Section: New Results

Open Network Architecture

Constrained Software Defined Networks

Participant: Damien Saucez.  

The objective of the ANR JCJC DET4ALL project was to offer the ability to multiplex constrained networks with real time and safety requirements on Ethernet network not initially thought for strict constraints. The reason for this move to Ethernet is to reduce the cost of networking solutions in automotive and industrial applications. We advocate that this move requires to rely on Software Defined Networking (SDN) that enables a programmatic approach to networking, hence offering modularity and flexibility. The challenge with SDN is to be able to certify the behaviour of the system while keeping the solution generic. Within DET4ALL we put the first element in place to show that the previous works that proposed programming languages and abstractions for best-effort network could be leveraged to offering safety properties and determinism in real-time industrial and automotive networks. More precisely, we have demonstrated that Linear Temporal Logic (LTL) can be used in real-time networks to demonstrate the that real-time constraints are always respected. We built a strawman to show that the Temporal NetKat language was adapted to express real-time constraints of networks even though it was not initially design for that purpose. Given that Temporal NetKat relies on LTL and an algebra, it is a good candidate to prove the correct behaviour of a SDN network which logic would be implemented with such a language. In the continuation of this work, we have determined what would be necessary to be able to provide provable live network updates in real time network without service degradation. This work is published in [30] and will be detailed in the next subsection. Due the leave of Damien Saucez to Safran for one year starting October 1st 2019, the activity on this project had to be stopped as it was in the context of an ANR JCJC project.

NUTS: Network Updates in Real Time Systems

Participants: Damien Saucez, Walid Dabbous.  

Recent manufacturing trends have highlighted the need to adapt to volatile, fast-moving, and customer-driven markets. To keep pace with ever quicker product lifecycles, shorter order lead times and growing product variants, factories will become distributed modular cyber-physical systems interconnected by complex communication networks. We advocate that the Software Define Networking (SDN) concept with its programmatic approach to networking is a key enabler for the so-called Industry 4.0 because it provides flexibility and the possibility to formally reason on networks. We have identified that a critical point to address is how to support safe network updates of deterministic real-time communication SDN. To achieve this goal 4 elements are required. First a declarative language with LTL support is needed to express the constraints. Second, a programmable data-plane with the ability to provide real-time constraints indications must be provided in order to assess the behaviour of the forwarding elements. Such language does not exist yet however among the data-plane languages currently on the market some provide the ability to add annotations that could be used to reach our objective. Third, we have identified that deterministic algorithms had to be used to provide a verifiable sequence of network updates in order to make live updates without service degradations. Finally, mathematical techniques must be used to provide bounds on the network updates. Network Calculus can be used for that objective. This study was published as a poster in SOSR'19 [30].

A Joint range extension and localization for LPWAN

Participants: Mohamed Naoufal Mahfoudi, Gayatri Sivadoss, Othmane Bensouda Korachi, Thierry Turletti, Walid Dabbous.  

We have proposed Snipe, a novel system offering joint localization and range extensions for LPWANs. Although LPWAN systems such as Long Range (LoRa) are designed to achieve high communication range with low energy consumption, they suffer from fading in obstructed environments with dense multipath components, and their localization system is sub-par in terms of accuracy. In this work, MIMO techniques are leveraged to achieve a higher signal-to-noise ratio at both the end device and the gateway while providing an opportunistic accurate radar-based system for localization with limited additional cost. This work has been published at Internet Technology Letters [15].

Online Robust Placement of Service Chains for Large Data Center Topologies

Participants: Ghada Moualla, Thierry Turletti, Damien Saucez.  

The trend today is to deploy applications and more generally Service Function Chains (SFCs) in public clouds. However, before being deployed in the cloud, chains were deployed on dedicated infrastructures where software, hardware, and network components were managed by the same entity, making it straightforward to provide robustness guarantees. By moving their services to the cloud, the users lose their control on the infrastructure and hence on the robustness. We propose an online algorithm for robust placement of service chains in data centers. Our placement algorithm determines the required number of replicas for each function of the chain and their placement in the data center. Our simulations on large data-center topologies with up to 30,528 nodes show that our algorithm is fast enough such that one can consider robust chain placements in real time even in a very large data center and without the need of prior knowledge on the demand distribution. This work has been published at IEEE Access [16].

Bandwidth-optimal Failure Recovery Scheme for Robust Programmable Networks

Participants: Giuseppe Di Lena, Damien Saucez, Thierry Turletti.  

With the emergence of Network Function Virtualization (NFV) and Software Defined Networking (SDN), efficient network algorithms considered too hard to be put in practice in the past now have a second chance to be considered again. In this context, we rethink the network dimensioning problem with protection against Shared Risk Link Group (SLRG) failures. In this work, we consider a path-based protection scheme with a global rerouting strategy, in which, for each failure situation, there may be a new routing of all the demands. Our optimization task is to minimize the needed amount of bandwidth. After discussing the hardness of the problem, we develop a scalable mathematical model that we handle using the Column Generation technique. Through extensive simulations on real-world IP network topologies and on random generated instances, we show the effectiveness of our method. Finally, our implementation in OpenDaylight demonstrates the feasibility of the approach and its evaluation with Mininet shows that technical implementation choices may have a dramatic impact on the time needed to reestablish the flows after a failure takes place. This work has been presented at the IEEE International Conference on Cloud Networking (CloudNet), November 2019, at Coimbra in Portugal [29] and documented in a research report [36]. A poster version is published in IFIP-Networking in Warsaw [41].

Efficient Pull-based Mobile Video Streaming leveraging In-Network Functions

Participants: Indukala Naladala, Thierry Turletti.  

There has been a considerable increase in the demand for high quality mobile video streaming services, while at the same time, the video traffic volume is expected to grow exponentially. Consequently, maintaining high quality of experience (QoE) and saving network resources are becoming crucial challenges to solve. In this work, we propose a name-based mobile streaming scheme that allows efficient video content delivery by exploiting a smart pulling mechanism designed for information-centric networks (ICNs). The proposed mechanism enables fast packet loss recovery by leveraging in-network caching and coding. Through an experimental evaluation of our mechanism over an open wireless testbed and the Internet, we demonstrate that the proposed scheme leads to higher QoE levels than classical ICN and TCP-based streaming mechanisms. This work will be presented at the IEEE Consumer Communications & Networking Conference (CCNC), in January 2020 at Las Vegas, USA [27]. The following link https://github.com/fit-r2lab/demo-cefore includes the artefacts that allows to reproduce performance results shown in the paper.

Low Cost Video Streaming through Mobile Edge Caching: Modelling and Optimization

Participants: Luigi Vigneri, Chadi Barakat.

Caching content at the edge of mobile networks is considered as a promising way to deal with the data tsunami. In addition to caching at fixed base stations or user devices, it has been recently proposed that an architecture with public or private transportation acting as mobile relays and caches might be a promising middle ground. While such mobile caches have mostly been considered in the context of delay tolerant networks, in this work done in collaboration with Eurecom with the support of the UCN@Sophia Labex, we argue that they could be used for low cost video streaming without the need to impose any delay on the user. Users can prefetch video chunks into their playout buffer from encountered vehicle caches (at low cost) or stream from the cellular infrastructure (at higher cost) when their playout buffer empties while watching the content. Our main contributions are: (i) to model the playout buffer in the user device and analyze its idle periods which correspond to bytes downloaded from the infrastructure; (ii) to optimize the content allocation to mobile caches, to minimize the expected number of non-offloaded bytes. We perform trace-based simulations to support our findings showing that up to 60 percent of the original traffic could be offloaded from the main infrastructure. These contributions were published in the IEEE Transactions on Mobile Computing journal [18].

Quality of Experience-Aware Mobile Edge Caching through a Vehicular Cloud

Participants: Luigi Vigneri, Chadi Barakat.  

Densification through small cells and caching in base stations have been proposed to deal with the increasing demand for Internet content and the related overload on the cellular infrastructure. However, these solutions are expensive to install and maintain. Instead, using vehicles acting as mobile caches might represent an interesting alternative. In this work, we assume that users can query nearby vehicles for some time, and be redirected to the cellular infrastructure when the deadline expires. Beyond reducing costs, in such an architecture, through vehicle mobility, a user sees a much larger variety of locally accessible content within only few minutes. Unlike most of the related works on delay tolerant access, we consider the impact on the user experience by assigning different retrieval deadlines per content. We provide the following contributions: (i) we model analytically such a scenario; (ii) we formulate an optimization problem to maximize the traffic offloaded while ensuring user experience guarantees; (iii) we propose two variable deadline policies; (iv) we perform realistic trace-based simulations, and we show that, even with low technology penetration rate, more than 60% of the total traffic can be offloaded which is around 20% larger compared to existing allocation policies. These results were published in the IEEE Transactions on Mobile Computing journal [19].

Machine Learning for Next-Generation Intelligent Transportation Systems

Participants: Tingting Yuan, Thierry Turletti, Chadi Barakat.

Intelligent Transportation Systems, or ITS for short, includes a variety of services and applications such as road traffic management, traveler information systems, public transit system management , and autonomous vehicles, to name a few. It is expected that ITS will be an integral part of urban planning and future cities as it will contribute to improved road and traffic safety, transportation and transit efficiency, as well as to increased energy efficiency and reduced environmental pollution. On the other hand, ITS poses a variety of challenges due to its scalability and diverse quality-of-service needs, as well as the massive amounts of data it will generate. In this survey, we explore the use of Machine Learning (ML), which has recently gained significant traction, to enable ITS. In the context of the Drive associated team, we did a comprehensive survey of the current state-of-the-art of how ML technology has been applied to a broad range of ITS applications and services, such as cooperative driving and road hazard warning, and identify future directions for how ITS can use and benefit from ML technology. The survey is documented in [42].