The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms—with a focus on aspects such as resiliency, scalability, performance, security, and dependability—as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

https://publications.uni.lu/bitstream/10993/20596/1/survey_on_SDN.pdf

Software-Defined Networking: A Comprehensive Survey

Although there is tremendous interest in designing improved networks for data centers, very little is known about the network-level traffic characteristics of data centers today. In this paper, we conduct an empirical study of the network traffic in 10 data centers belonging to three different categories, including university, enterprise campus, and cloud data centers. Our definition of cloud data centers includes not only data centers employed by large online service providers offering Internet-facing applications but also data centers used to host data-intensive (MapReduce style) applications). We collect and analyze SNMP statistics, topology and packet-level traces. We examine the range of applications deployed in these data centers and their placement, the flow-level and packet-level transmission properties of these applications, and their impact on network and link utilizations, congestion and packet drops. We describe the implications of the observed traffic patterns for data center internal traffic engineering as well as for recently proposed architectures for data center networks.

/pdf/network-traffic-characteristics-of-data-centers-in-the-wild-43qhxtldqh.pdf

Network traffic characteristics of data centers in the wild

The idea of programmable networks has recently re-gained considerable momentum due to the emergence of the Software-Defined Networking (SDN) paradigm. SDN, often referred to as a ''radical new idea in networking'', promises to dramatically simplify network management and enable innovation through network programmability. This paper surveys the state-of-the-art in programmable networks with an emphasis on SDN. We provide a historic perspective of programmable networks from early ideas to recent developments. Then we present the SDN architecture and the OpenFlow standard in particular, discuss current alternatives for implementation and testing of SDN-based protocols and services, examine current and future SDN applications, and explore promising research directions based on the SDN paradigm.

/pdf/a-survey-of-software-defined-networking-past-present-and-6tt309ed7r.pdf

A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks

Software-Defined Networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound APIs, network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms -- with a focus on aspects such as resiliency, scalability, performance, security and dependability -- as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

Mininet is a system for rapidly prototyping large networks on the constrained resources of a single laptop The lightweight approach of using OS-level virtualization features, including processes and network namespaces, allows it to scale to hundreds of nodes Experiences with our initial implementation suggest that the ability to run, poke, and debug in real time represents a qualitative change in workflow We share supporting case studies culled from over 100 users, at 18 institutions, who have developed Software-Defined Networks (SDN) Ultimately, we think the greatest value of Mininet will be supporting collaborative network research, by enabling self-contained SDN prototypes which anyone with a PC can download, run, evaluate, explore, tweak, and build upon

/pdf/a-network-in-a-laptop-rapid-prototyping-for-software-defined-27edokofdp.pdf

A network in a laptop: rapid prototyping for software-defined networks

Networks are a shared resource connecting critical IT infrastructure, and the general practice is to always leave them on. Yet, meaningful energy savings can result from improving a network's ability to scale up and down, as traffic demands ebb and flow. We present ElasticTree, a network-wide power1 manager, which dynamically adjusts the set of active network elements -- links and switches--to satisfy changing data center traffic loads.We first compare multiple strategies for finding minimum-power network subsets across a range of traffic patterns. We implement and analyze ElasticTree on a prototype testbed built with production OpenFlow switches from three network vendors. Further, we examine the trade-offs between energy efficiency, performance and robustness, with real traces from a production e-commerce website. Our results demonstrate that for data center workloads, ElasticTree can save up to 50% of network energy, while maintaining the ability to handle traffic surges. Our fast heuristic for computing network subsets enables ElasticTree to scale to data centers containing thousands of nodes. We finish by showing how a network admin might configure ElasticTree to satisfy their needs for performance and fault tolerance, while minimizing their network power bill.

/pdf/elastictree-saving-energy-in-data-center-networks-2w53iv2yv5.pdf

ElasticTree: saving energy in data center networks

1 SLICED PROGRAMMABLE NETWORKS OpenFlow [4] has been demonstrated as a way for researchers to run networking experiments in their production network Last year, we demonstrated how an OpenFlow controller running on NOX [3] could move VMs seamlessly around an OpenFlow network [1] While OpenFlow has potential [2] to open control of the network, only one researcher can innovate on the network at a time What is required is a way to divide, or slice, network resources so that researchers and network administrators can use them in parallel Network slicing implies that actions in one slice do not negatively affect other slices, even if they share the same underlying physical hardware A common network slicing technique is VLANs With VLANs, the administrator partitions the network by switch port and all traffic is mapped to a VLAN by input port or explicit tag This coarse-grained type of network slicing complicates more interesting experiments such as IP mobility or wireless handover Here, we demonstrate FlowVisor, a special purpose OpenFlow controller that allows multiple researchers to run experiments safely and independently on the same production OpenFlow network To motivate FlowVisor’s flexibility, we demonstrate four network slices running in parallel: one slice for the production network and three slices running experimental code (Figure 1) Our demonstration runs on real network hardware deployed on our production network at Stanford and a wide-area test-bed with a mix of wired and wireless technologies

/pdf/carving-research-slices-out-of-your-production-networks-with-5aa0xzozdu.pdf

Carving research slices out of your production networks with OpenFlow

Debugging operational networks can be a daunting task, due to their size, distributed state, and the presence of black box components such as commercial routers and switches, which are poorly instrumentable and only coarsely configurable The debugging tool set available to administrators is limited, and provides only aggregated statistics (SNMP), sampled data (NetFlow/sFlow), or local measurements on single hosts (tcpdump) In this paper, we leverage split forwarding architectures such as OpenFlow to add record and replay debugging capabilities to networks - a powerful, yet currently lacking approach We present the design of OFRewind, which enables scalable, multi-granularity, temporally consistent recording and coordinated replay in a network, with fine-grained, dynamic, centrally orchestrated control over both record and replay Thus, OFRewind helps operators to reproduce software errors, identify datapath limitations, or locate configuration errors

/pdf/ofrewind-enabling-record-and-replay-troubleshooting-for-17p28m9ir1.pdf

OFRewind: enabling record and replay troubleshooting for networks

http://yuba.stanford.edu/~nickm/papers/openflow-deployments.pdf

Maturing of OpenFlow and Software-defined Networking through deployments

We have built and deployed OpenRoads [11], a testbed that allows multiple network experiments to be conducted concurrently in a production network. For example, multiple routing protocols, mobility managers and network access controllers can run simultaneously in the same network. In this paper, we describe and discuss our deployment of the testbed at Stanford University. We focus on the challenges we faced deploying in a production network, and the tools we built to overcome these challenges. Our goal is to gain enough experience for other groups to deploy OpenRoads in their campus network.

/pdf/the-stanford-openroads-deployment-37gzxjaep6.pdf

Srini Seetharaman

Papers

ElasticTree: saving energy in data center networks

Carving research slices out of your production networks with OpenFlow

OFRewind: enabling record and replay troubleshooting for networks

Maturing of OpenFlow and Software-defined Networking through deployments

The Stanford OpenRoads deployment