named Joint Batch Routing and Channel assignment approach in HDCN (BR-HDCN). Based on extensive simulations conducted in QualNet simulator while considering the full protocol stack, the obtained results for both: i) real Facebook's DC, and ii) uniform, traces, show that our proposal outperforms the related prominent strategies.

To cope with the unprecedented traffic explosion, Internet giants are urged to rethink their data center design. Unfortunately, the conventional wired data centers struggle to support the impressive growth of both data and online services. In this regard, we resort to augmenting the wired data center network (DCN) with wireless communication (60 GHz technology). Heretofore, only few research work have dealt with the optimization of multi-hop communications in such hybrid DCN (HDCN) infrastructures. In this paper, we investigate, in HDCN, the issue of jointly: 1) routing and 2) channel allocating, in both online and batch arrival modes. First, we formulate the online joint routing and channel assignment problem as a minimum weighted perfect matching problem. We propose a new strategy named joint routing and channel allocation algorithm that makes use of the Blossom algorithm and sequentially computes the optimal routing communication paths. Second, to handle the batched arrivals of flows, we formulate the batch joint routing and channel assignment problem as an advanced multi-commodity flow model. We propose two scalable approaches: 1) a heuristic based solution, named joint routing and channel assignment heuristic and 2) an approximate solution, named scalable joint batch routing and channel allocation, based on the Lagrangian relaxation technique. Based on extensive network simulations conducted in QualNet simulator while considering the full protocol stack, the obtained results for both: 1) real Facebook's data center and 2) uniform, traces, show that our schemes outperform the related strategies.

In this paper, we tackle the virtual network (VN) embedding problem within Cloud's backbone network by taking into consideration the impact of physical equipment outages. Our main focus is to improve the Cloud provider's (CPs) revenue by: 1) maximizing the acceptance rate of VNs within the Cloud's backbone and 2) minimizing the penalties induced by service disruption due to the hardware outages. This optimization problem is non-linear multi-objective and it has been proven to be NP-hard. To cope with this complexity, we propose an advanced coordination game for VN embedding (Advanced-CG-VNE). In this mapping (i.e., embedding) game, fictitious players are playing on behalf of the CP in order to maximize the turnover. The decision makers cooperate in aim to converge to a Nash Equilibrium that we prove the existence of and the matching with a social optimum. Two variants of Advanced-CG-VNE are proposed according to the virtual links embedding approach. The first one, denoted by Advanced-CG-VNE-unsplittable, embeds each virtual link in only one substrate path. The second variant, denoted by Advanced-CG-VNE-splittable, dispatches the required bandwidth of a virtual link among a set of substrate paths. Advanced-CG-VNE does not allocate any backup to handle service interruption caused by hardware failures. Our proposal adopts preventive and reactive mechanisms to palliate substrate failures. Based on extensive simulations to gauge the effectiveness of Advanced-CG-VNE, the obtained results show that our proposal outperforms the most prominent related strategies in terms of: 1) rejection rate of VNs; 2) rate of VNs impacted by physical failures; and 3) CPs turnover.

In this paper, we propose a novel 3D Underwater Wireless Sensor Network Deployment scheme for solid detection in rivers. Our objective is to minimize the number of deployed underwater sensors within a target field installation while ensuring i) the required Quality of Monitoring (QoM) (i.e., differentiated probabilistic detection) and ii) wireless network connectivity. To generate the best topology, we propose a novel deployment heuristic, named 3D-UWSN-Deploy, based on a subcube tessellation of the monitored field installation and a mixed integer linear program optimization. To gauge the effectiveness of 3D-UWSN-Deploy, we compare it with the most prominent related strategies. Simulation results show that our proposal is scalable and obtains the best performance in terms of cost deployment, quality of monitoring and connectivity.

We address the online virtual network embedding problem within the Cloud's backbone to optimally map the virtual routers and links in the substrate network in order to maximize the Cloud's provider revenue. Since the problem is NP-hard, we propose a novel approach, named VNE-GH, to significantly reduce the problem size using the Gomory-Hu transformation without losing useful information on the virtual network embedding problem. Starting from the Gomory-Hu compact tree structure, we formulate the virtual network embedding as an Integer Linear Program and resolve the reduced size problem using the branch- and-cut algorithm. Results obtained via extensive simulations show that VNE-GH outperforms the most prominent related work strategies in terms of i) acceptance rate of virtual network requests and ii) Cloud provider's revenue.

In this paper, we address the survivable batch-embedding virtual network problem within Cloud's backbone. In fact, the batch mapping of virtual networks will enhance the cumulative Cloud provider's revenue thanks to the global view of the incoming requests during a predefined time slot. Hence, the differentiation between requests can be performed and the arrival order of requests is ignored. The embedding of one virtual network is NP-hard. Adding the batch processing of the requests will further increase the complexity of the problem. In order to skirt the exponential complexity, we formulate the problem as building and researching problems within a decision tree. To resolve it, we propose a novel reliable batch-embedding virtual network strategy denoted by BR-VNE. It is based on Monte-Carlo Tree Search optimization method in which the upper confidence bounds can be reached in polynomial time. Based on extensive simulations, the results obtained show that BR-VNE outperforms the related work in terms of i) acceptance rate of virtual network requests, ii) Cloud provider's revenue and iii) rate of requests impacted by physical failures within the Cloud's backbone.

International audience

In this paper, we propose a new 2-Dimensional Underwater Barrier Deployment Algorithm (2D-UBDA) ensuring the barrier detection of toxic substances in a river. Our objective is to guarantee a full detection of chemical pollutant sources, while minimizing the deployment cost. To achieve this, first 2D-UBDA determines the potential deployment areas within a predefined target field installation and this, for each pollution source, by using a 3D-propagation model of a substance to predict its molarity in any point within the river. Then, based on an integer linear programming algorithm, 2D-UBDA selects the minimum number of sub-areas in which chemical sensors will be deployed by taking into consideration the intersections between the potential deployment zones of all pollution sources located upstream of the target field installation. To validate our proposal, the Pamplonita river located in Amazon rainforest is used as a case of study. Based on extensive simulations, 2D-UBDA outperforms the basic deployment strategies in terms of number of chemical sensors and successful detection of pollutant.

In this paper, we propose a new survivable virtual network mapping strategy within Cloud's backbone enhancing the Cloud Provider's revenue and dealing with physical failures of routers and links. In order to skirt the exponential complexity of the mapping, we propose a new reliable embedding strategy, denoted by CG-VNE, based on coordination game framework. To do so, we have formulated the problem as two interleaved coordination games. The first game addresses the virtual routers' mapping. In fact, the actions of each virtual router player strongly depend on the mapping of its attached virtual links. Hence, the second game is launched to embed the virtual links. Note that with both games, fictitious players cooperate to reach Nash Equilibrium of which we have proven the existence and it corresponds to a social optimum. CG-VNE aims to maximise the Cloud's provider revenue by maximising the acceptance rate of clients, as well as minimise the blackout rate of virtual networks caused by the outage of substrate routers and/or links. Based on extensive simulations, the results obtained show that CG-VNE has the best performance in terms of i) rejection rate of new clients, ii) Cloud's revenue and iii) rate of clients impacted by physical failures.

Cloud Computing offers elasticity and enhances resource utilisation. This is why its success strongly depends on the efficiency of the physical resource management. This paper deals with dynamic resource reconfiguration to achieve high resource utilisation and to increase Cloud providers income. We propose a new adaptive virtual network resource reconfiguration strategy named VNR-GA to handle dynamic users' needs and to adapt virtual resource allocation according to the applications' requirements. The proposed algorithm VNR-GA is based on Genetic metaheuristic and takes advantage of resources migration techniques to recompute the resource allocation of instantiated virtual networks. In order to optimally adapt the resource allocation according to customers' needs growth, the main idea behind the proposal is to sequentially generate populations of reconfiguration solutions that minimise both the migration and mapping cost and then select the best reconfiguration solution. VNR-GA is validated by extensive simulations and compared to the most prominent related strategy found in literature (i.e., SecondNet). The results obtained show that VNR-GA reduces the rejection rate of i) virtual networks and ii) resource upgrade requests and thus enhances Cloud Provider revenue and customer satisfaction. Moreover, reconfiguration cost is minimised since our proposal reduces both the amount of migrated resources and their new mapping cost.

In this paper, we study the wireless sensor network deployment for water pollution monitoring in the Amazon rainforest rivers. Our objective consists in minimising the number of deployed geographical field installations along the river, while ensuring the detection of the substance spilled in the given river regardless of the position of its source. A geographical field installation is formed by a set of barrier coverage underwater sensors which detect the pollutant if its molarity in the water is greater than a predefined threshold. Indeed, the substance molarity is inversely proportional to the moving distance. To generate the best topology, we propose a sub-optimal novel geographic Installation Field Deployment Algorithm based on the Backtracking heuristic named BT-FIDA. Since the river has a several forks, in order to reduce the number of installation fields, BT-FIDA minimises the rate of at least 2-covered river segments. The simulation results obtained show that our proposal minimises the number of field installations (i.e., deployment cost) while minimising the rate of areas which are miss-covered and over-covered.

In this paper, we propose a new preventive reliable virtual network embedding algorithm denoted by PR-VNE within the Cloud's backbone network. The proposal does not allocate any backup resources and takes into consideration the ageing of the hardware backbone network. The main objective is to maximise the number of hosted virtual networks while minimising the rate of crashed virtual networks impacted by physical (i.e., routers or links) failures. The problem is a multi-objective non-linear optimisation and classified as NP-hard. To overcome its complexity, PR-VNE is based on the artificial bee colony metaheuristic. Moreover, it makes use of a multi commodity flow algorithm in order to maximise the load balancing of bandwidth usage within the physical network. Based on extensive simulations, the performance obtained is better than the related strategies found in literature in terms of reject and blackout rates of virtual networks.

In this paper, we propose a new dynamic adaptive virtual network resource allocation strategy named Adaptive-VNE to deal with the complexity and the inefficiency of resource allocation. The proposal coordinates virtual node and virtual link mapping stages. The main idea behind the proposal is take advantage of unused bandwidth with respect to the occupancy rate of embedded virtual links. Hence, the unused bandwidth will be reassigned to incoming virtual network requests. To do so, Adaptive-VNE adopts the “divide and conquer” strategy. It divides the virtual network request topology into many star topologies. Then, the mapping of each piece within the whole topology is formulated as a K-supplier problem and resolved by an approximation bottleneck algorithm. To generate the global virtual network topology, Adaptive-VNE uses a backtracking algorithm in order to minimise the global mapping cost. Note that the proposal forecasts usage rate of virtual links and adapts their bandwidth reservation. Adaptive-VNE was validated by simulations and compared to the related strategies found in literature. The results obtained show that, contrarily to static bandwidth allocation approaches, the adaptive strategy maximises substrate bandwidth usage while the virtual links' bottleneck rate is minimised. Moreover, the congestion periods are minimised and during the bottleneck the bandwidth satisfaction is maximised. Finally, Adaptive-VNE improves performances in terms of acceptance rate of virtual networks and revenue of infrastructure providers.

Sky computing is a promising concept enabling a flexible deployment of geographical distributed applications. Whereas, it is faced with a fundamental challenge which is: “efficient resource utilisation” within Cloud's infrastructure. Hence, a high flexible and intelligent resource allocation scheme is necessary to accommodate unpredictable and variable users demands. This paper tackles the fundamental challenge of efficient resource allocation within Cloud's backbone network. The ultimate goal is to satisfy the Cloud's user requirements while maximising Cloud provider's revenue. The problem consists in embedding virtual networks within substrate infrastructure. A new dynamic adaptive virtual network resource allocation strategy named Backtracking-VNE is investigated to deal with the complexity of resource provisioning within Cloud network. The proposal coordinates virtual nodes and virtual links mapping stages to optimise resources usage. Moreover, thanks to forecasting module, Backtracking-VNE guarantees an efficient resources share between embedded virtual links with respect to their occupancy. We demonstrate through extensive simulations that contrarily to static bandwidth allocation approaches, Backtracking-VNE enhances substrate bandwidth usage whilst minimising virtual links congestion. Acceptance rate of virtual networks and Cloud providers income are also improved compared with related strategies.

In this paper we address the problem of virtual network reconfiguration. In our previous work on virtual network embedding strategies, we found that most virtual network rejections were caused by bottlenecked substrate links while peak resource use is equal to 18%. These observations lead us to propose a new greedy Virtual Network Reconfiguration algorithm, VNR. The main aim of our proposal is to 'tidy up' substrate network in order to minimise the number of overloaded substrate links, while also reducing the cost of reconfiguration. We compare our proposal with the related reconfiguration strategy VNA-Periodic, both of them are incorporated in the best existing embedding strategies VNE-AC and VNE-Greedy in terms of rejection rate. The results obtained show that VNR outperforms VNA-Periodic. Indeed, our research shows that the performances of VNR do not depend on the virtual network embedding strategy. Moreover, VNR minimises the rejection rate of virtual network requests by at least ≃83% while the cost of reconfiguration is lower than with VNA-Periodic.

In this paper, we address a virtual network embedding problem. Indeed, our objective is to map virtual networks in the substrate network with minimum physical resources while satisfying its required QoS in terms of bandwidth, power processing and memory. In doing so, we minimize the reject rate of requests and maximize returns for the substrate network provider. Since the problem is NP-hard and to deal with its computational hardness, we propound a new scalable embedding strategy named \texttt{VNE-AC} based on the Ant Colony metaheuristic. The intensive simulations and evaluation results show that our proposal enhances the substrate provider's revenue and outperforms the related strategies found in current literature.