Leveraging virtualization technology, Mobile Edge Computing (MEC) deploys multiple services with different execution time requirements running as isolated processes. For instance, both real-time (RT) and non-RT applications may be (are) running on the same infrastructure using containerized virtualization. Nevertheless, sharing resources (e.g., CPU) with collocated workloads could impact the RT performance of RT applications. This paper presents PRINCIPIA, a dynamic CPU and CPU-shares allocation mechanism that opportunistically enables non-RT applications to run on underutilized CPUs while providing RT guarantees to RT applications. By monitoring MEC’s system metrics like processor’s CPU utilization and container’s CPU usage, PRINCIPIA dynamically allocates both CPU and CPU-shares to containers running non-RT applications aiming at opportunistically exploiting underutilized CPUs by containers running RT applications. We evaluate PRINCIPIA on a small-scale MEC server which uses containerized virtualization along with Linux RT Kernel to deploy both RT and non-RT applications. Our findings show that PRINCIPIA mitigates the impact on the RT performance of RT applications providing bounded processing latency in comparison with the default host Kernel scheduler.

Leveraging virtualization technology, Cloud-RAN deploys multiple virtual Base Band Units (vBBUs) along with collocated applications on the same Mobile Edge Computing (MEC) server. However, the performance of real-time (RT) applications such as the vBBU could potentially be impacted by sharing computing resources with collocated workloads. To address this challenge, this paper presents a dynamic CPU sharing mechanism, specifically designed for containerized virtualization in MEC servers, that hosts both RT and non-RT general-purpose applications. Initially, the CPU sharing problem in MEC servers is formulated as a Mixed-Integer Programming (MIP). Then, we present an algorithmic solution that breaks down the MIP into simpler subproblems that are then solved using efficient, constant factor heuristics. We assessed the performance of this mechanism against instances of a commercial solver. Further, via a small-scale testbed, we assessed various CPU sharing mechanisms and their effectiveness in reducing the impact of CPU sharing on RT application processing performance. Our findings indicate that our CPU sharing mechanism reduces the worst-case execution time by more than 150% compared to the default host RT-Kernel approach. This evidence is strengthened when evaluating this mechanism within Cloud-RAN, in which vBBUs share resources with collocated applications on a MEC server. Using our CPU sharing approach, the vBBU’s scheduling latency decreases by up to 21% in comparison with the host RT-Kernel.

The adoption of new technologies and the increasing amount of connected devices have drawn the attention of cyber-attackers, whose intentions are oriented to disturb computational services or even steal critical information. This poses new challenges in the design of Intrusion Detection Systems (IDS), which are in charge of detecting threats. When dealing with unknown cyber-attacks, anomaly-based IDS (AIDS) have drawn the attention of the research community, since they could detect, for instance, zero-day attacks. A recurrent critical aspect in the design of these systems is the training procedures. In the context of attack detection, training involves difficulties due to the imbalanced nature (one class much more represented than the other) of the associated data sets. Hence, approaches to address the class imbalance are always relevant. In this work, we present the evaluation of different machine learning (ML) algorithms, known as one-class classifiers, that hold the potential to be implemented over an AIDS. For this task, we used the UNSW-NB15, comparing their performance using pertinent metrics that are normally used to assess an attack detection algorithm.

Recently, the operation of LTE in unlicensed bands has been proposed to cope with the ever-increasing mobile traffic demand. However, the deployment of LTE in such bands implies sharing spectrum with mature technologies such as Wi-Fi. Several studies have discussed this coexistence problem by suggesting that LTE implements different adaptation mechanisms that allow transmission possibilities to Wi-Fi. While such adaptation mechanisms exist, they still negatively impact Wi-Fi performance, mainly due to the lack of collaboration/coordination mechanisms that inform about the co-located networks' activities. In this paper, we propose a distributed spectrum management framework that enhances the performance of Wi-Fi, as a particular case, by detecting harmful co-located wireless networks and changes the Wi-Fi's operating central frequency to avoid them. The framework is based on a Convolutional Neural Network (CNN) that can identify different wireless technologies and provides spectrum usage statistics. Experiments were carried out in a real-life testbed, and the results show that Wi-Fi maintains its performance when using our framework. This translates in an increase of at least 40% on the overall throughput compared to a non-managed operation of Wi-Fi.

The experimental component of a computer networks online course usually brings challenges in its implementation, because of the limitations imposed by computer resources, use of physical devices and automatic assessment. This work presents the implementation of a remote laboratory that is part of a Massive Open Online Course, and allows multiple simultaneous students to run computer networks applications in programmable network simulators and in real network equipment, without the need for installing any specific software. This platform also enables the possibility of automatic assessment, thus easing the evaluation of the particular parameters defined during the course.

Through network virtualization, new networking paradigms can be easily implemented by sharing a common physical infrastructure between different users and applications. A fundamental aspect in this environment is how to efficiently map virtual resources onto physical resources, frequently referred as virtual network embedding. Integer linear programming, heuristics and meta-heuristics have been commonly used to solve this problem. However, efficient allocation algorithms cannot ignore the main characteristics of the physical infrastructure. Simultaneously, elastic optical networks (EONs), are gaining attention of researchers and industry due to their high spectrum efficiency, flexibility and adaptability that allow higher transmission rates able to cope with the tremendous IP traffic demand of new applications and services. In this paper, we formulate a mathematical model for the offline virtual optical network embedding assuming a transparent EON-based infrastructure. We then propose a meta-heuristic (PRVONE) based on an optical-path ranking system to provide scalable embeddings within reasonable running time. We evaluate our work through extensive simulations and compare it with state-of-the-art approaches. Results show that PRVONE can achieve improvements in terms of acceptance ratio in all test case scenarios while providing load balanced embeddings.

Network function (NF) virtualization is a promising way for service providers to improve configurability of the offered network services. First, NFs and their corresponding flows can be embedded in the substrate network in an automated way. Second, the modularity offered by the description of a service as a composition of NFs and their interconnecting virtual links, enables tuning of the service's logical structure. A major challenge with respect to the management of these environments is the placement of service requests in the physical infrastructure, comprising NFs that can only be instantiated on particular hosts, due to latency, legislation, or hardware-related constraints. These location constraints are known to complicate the resource allocation. This paper proposes two service placement algorithms with better coordination between the composition and embedding phases. We formulate the combined service embedding and chain composition problem as an integer linear program and then, propose a fast heuristic based on greedy chain selection that iteratively adds virtual NF chains to the composition and embeds them at the same time. Simulation experiments show that the proposed cross-layer optimization can significantly increase the acceptance ratio and reduce the embedding cost.

Cloud providers rely on fault-tolerance mechanisms to realize high-availability services on best-effort infrastructure. Service replication limits the data-loss caused by failure, at the expense of additional operational costs. Recently, with the advent of Mobile Edge Computing, cloud environments are becoming increasingly heterogeneous and dynamic, by the incorporation of (very) unreliable and resource-constrained devices. In this paper, we investigate how to devise an economically viable replication strategy, for a given service on a particular cloud environment. Previous work either focused on finding replication strategies for stateless services, ignoring recovery processes and correlated failures, or considered system dynamics, while lacking Service Level Agreement (SLA)-awareness. We approach the replica management problem as a run-time revenue maximization problem. Our proposed Dynamic Programming (DP) algorithm can generate the optimal replication strategy over the application lifetime. Through extensive simulations, we show that our algorithm significantly improves provider revenue over a wide range of cloud- and SLA-conditions, and adapt its strategy to evolving operating conditions. The results show that coupling dynamic failure models with SLA-awareness can lead to profitable replication strategies, even in cases where providers currently turn a loss.

The main resource allocation research challenge in network virtualization is the Virtual Network Embedding (VNE) Problem. It consists of two stages, usually performed separately: node mapping and link mapping. A new mathematical multi-constraint routing framework for linear and non-linear metrics called “paths algebra” has already been proposed to solve the second stage, providing good results thanks to its flexibility. Unlike existing approaches, paths algebra is able to include any kind of network parameters (linear and non-linear) to solve VNE in reasonable runtime. While paths algebra had only been used to solve one stage (link mapping) of the VNE, this paper suggests an improvement to solve VNE using the paths algebra-based strategy by coordinating, in a single stage, the mapping of nodes and links based on a ranking made of the bi-directional pair of nodes of the substrate network, ordered by their available resources. Simulation results show that the New Paths Algebra-based strategy shows significant performance improvements when compared against the uncoordinated paths Algebra-based link mapping approach.

Network functions virtualization (NFV) is a new network architecture framework where network function that traditionally used dedicated hardware (middleboxes or network appliances) are now implemented in software that runs on top of general purpose hardware such as high volume server. NFV emerges as an initiative from the industry (network operators, carriers, and manufacturers) in order to increase the deployment flexibility and integration of new network services with increased agility within operator's networks and to obtain significant reductions in operating expenditures and capital expenditures. NFV promotes virtualizing network functions such as transcoders, firewalls, and load balancers, among others, which were carried out by specialized hardware devices and migrating them to software-based appliances. One of the main challenges for the deployment of NFV is the resource allocation of demanded network services in NFV-based network infrastructures. This challenge has been called the NFV resource allocation (NFV-RA) problem. This paper presents a comprehensive state of the art of NFV-RA by introducing a novel classification of the main approaches that pose solutions to solve it. This paper also presents the research challenges that are still subject of future investigation in the NFV-RA realm.

Network Functions Virtualization (NFV) is an emerging initiative to overcome increasing operational and capital costs faced by network operators due to the need to physically locate network functions in specific hardware appliances. In NFV, standard IT virtualization evolves to consolidate network functions onto high volume servers, switches and storage that can be located anywhere in the network. Services are built by chaining a set of Virtual Network Functions (VNFs) deployed on commodity hardware. The implementation of NFV leads to the challenge: How several network services (VNF chains) are optimally orchestrated and allocated on the substrate network infrastructure? In this paper, we address this problem and propose CoordVNF, a heuristic method to coordinate the composition of VNF chains and their embedding into the substrate network. CoordVNF aims to minimize bandwidth utilization while computing results within reasonable runtime.

One of the main challenges of network virtualization is the mapping of virtual network demands to physical network resources, commonly known as the virtual network embedding (VNE) problem. This paper introduces DPVNE, a distributed, parallel and generic VNE framework. DPVNE can be used 1) to run various cost-reducing embedding algorithms 2) in a distributed way. Thereby, computational load for embedding multiple virtual networks is spread across the substrate network reducing workload of individual nodes and 3) enabling the embedding of multiple virtual networks in parallel. DPVNE, in contrast to existing distributed algorithms, 4) achieves lower message overhead and, despite of being distributed, 5) keeps embedding costs comparable to those of centralized approaches.

This work describes a novel approach for the reduction of energy consumption in data centres (DCs) that will yield benefits both in terms of running costs and its environmental impact. The method is based on the introduction of collaborative interactions and flexibility clauses in contracts between all the DC ecosystem entities. The included entities are all the actors along the energy production–consumption chain, from the energy provider to the Information Technology customer. The collaborative approach also integrates the interaction between federated DCs. In this paper, we find a detailed description of the architecture that enables interaction between the DC ecosystem parties, which is designed to be progressively deployed, allowing traditional and ‘greened’ services to coexist, and without modification of the existing DC automation and framework systems.