To implement a Network Service (NS) within a Network Function Virtualization (NFV) environment, it is essential to create a sequence of connected Virtual Network Functions (VNFs), known as a VNF Forwarding Graph (VNF-FG), and then embed it onto the substrate network. The emergence of VNF decomposition as a new functional architecture allows VNFs to be broken down into smaller sub-functions, offering enhanced flexibility, resource sharing, and scalability. VNF decomposition can significantly reduce VNF embedding costs since different sub-functions can be efficiently reused by multiple network requests. However, when VNFs are decomposed into multiple sub-functions, selecting the appropriate decomposition option for each VNF and constructing the VNF-FG to embed onto the substrate network poses a significant challenge in NFV resource allocation (NFV-RA). A key challenge is identifying the optimal decomposition option among all possible choices for VNF embedding. In this paper, we introduce a cost-aware algorithm designed to address the topological decomposition of VNF-FGs, focusing on minimizing embedding costs while meeting specified service requirements. We formulate the VNF topology decomposition problem using Integer Linear Programming (ILP) to select the best decomposition option and minimize the embedding cost. Furthermore, we propose four efficient heuristics for different topologies to identify the optimal decomposition options for network embedding. Simulation results demonstrate that our proposed algorithm outperforms existing benchmarks in terms of embedding costs and achieves execution times that are up to 95% better than the SE approach.

Resource dimensioning refers to the process of determining the amount of resources needed to achieve target KPIs of Virtual Network Functions (VNFs) in a Network Service (NS) in a cost-effective fashion. VNFs in an NS usually have some dependency relationship among themselves. This relationship can either be represented as a chain of VNFs (in the case of Service Function Chain “SFC”), or by more general graph topologies (Virtual Network Function – Forwarding Graph “VNF-FG”). In cloud computing, a similar concept can be found in microservices whereby microservices interact with each other to implement the functionality of the application. The dependencies between the VNFs (or microservices) imply that the performance of a given VNF does not depend only on the amount of resources available to itself, but also on the performance of other VNFs on which it depends. This makes developing accurate solutions for resource dimensioning a challenging task. In this paper, we propose a Hybrid Reinforcement Learning (RL)-based solution to automatically generate resource amounts for VNFs such that they meet the expected performance of the NS with minimal resources. The proposed solution relies both on a simulation and a real cloud environment. We evaluated the performance of the proposed solution in terms of training and inferencing convergence, and inferencing time. The results demonstrate that our training and inferencing algorithms successfully converge to an optimal solution.

Network Function Virtualization (NFV) is a network architecture that separates network functions from dedicated hardware, implementing them as software modules known as Virtual Network Functions (VNFs), which are executed in virtual machines or containers. NFV increases the deployment flexibility and agility within operator networks and reduces the operating and capital expenditures significantly. In NFV, migration of VNFs can significantly reduce the embedding cost. However, stringent latency requirements between VNFs can make them tightly coupled, thus hindering each VNF from being migrated individually, and resulting in poor performance. One of the main challenges in an NFV environment is therefore to migrate a cluster of VNFs to minimize the embedding cost. In this paper, we aim to solve the problem of cluster VNF migration by considering the given inter-VNF latency requirements. We formulate the VNF migration problem as an Integer Linear Programming (ILP) and present two scalable and efficient algorithms for migrating a cluster of VNFs. Through extensive experiments, we show that our proposed algorithms are highly effective. They reduce the total embedding cost by 14% compared to the existing heuristics, while being much more scalable in terms of execution time compared to the brute-force approach.

Network Function Virtualization (NFV) enables the decoupling of network functions from dedicated hardware to run them as software instances on commodity servers through virtualization, replacing hardware-based network functions with software-based Virtual Network Functions (VNFs). In this paper, we study the joint problem of VNF decomposition and migration to address VNF embedding in NFV resource allocation (NFV-RA). More specifically, we investigate how VNF migration and VNF decomposition can be mutually beneficial to minimize the embedding cost of network services. After presenting a novel formulation of the problem as an integer linear programming (ILP), we validate it by CPLEX and show that our joint VNF decomposition and migration approach can outperform the VNF decomposition-only approach by 20% in terms of embedding cost.

To realize a Network Service (NS) in a Network Function Virtualization (NFV) network, it is needed to form an ordered set of connected Virtual Network Functions (VNFs), commonly referred to as VNF Forwarding Graph (VNF-FG), and then embed it onto the substrate network. Forming a VNF-FG is a challenging step of NFV resource allocation (NFV-RA), especially when the VNFs can be further decomposed into different sub-functions. In this paper, we propose a cost-aware algorithm to solve the problem of topological decomposition of VNF-FGs with the main objective of minimizing the embedding cost while satisfying the given service requirements. The simulation results indicate that our proposed algorithm outperforms the existing benchmark in terms of embedding cost, while being significantly scalable compared to the brute-force approach.

By decoupling network functions from the underlying hardware, Network Function Virtualization (NFV) allows application components to be implemented as sets of Virtual Network Functions (VNFs) chained in a specific order, represented by VNF-Forwarding Graphs (VNF-FG). Fog computing is instrumental to tap into the full potential of NFV by deploying VNFs in close proximity to end-users, thus decreasing the latency significantly. However, the mobility of end-users and the fog nodes, and the limited fog nodes coverage results in service discontinuity and may increase application delay. Application component migration offers great potential to address this issue. In this paper, we propose a component migration strategy in an NFV-based hybrid cloud/fog system considering the mobility of both end-users and fog nodes. We use the Gauss-Markov mobility model and a random walk mobility model for fog nodes and end-user devices, respectively. We modeled the problem mathematically, which minimizes the aggregated weighted function of application delay and cost. However, considering the mobility of both end-users and fog nodes makes the problem quite complex. Hence, we propose a Deep Reinforcement Learning (DRL) approach to decide where and when to migrate application components and to achieve rapid decision-making. Simulation results demonstrate that the proposed scheme performs well. It offers favorable convergence and outperforms existing algorithms in terms of application delay and migration costs.

Dynamic and zero-touch management is expected to be the key feature of next-generation 6G networks. Network Function Virtualization (NFV) is one of the key technologies for realizing such management through software-based networks. Despite great benefits offered by NFV, deploying network services (NSs) in NFV ecosystems remains a challenge, especially for latency-sensitive NSs, as they demand stringent latency requirements and fast service provisioning. Specifically, service graphs should be embedded into an infrastructure such that these requirements are satisfied while optimizing network operator’s objectives. To cope with the scalability of optimization-based approaches, heuristic methods are known as promising alternatives to find a satisfactory solution within an acceptable execution time. However, existing VNF embedding heuristics still suffer from the so-called causality issue, which may degrade the embedding solution quality. The causality issue means that embedding decisions cannot be optimally determined before all neighboring dependencies are known. To this end, we introduce our${h}$-horizon sequential look-ahead greedy embedding framework, which provides efficient embedding and re-embedding strategies to alleviate the impact of the causality issue. The simulation results indicate that our proposed algorithm significantly improves embedding cost, compared to the existing heuristic algorithms while being much more scalable than an optimization-based approach.

Internet of Things (IoT) applications are becoming more and more pervasive. However, they are usually embedded in the actual IoT devices. This precludes the efficient usage of IoT capabilities, particularly, sensing and actuation capabilities. Cloud computing enables the efficient usage of resources through the Infrastructure as a Service (IaaS) service model. Unfortunately, an IoT IaaS faces many challenges that the research community has not yet solved. Some examples are the heterogeneity of the devices, their orchestration, and the provision of bare-metal access. This paper proposes an architecture for a cloud IaaS in IoT settings. Modules, interfaces, and procedures are proposed. The validation is done by a proof of concept, with concrete measurements on the prototype and simulations. The prototype relies on real-life sensors (i.e., Virtenio and Advanticsys) and robots (i.e., EV3 LEGO Mindstorms).

Cloud computing, despite its inherent advantages (e.g., resource efficiency), still faces several challenges. The wide area network used to connect the cloud to end users could cause high latency, which may not be tolerable for some applications, especially Internet-of-Things (IoT) applications. Fog computing can reduce this latency by extending the traditional cloud architecture to the edge of the network and by enabling the deployment of some application components on fog nodes. Application providers use Platform-as-a-Service (PaaS) to provision (i.e., develop, deploy, manage, and orchestrate) applications in cloud. However, existing PaaS solutions (including IoT PaaS) usually focus on cloud and do not enable provisioning of applications with components spanning cloud and fog. Provisioning such applications requires novel functions, such as application graph generation, that are absent from existing PaaS. Furthermore, several functions offered by existing PaaS (e.g., publication/discovery) need to be significantly extended in order to fit in a hybrid cloud/fog environment. In this article, we propose a novel architecture for PaaS for hybrid cloud/fog system. It is IoT use case driven, and its applications' components are implemented as virtual network functions (VNFs) with execution sequences modeled as graphs with substructures, such as selection and loops. It automates the provisioning of applications with components spanning cloud and fog. In addition, it enables the discovery of existing cloud and fog nodes and generates application graphs. A proof of concept is built based on Cloudify open source. Feasibility is demonstrated by evaluating its performance when the PaaS modules and application components are placed in clouds and fogs in different geographical locations.

Fog computing extends the cloud to the edge of the network, close to the end-users enabling the deployment of some application component in the fog while others in the cloud. Network Functions Virtualization (NFV) decouples the network functions from the underlying hardware. In NFV settings, application components can be implemented as sets of Virtual Network Functions (VNFs) chained in specific order representing VNF-Forwarding Graphs (VNF-FG). Many studies have been carried out to map the VNF-FGs to cloud systems. However, in hybrid cloud/fog systems, an additional challenge arises. The mobility of fog nodes may cause high latency as the distance between the end-users and the nodes hosting the components increases. This may not be tolerable for some applications. In such cases, a prominent solution is to migrate application components to a closer fog node. This paper focuses on application component migration in NFV-based hybrid cloud/fog systems. The objective is to minimize the aggregated makespan of the applications. The problem is modeled mathematically, and a heuristic is proposed to find the sub-optimal solution in an acceptable time. The heuristic aims at finding the optimal fog node in each time-slot considering a pre-knowledge of the mobility models of the fog nodes. The experiment's results show that our proposed solution improves the makespan and the number of migrations compared to random migration and No-migration.

Fog computing reduces the latency induced by distant clouds by enabling the deployment of some application components at the edge of the network, on fog nodes, while keeping others in the cloud. Application components can be implemented as Virtual Network Functions (VNFs) and their execution sequences can be modeled by a combination of sub-structures like sequence, parallel, selection, and loops. Efficient placement algorithms are required to map the application components onto the infrastructure nodes. Current solutions do not consider the mobility of fog nodes, a phenomenon which may happen in real systems. In this paper, we use the random waypoint mobility model for fog nodes to calculate the expected makespan and application execution cost. We then model the problem as an Integer Linear Programming (ILP) formulation which minimizes an aggregated weighted function of the makespan and cost. We propose a Tabu Search-based Component Placement (TSCP) algorithm to find sub-optimal placements. The results show that the proposed algorithm improves the makespan and the application execution cost.

Internet of Things (IoT) is expected to enable a myriad of applications by interconnecting objects - such as sensors and robots - over the Internet. IoT applications range from healthcare to autonomous vehicles and include disaster management. Enabling these applications in cloud environments requires the design of appropriate IoT Infrastructure-as-a-Service (IoT IaaS) to ease the provisioning of the IoT objects as cloud services. This paper discusses a case study on search and rescue IoT applications in large-scale disaster scenarios. It proposes an IoT IaaS architecture that virtualizes robots (IaaS for robots) and provides them to the upstream applications as-a-Service. Node- and Network-level robots virtualization are supported. The proposed architecture meets a set of identified requirements, such as the need for a unified description model for heterogeneous robots, publication/discovery mechanism, and federation with other IaaS for robots when needed. A validating proof of concept is built and experiments are made to evaluate its performance. Lessons learned and prospective research directions are discussed.

Applications are sets of interacting components that can be executed in sequence, in parallel, or by using more complex constructs such as selections and loops. They can, therefore, be modeled as structured graphs with sub-structures consisting of these constructs. Fog computing can reduce the latency induced by distant clouds by enabling the deployment of some components at the edge of the network (i.e., closer to end-devices) while keeping others in the cloud. Network Functions Virtualization (NFV) decouples software from hardware and enables an agile deployment of network services and applications as Virtual Network Functions (VNFs). In NFV settings, efficient placement algorithms are required to map the structured graphs representing the VNF Forwarding Graphs (VNF-FGs) onto the infrastructure of the hybrid cloud/fog system. Only deterministic graphs with sequence and parallel sub-structures have been considered thus to date. However, several real-life applications do require non-deterministic graphs with sub-structures as selections and loops. This paper focuses on application component placement in NFV-based hybrid cloud/fog systems, with the assumption that the graph representing the application is non-deterministic. The objective is to minimize an aggregated weighted function of makespan and cost. The problem is modeled as an Integer Linear Programming (ILP) and evaluated over small-scale scenarios using the CPLEX optimization tool.

Large-scale disaster management applications are among the several realistic applications of the Internet of Things (IoT). Fire detection and earthquake early warning applications are just two examples. Several IoT devices are used in such applications, e.g., sensors and robots. These sensors and robots are usually heterogeneous. Moreover, in disaster scenarios, the existing communication infrastructure may become completely or partially destroyed, leaving mobile ad-hoc networks the only alternative to provide connectivity. Utilizing these applications raises new challenges such as the need for dynamic, flexible, and distributed gateways which can accommodate new applications and new IoT devices. Network functions virtualization (NFV) and software defined networking (SDN) are emerging paradigms that can help to overcome these challenges. This paper leverages NFV and SDN to propose an architecture for on-the-fly distributed gateway provisioning in large-scale disaster management. In the proposed architecture, the gateway functions are provisioned as virtual network functions that are chained on-the-fly in the IoT domain using SDN. A prototype is built and the performance results are presented.

In large-scale natural disasters, humans are likely to fail when they attempt to reach high-risk sites or act in search and rescue operations. Robots, however, outdo their counterparts in surviving the hazards and handling the search and rescue missions due to their multiple and diverse sensing and actuation capabilities. The dynamic formation of optimal coalition of these heterogeneous robots for cost efficiency is very challenging and research in the area is gaining more and more attention. In this paper, we propose a novel heuristic. Since the population of robots in large-scale disaster settings is very large, we rely on Quantum Multi-Objective Particle Swarm Optimization (QMOPSO). The problem is modeled as a multi-objective optimization problem. Simulations with different test cases and metrics, and comparison with other algorithms such as NSGA-II and SPEA-II are carried out. The experimental results show that the proposed algorithm outperforms the existing algorithms not only in terms of convergence but also in terms of diversity and processing time.

Fog computing brings cloud closer to end-users and data sources by enabling computation at the edge of the network. Low latency is the main benefit. IoT applications are often latency-sensitive. Such applications may be provisioned as component-based in a hybrid cloud/fog environment with components spanning cloud and fog. This will enable placing some of its components in the fog domain closer to the IoT devices, and consequently reduce the latency. However, provisioning applications in hybrid cloud/fog environment is still manual today. Existing PaaS do not support interacting with fog nodes, at the edge, for applications' components provisioning. This demo shows the key features of the hybrid Platform as-a-Service (PaaS) we have designed for IoT applications provisioning in cloud and fog environments. Three goals are assigned to the demo: (1) How applications can be designed and developed in such environments, (2) how the hybrid PaaS deploys the applications' components across cloud and fog nodes, and (3) how it executes and manages them using appropriate orchestration techniques.