Traffic optimization and smart buffering are fundamental to achieve both great application performance and resource efficiency in data centers with heterogeneous workloads, including incast and elephant traffics. However, general performance models providing insights on how various factors affect traffic performance metrics needed by these management functions are missing. For the special case of incast, the existing models are analytical ones, either tightly coupled with a particular protocol version or specific to certain empirical data. Motivated by this observation, this paper proposes an SDN-enabled machine-learning-based performance modeling approach in data center networks that leverages random forest predictions. Evaluations based on datasets constructed through intensive NS-3 simulations show that we can achieve accurate predictions of incast and elephant performance metrics based on various features. With this performance modeling capability, smart buffering schemes or traffic optimization algorithms could anticipate and efficiently optimize system parameters adjustment to achieve optimal performance continuously in data centers.

This extended abstract discusses our first attempt in revealing the deployment and usage of Segment Routing with MPLS as forwarding plane (Sr-Mpls), in a large European ISP. To do so, we study a longitudinal traceroute like dataset. Early results show that Sr-Mpls is mainly used in interworking with classic MPLS tunnels.

Zero-Touch is a broad concept, but it is understood as a means to limit human exposure to the data-plane states, and therefore reduces the potential for error. By combining the various layers from the administration level down to the switching fabric, introducing a new top to bottom architecture. This paper presents our operational research project HolistIX, a full-stack management solution enabling zero-touch networking within IXP infrastructure networks. HolistIX achieves this by first bringing diagramming to the forefront of topology planning and automated network configuration generation. This is then followed by verifying the proposed network configuration through emulation before deployment to the production network. By utilizing Umbrella SDN switching fabric, HolistIX enables full-stack management, with minimal human interaction required. We also briefly present the results of deploying our HolistIX approach in two IXPs, one in France (TouIX) and the other in Japan (DIX-IE).

Traffic optimization is fundamental to achieve both great application performance and resource efficiency in data centers with heterogeneous workloads, including incast. However, general performance models, providing insights on how various factors affect a certain performance metric used in the network optimization process, are missing. For the special case of incast, the existing models are analytical models, either tightly coupled with a particular protocol version or specific to certain empirical data. This paper proposes an SDN-enabled machine-learning-based optimization framework for incast performance optimization in data center networks that leverages learning-based performance modeling. Evaluations based on intensive NS-3 simulations show that we can achieve accurate performance predictions that enable finding the efficient switch buffer space to achieve optimal incast completion time in different configurations. We expect this framework to be a building block for autonomous data center network management.

In Software-Defined Networking, near-real-time collection of flow-level statistics provided by OpenFlow (e.g. byte count) is needed for control and management applications like traffic engineering, heavy hitters detection, attack detection, etc. The practical way to do this near-real-time collection is a periodic collection at high frequency. However, periodic polling may generate a lot of overheads expressed by the number of OpenFlow request and reply messages on the control network. To handle these overheads, adaptive techniques based on the pull model were proposed. But we can do better by detaching from the classical OpenFlow request-reply model for the particular case of periodic statistics collection. In light of this, we propose a push and prediction based adaptive collection to handle efficiently periodic OpenFlow statistics collection while maintaining good accuracy. We utilize the Ryu Controller and Mininet to implement our solution and then we carry out intensive experiments using real-world traces. The results show that our proposed approach can reduce the number of pushed messages up to 75% compared to a fixed periodic collection with a very good accuracy represented by a collection error of less than 0.5%.

Software-defined Internet eXchange points (SDXs) are a promising solution to the long-standing limitations and problems of interdomain routing. While the proposed SDX architectures have improved the scalability of the control plane, these solutions have ignored the underlying fabric upon which they should be deployed. In this paper, we present Umbrella, a software-defined interconnection fabric that complements and enhances those architectures. Umbrella is a switching fabric architecture and management approach that improves the overall robustness, limiting control plane dependence, and suitable for the topology of any existing Internet eXchange Point (IXP). We validate Umbrella through a real-world deployment on two production IXPs, TouSIX and NSPIXP-3, and demonstrate its use in practice, sharing our experience of the challenges faced.

Innovation in interdomain routing has remained stagnant for over a decade. Recently, Internet eXchange Points (IXPs) have emerged as economically-advantageous interconnection points for reducing path latencies and exchanging ever increasing traffic volumes among, possibly, hundreds of networks. Given their far-reaching implications on interdomain routing, IXPs are the ideal place to foster network innovation and extend the benefits of software defined networking (SDN) to the interdomain level. In this paper, we present, evaluate, and demonstrate ENDEAVOUR, an SDN platform for IXPs. ENDEAVOUR can be deployed on a multi-hop IXP fabric, supports a large number of use cases, and is highly scalable, while avoiding broadcast storms. Our evaluation with real data from one of the largest IXPs, demonstrates the benefits and scalability of our solution: ENDEAVOUR requires around 70% fewer rules than alternative SDN solutions thanks to our rule partitioning mechanism. In addition, by providing an open source solution, we invite everyone from the community to experiment (and improve) our implementation as well as adapt it to new use cases.

The Internet eXchange Points (IXP) are essential for the Internet evolution as they empower high bandwidth low latency and inexpensive local traffic peering as opposed to transit traffic. On the other side, Software Defined Networking, or SDN for short (and its pre-dominant realization, the OpenFlow protocol) enables more dynamic network programmability to control network behaviour via open interfaces, as opposed to the legacy closed-box solutions and proprietary-defined interfaces. This paper then reports the work that has been done to migrate the TouIX IXP located in Toulouse (France) from a traditional to a full OpenFlow IXP. It especially describes how switches have been selected, configured and installed, and presents the management tools that have been developed, together with performance and service measurements and evaluations. To the best of our knowledge, from may 2015, TouSIX (the SDN evolution of TouIX) is the first Internet Exchange Provider in Europe to fully leverage OpenFlow for its day-to-day operations.

The heterogeneity barrier breakthrough achieved by the OpenFlow protocol is currently paced by the variability in performance semantics among network devices, which reduces the ability of applications to take complete advantage of programmable control. As a result, control applications remain conservative on performance requirements in order to be generalizable and trade performance for explicit state consistency in order to support varying performance behaviours. In this paper we argue that network control must be optimized towards network device capabilities and network managers and application developers must perform informed design decision using accurate switch performance profiles. This becomes highly critical for modern OpenFlow-enabled 10 GbE optical switches which significantly elevate switch performance requirements. We present OFLOPS-Turbo, the integration of the OFLOPS switch evaluation platform, with the OSNT platform, a hardware-accelerated traffic generation and capture system supporting lossless 10 GbE functionality. Using OFLOPS-Turbo, we conduct an evaluation of flow table manipulation capabilities in a representative collection of 10 GbE production OpenFlow switch devices and interpret the evolution of OpenFlow support by comparison with historical data.

To make networks more reliable, enormous resources are poured into all phases of the network-equipment lifecycle. The process starts early in the design phase when simulation is used to verify the correctness of a design, and continues through manufacturing and perhaps months of rigorously trials. With over 7,000 Internet RFCs and hundreds of IEEE standards, a typical piece of networking equipment undergoes hundreds of conformance tests before being deployed. Finally, when deployed in a production network, the equipment is tested regularly. Throughout the process, a relentless battery of tests and measurement help ensure the correct operation of the equipment.