Section: New Results

Future networks and architectures

Participants: Jean-Michel Sanner, Hamza Ben Ammar, Louiza Yala, Yassine Hadjadj-Aoul, Gerardo Rubino

SDN and NFV placement. Mastering the increasing complexity of current and future networks, while reducing the operational and investments costs, is one of the major challenges faced by network operators (NOs). This explains in large part the recent enthusiasm of NOs towards Software Defined Networking (SDN) and Network Function Virtualization (NFV). Indeed, on the one hand, SDN makes it possible to get rid of the control plane distribution complexity, by centralizing it logically, while allowing its programmability. On the other hand, the NFV allows virtualizing the network functions, which considerably facilitates the deployment and the orchestration of the network resources. Providing a carrier grade network involves, however, several requirements such as providing a robust network meeting the constraints of the supported services. In order to achieve this objective, it is clearly necessary to scale network functions while placing them strategically in a way to guarantee the system's responsiveness.

The placement in TelCo networks are generally multi-objective and multi-constrained problems. The solutions proposed in the literature usually model the placement problem by providing a mixed integer linear program (MILP). Their performances are, however, quickly limited for large sized networks, due to the significant increase in the computational delays. In order to avoid the inherent complexity of optimal approaches and the lack of flexibility of heuristics, we propose in [54] a genetic algorithm designed from the NSGA II framework that aims to deal with the controller placement problem. Genetic algorithms can be both multi-objective, multi-constraints and can be designed to be implemented in parallel. They constitute a real opportunity to find good solutions to this category of problems. Furthermore, the proposed algorithm can be easily adapted to manage dynamic placements scenarios. In [55], our main focus were devoted to maximize the clusters average connectivity and to balance the control's load between clusters, in a way to improve the networks' reliability.

We focus, in [60], on the problem of optimal computing resource allocation and placement for the provision of a virtualized Content Delivery Network (CDN) service over a telecom operator's Network Functions Virtualization (NFV) infrastructure. Starting from a Quality of Experience (QoE)-driven decision on the necessary amount of CPU resources to allocate in order to satisfy a virtual CDN deployment request with QoE guarantees, we address the problem of distributing these resources to virtual machines and placing the latter to physical hosts, optimizing for the conflicting objectives of management cost and service availability, while respecting physical capacity, availability and cost constraints. We present a multi-objective optimization problem formulation, and provide efficient algorithms to solve it by relaxing some of the original problem's assumptions. Numerical results demonstrate how our solutions address the trade-off between service availability and cost, and show the benefits of our approach compared with resource placement algorithms which do not take this trade-off into account.

Real-time NFV placement in edge cloud. Sometimes, the placement of NFV can not be planned in advance and therefore requires real-time placement as requests arrive. The placement is particularly challenging with the recent development of geographically distributed mini data centers, also referred to as cloudlets, at the edge of the network (i.e., typically at Points of Presence (PoPs) level). These edge data centers have rather small capacities in terms of storage, computing and networking resources, when compared with the huge centralized data centers deployed today.

All these radical changes in NOs' infrastructures raise many new issues (especially in terms of resource allocation), which so far have not been considered in the cloud literature. Traditionally, resources in cloud platforms are considered as to be infinite and request blocking is most of the time ignored when evaluating resources' allocation algorithms, precisely because of this infinite capacity assumption. However, if we assume that the NO's infrastructure will very likely be composed of small data centers with limited capacities, and deployed at the edge of network, the congestion of such a system may occur, notably if the demand is sufficiently high and exceeds what the infrastructure can handle at a given time.

We proposed in [57] an analytical model for the blocking analysis in a multidimensional cloud system, which was validated using discrete events' simulations. Besides, we conducted a comparative analysis of the most popular placement's strategies. The proposed model, as well as the comparative study, reveal practical insights into the performance evaluation of resource allocation and capacity planning for distributed edge cloud with limited capacities.

In [58] we set design principles of future distributed edge clouds in order to meet application requirements. We precisely introduce a costless distributed resource allocation algorithm, named CLOSE, which considers local information only. We compare via simulations the performance of CLOSE against those obtained by using mechanisms proposed in the literature, notably the Tricircle project within OpenStack. It turns out that the proposed distributed algorithm yields better performance while requiring less overhead.

In the context of the Open Network Automation Platform (ONAP), we develop in [56] a resource allocation strategy for deploying Virtualized Network Functions (VNFs) on distributed data centers. For this purpose, we rely on a three-level data center hierarchy exploiting co-location facilities available within Main and Core Central Offices. We precisely propose an active VNFs' placement strategy, which dynamically offloads requests on the basis of the load observed within a data center. We compare via simulations the performance of the proposed solution against mechanisms so far proposed in the literature, notably the centralized approach of the multi-site project within OpenStack, currently adopted by ONAP. Our algorithm yields better performance in terms of both data center occupancy and overhead. Furthermore, it allows extending the applicability of ONAP in the context of distributed cloud, without requiring any modification.

Content Centric Networking. Content-Centric Networking (CCN) has been proposed to address the challenges raised by the Internet usage evolution over the last years. One key feature provided by CCN to improve the efficiency of content delivery is the in-network caching, which has major impact on the system performance. In order to improve caching effectiveness in such systems, the study of the functioning of CCN in-network storage must go deeper. In [39], we propose MACS, a Markov chain-based Approximation of CCN caching Systems. We start initially by modeling a single cache node. Then, we extend our model to the case of multiple nodes. A closed-form expression is then derived to define the cache hit probability of each content in the caching system. We compare the results of MACS to those obtained with simulations. The conducted experiments show clearly the accuracy of our model in estimating the cache hit performance of the system.

In [16], we present the design and implementation of a Content-Delivery-Network-as-a-Service (CDNaaS) architecture, which allows a telecom operator to open up its cloud infrastructure for content providers to deploy virtual CDN instances on demand, at regions where the operator has presence. Using northbound REST APIs, content providers can express performance requirements and demand specifications, which are translated into an appropriate service placement on the underlying cloud substrate. Our architecture is extensible, supporting various different CDN flavors, and, in turn, different schemes for cloud resource allocation and management. In order to decide on the latter in an optimal manner from an infrastructure cost and a service quality perspective, knowledge of the performance capabilities of the underlying technologies and computing resources is critical. Therefore, to gain insight which can be applied to the design of such mechanisms, but also with further implications on service pricing and SLA design, we carry out a measurement campaign to evaluate the capabilities of key enabling technologies for CDNaaS provision. In particular, we focus on virtualization and containerization technologies for implementing virtual CDN functions to deliver a generic HTTP service, as well as an HTTP video streaming one, empirically capturing the relationship between performance and service workload, both from a system operator and a user-centric viewpoints.

New tools for network design. In the efforts for designing future networks’ topologies, the inclusion of dependability aspects has been recently enriched with finer criteria, and one relatively new family of metrics consider diameter-constrained parameters that capture more accurately reliability aspects of communication infrastructures. This is done by taking into account not only connectivity properties but also delays when nodes are connected. Paper [15] deals with factorization theory in diameter-constrained reliability, when terminal nodes are further required to be connected by $d$ hops or fewer ($d$ is a given strictly positive parameter of the metric, called its diameter). This metric was defined in 2001, inspired by delay-sensitive applications in telecommunications. Factorization theory is fundamental for classical network reliability evaluation, and today it is a mature area. However, its extension to the diameter-constrained context requires at least the recognition of irrelevant links, which is an open problem. In this paper, irrelevant links are efficiently determined in the most used case, where we consider the communication between a given pair of nodes in the network. The article also proposes a Factoring algorithm that includes the way series-parallels substructures can be handled.

Quality of Experience activities. We continue to develop tools for Quality of Experience assessment, and applications of this quantitative evaluation.

Predicting time series. For the future of the PSQA project, we intend to integrate the capability of predicting the Perceptual Quality and not only evaluating its current value. With this goal in mind, we explored this year the idea of combining a Reservoir Computing architecture (whose good performances have been reported many times, when used to predict sequences of numbers or of vectors) with Recurrent Random Neural Networks, that belong to a class of Neural Networks that have some nice properties. Both have been very successful in many applications. In [29] we propose a new model belonging to the first class, taking the structure of the second for its dynamics. The new model is called Echo State Queuing Network. The paper positions the model in the global Machine Learning area, and provides examples of its use and performances. We show on largely used benchmarks that it is a very accurate tool, and we illustrate how it compares with standard Reservoir Computing models. In [31] we presented some preliminary results to the Random Neural Network community.

QoE and P2P design. In [30] we describe a Peer-to-Peer (P2P) network that was designed to support Video on Demand (VoD) services. The network is based on a video-file sharing mechanism that classifies peers according to the window (segment of the file) that they are downloading. This classification easily allows identifying peers that are able to share windows among them, so one of our major contributions is the definition of a mechanism that could be implemented to efficiently distribute video content in future 5G networks. Considering that cooperation among peers can be insufficient to guarantee an appropriate system performance, we also propose that this network must be assisted by upload bandwidth coming from servers; since these resources represent an extra cost to the service provider, especially in mobile networks, we complement our work by defining a scheme that efficiently allocates them only to those peers that are in windows with resources scarcity (we called it prioritized windows distribution scheme). On the basis of a fluid model and a Markov chain, we also develop a methodology that allows us to select the system parameters values (e.g., windows sizes or minimum servers upload bandwidth) that satisfy a set of Quality of Experience (QoE) parameters.