http://www.csun.edu/~andrzej/COMP529-S05/papers/Mesh%20Networks%20Survey.pdf

Wireless mesh networks: a survey

Social Network Analysis

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

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.

OpenFlow assumes a logically centralized controller, which ideally can be physically distributed. However, current deployments rely on a single controller which has major drawbacks including lack of scalability. We present HyperFlow, a distributed event-based control plane for OpenFlow. HyperFlow is logically centralized but physically distributed: it provides scalability while keeping the benefits of network control centralization. By passively synchronizing network-wide views of OpenFlow controllers, HyperFlow localizes decision making to individual controllers, thus minimizing the control plane response time to data plane requests. HyperFlow is resilient to network partitioning and component failures. It also enables interconnecting independently managed OpenFlow networks, an essential feature missing in current OpenFlow deployments. We have implemented HyperFlow as an application for NOX. Our implementation requires minimal changes to NOX, and allows reuse of existing NOX applications with minor modifications. Our preliminary evaluation shows that, assuming sufficient control bandwidth, to bound the window of inconsistency among controllers by a factor of the delay between the farthest controllers, the network changes must occur at a rate lower than 1000 events per second across the network.

/pdf/hyperflow-a-distributed-control-plane-for-openflow-1o85a539jd.pdf

HyperFlow: a distributed control plane for OpenFlow

Limiting the overhead of frequent events on the control plane is essential for realizing a scalable Software-Defined Network. One way of limiting this overhead is to process frequent events in the data plane. This requires modifying switches and comes at the cost of visibility in the control plane. Taking an alternative route, we propose Kandoo, a framework for preserving scalability without changing switches. Kandoo has two layers of controllers: (i) the bottom layer is a group of controllers with no interconnection, and no knowledge of the network-wide state, and (ii) the top layer is a logically centralized controller that maintains the network-wide state. Controllers at the bottom layer run only local control applications (i.e., applications that can function using the state of a single switch) near datapaths. These controllers handle most of the frequent events and effectively shield the top layer. Kandoo's design enables network operators to replicate local controllers on demand and relieve the load on the top layer, which is the only potential bottleneck in terms of scalability. Our evaluations show that a network controlled by Kandoo has an order of magnitude lower control channel consumption compared to normal OpenFlow networks.

/pdf/kandoo-a-framework-for-efficient-and-scalable-offloading-of-2ebg6asygw.pdf

Kandoo: a framework for efficient and scalable offloading of control applications

Network architectures in which the control plane is decoupled from the data plane have been growing in popularity. Among the main arguments for this approach is that it provides a more structured software environment for developing network-wide abstractions while potentially simplifying the data plane. As has been adopted elsewhere [11], we refer to this split architecture as Software-Defined Networking (SDN). While it has been argued that SDN is suitable for some deployment environments (such as homes [17, 13], data centers [1], and the enterprise [5]), delegating control to a remote system has raised a number of questions on control-plane scaling implications of such an approach. Two of the most often voiced concerns are: (a) how fast can the controller respond to data path requests?; and (b) how many data path requests can it handle per second? There are some references to the performance of SDN systems in the literature [16, 5, 3]. For example, an oft-cited study shows that a popular network controller (NOX) handles around 30k flow initiation events1 per second while maintaining a sub-10ms flow install time [14]. Unfortunately, recent measurements of some deployment environments suggests that these numbers are far from sufficient. For example, Kandula et al. [9] found that a 1500-server cluster has a median flow arrival rate of 100k flows per second. Also, Benson et al. [2] show that a network with 100 switches can have spikes of 10M flows arrivals per second in the worst case. In addition, the 10ms flow setup delay of an SDN controller would add a 10% delay to the majority of flows (short-lived) in such a network. This disconnect between relatively poor controller performance and high network demands has motivated a

/pdf/on-controller-performance-in-software-defined-networks-u2erpxgvyv.pdf

On controller performance in software-defined networks

In this article, we deconstruct scalability concerns in software-defined networking and argue that they are not unique to SDN. We explore the often voiced concerns in different settings, discuss scalability trade-offs in the SDN design space, and present some recent research on SDN scalability. Moreover, we enumerate overlooked yet important opportunities and challenges in scalability beyond the commonly used performance metrics.

On scalability of software-defined networking

As the Internet evolves into a ubiquitous communication infrastructure and supports increasingly important services, its dependability in the presence of various failures becomes critical. In this paper, we analyze IS-IS routing updates from the Sprint IP backbone network to characterize failures that affect IP connectivity. Failures are first classified based on patterns observed at the IP-layer; in some cases, it is possible to further infer their probable causes, such as maintenance activities, router-related and optical layer problems. Key temporal and spatial characteristics of each class are analyzed and, when appropriate, parameterized using well-known distributions. Our results indicate that 20% of all failures happen during a period of scheduled maintenance activities. Of the unplanned failures, almost 30% are shared by multiple links and are most likely due to router-related and optical equipment-related problems, respectively, while 70% affect a single link at a time. Our classification of failures reveals the nature and extent of failures in the Sprint IP backbone. Furthermore, our characterization of the different classes provides a probabilistic failure model, which can be used to generate realistic failure scenarios, as input to various network design and traffic engineering problems.

Yashar Ganjali

Papers

HyperFlow: a distributed control plane for OpenFlow

Kandoo: a framework for efficient and scalable offloading of control applications

On controller performance in software-defined networks

On scalability of software-defined networking

Characterization of failures in an operational IP backbone network