An apparatus comprising a service network and a plurality of Layer 2 networks at a plurality of different physical locations coupled to the service network via a plurality of edge nodes at the Layer 2 networks, wherein the edge nodes are configured to maintain a plurality of Internet Protocol (IP) addresses of a plurality of hosts across the Layer 2 networks, and wherein the IP addresses of the hosts in each of the Layer 2 networks are mapped by the other Layer 2 networks to a Media Access Control (MAC) address of each of the edge nodes in the same Layer 2 networks of the hosts.

Virtual Layer 2 and Mechanism to Make it Scalable

A network component comprising a receiver configured to receive an outgoing frame from a local host, a logic circuit configured to map a destination address (DA) for a target host in the outgoing frame to a DA for a target location of the target host and encapsulate the outgoing frame using the DA for the target location, and a transmitter configured to receive a pre-encapsulated outgoing frame from a local switch, and send the pre-encapsulated outgoing frame to a gateway at a target location, wherein the transmitter does not encapsulate frames received from local switches and decapsulates an incoming frame from a remote gateway destined towards local hosts.

Asymmetric network address encapsulation

Software-defined networking (SDN) is an emerging network architecture that promises to simplify network management, improve network resource utilization, and boost evolution and innovation in traditional networks. The SDN allows the abstraction and centralized management of the lower-level network functionalities by decoupling the network logic from the data forwarding devices into the logically centralized distributed controllers. However, this separation introduces new scalability and performance challenges in large-scale networks of dynamic traffic and topology conditions. Many research studies have represented that centralization and maintaining the global network visibility over the distributed SDN controller introduce scalability concern. This paper surveys the state-of-the-art proposed techniques toward minimizing the control to data planes communication overhead and controllers' consistency traffic to enhance the OpenFlow-SDN scalability in the context of logically centralized distributed SDN control plane architecture. The survey mainly focuses on four issues, including logically centralized visibility, link-state discovery, flow rules placement, and controllers' load balancing. In addition, this paper discusses each issue and presents an updated and detailed study of existing solutions and limitations in enhancing the OpenFlow-SDN scalability and performance. Moreover, it outlines the potential challenges that need to be addressed further in obtaining adaptive and scalable OpenFlow-SDN flow control.

Toward adaptive and scalable openflow-SDN flow control: A survey

Flows of packets are dynamically mapped to resource queues. Flows of packets are received at a network device to be routed from the network device in a network. Each flow comprises packets to be sent from a source to a connection. Data is stored for a queue allocation table that maintains a plurality of buckets to which received packets for a flow are assigned and indicating which of a plurality of resource queues are allocated for respective buckets. For each packet in a flow, a hash function is computed from values in a header of the packet and the packet is assigned to one of the plurality of buckets based on the computed hash function. One of a plurality of resource queues is allocated for each bucket to which packets are assigned based on the computed hash function.

Dynamic load balancing without packet reordering

An apparatus comprising a service network, and a plurality of Layer 2 sites connected by the service network via a plurality of gateways, wherein the gateways are configured to map a plurality of Internet Protocol (IP) addresses of a plurality of hosts under a plurality of virtual local area networks (VLANs) in a plurality of Layer 2 sites to a plurality of addresses (e.g. MAC or others) of the corresponding gateways inform the other gateways in the other Layer 2 sites of the mapped IP addresses under each of the VLANs in the local Layer 2 sites, and forward data frames originated from the hosts in the local Layer 2 sites to the other gateways in the other Layer 2 sites when the data frames' destinations are residing in the other Layer 2 sites.

Layer Two Over Multiple Sites

Hybrid SDN evolution: A comprehensive survey of the state-of-the-art

In conventional networks, there was a tight bond between the control plane and the data plane. The introduction of Software-Defined Networking (SDN) separated these planes, and provided additional features and tools to solve some of the problems of traditional network (i.e., latency, consistency, efficiency). SDN is a flexible networking paradigm that boosts network control, programmability and automation. It proffers many benefits in many areas, including routing. More specifically, for efficiently organizing, managing and optimizing routing in networks, some intelligence is required, and SDN offers the possibility to easily integrate it. To this purpose, many researchers implemented different machine learning (ML) techniques to enhance SDN routing applications. This article surveys the use of ML techniques for routing optimization in SDN based on three core categories (i.e. supervised learning, unsupervised learning, and reinforcement learning). The main contributions of this survey are threefold. Firstly, it presents detailed summary tables related to these studies and their comparison is also discussed, including a summary of the best works according to our analysis. Secondly, it summarizes the main findings, best works and missing aspects, and it includes a quick guideline to choose the best ML technique in this field (based on available resources and objectives). Finally, it provides specific future research directions divided into six sections to conclude the survey. Our conclusion is that there is a huge trend to use intelligence-based routing in programmable networks, particularly during the last three years, but a lot of effort is still required to achieve comprehensive comparisons and synergies of approaches, meaningful evaluations based on open datasets and topologies, and detailed practical implementations (following recent standards) that could be adopted by industry. In summary, future efforts should be focused on reproducible research rather than on new isolated ideas. Otherwise, most of these applications will be barely implemented in practice.

/pdf/a-survey-on-machine-learning-techniques-for-routing-1cfrotoops.pdf

A Survey on Machine Learning Techniques for Routing Optimization in SDN

Currently, software-defined networking (SDN) platforms leverage the link-layer discovery protocol (LLDP) to discover the underlying topology. However, the LLDP is suboptimal in terms of message load. In this letter, we present the tree exploration discovery protocol (TEDP), proving that shortest paths can be built at the same time that the topology information is gathered, without extra messages compared with LLDP. We also analyze two alternative implementations for the TEDP and give insights into some features that SDN platforms should ideally provide for an efficient topology discovery service.

TEDP: An Enhanced Topology Discovery Service for Software-Defined Networking

All-Path bridging

In this paper we propose Fast Path Ethernet, an evolution of the transparent bridges learning mechanisms to increase infrastructure utilization for campus and datacenter networks in a simple way. Fast Path Ethernet Switches reuse standard ARP Request and Reply packets to set up fast on-demand paths between hosts. This architecture uses the standard Ethernet frame format, so it is fully transparent to hosts and compatible with 802.1D bridging in core-island mode. A proof of concept has been implemented in Linux. Preliminary simulations in metropolitan and campus network topologies show superior performance to spanning tree and even to shortest path forwarding, at a fraction of the their complexity.

/pdf/fast-path-ethernet-switching-on-demand-efficient-transparent-19uexcddqj.pdf

Jose M. Arco

Papers

Hybrid SDN evolution: A comprehensive survey of the state-of-the-art

A Survey on Machine Learning Techniques for Routing Optimization in SDN

TEDP: An Enhanced Topology Discovery Service for Software-Defined Networking

All-Path bridging

Fast Path Ethernet Switching: On-demand, efficient transparent bridges for data center and campus networks