SDN and NFV - SKKUmonet.skku.edu/wp-content/uploads/2018/08/W15.SDN_NFV.pdf · 2018-12-12 ·...

Networking Laboratory 1/78

Sungkyunkwan University

Copyright 2000-2018 Networking Laboratory

SDN and NFV

Prepared by P. Thorat, R. Challa, S. M. Raza and H. Choo


Presentation Outline

Software Defined Networking (SDN)

Case Studies of SDN

Role of SDN in Different Networks

Knowledge Defined Networking

Network Function Virtualization (NFV)

NFV Architecture Framework

RouteFlow: NFV Case Study

SDN and NFV Role and Challenges

SDN Current Activities and Standardization


SDN Introduction Video

Content

► Definition of a Software Defined Network (SDN)

► The control and data components in a SDN and how they work with one

another

► Connections establishment in a scalable enterprise architecture

► The features and benefits of SDN

Duration

► 5 minutes and 21 seconds

VIDEO

Software_Defined_Network.mp4


SDN Concept-Past, Present and Future

(1/2)

Past► Combined control and data plan

► Limited performance and capability

► Restricted pace of innovation

Present► Decoupled control and data plans

► High throughput

► More Scalable

► Pace of innovation limited to pace of network

element innovation

► Restricted by hardware and silicon innovation

Future► Separate network and control element

► Powerful computers accelerate control plan

innovation

► Very simple data plan


SDN Concept-Past, Present and Future

(2/2)

SDN concept

► Separates the data plane from the control plane

► Network Operating System in the controller layer controls the data plane

► Different applications run on top of the Network Operating System


SDN Architecture


Case Study: G-Scale (1/3)

General Description

Google’s global user based services (Google Web Search,

Google+, Gmail, YouTube, Google Maps, etc.) require significant

amount of data to be moved from one region to another, making

these applications and services very WAN-intensive

Google maintains two separate networks because they have

different requirements

Google’s WAN is organized as two backbones

► Internet facing (I-scale) network that carries user traffic

► Internal (G-scale) network that carries traffic between datacenters



Operational Mode

Thousands of individual applications runing across B4 target

network, Google categorizes them into three basic classes

► User data copies (e.g., email, documents, audio/video files) to remote

datacenters for availability/durability

► Remote storage access for computation over inherently distributed data

sources

► Large-scale data push synchronizing state across multiple datacenters



Deployed Equipment

Built from the merchant silicon

► 100s of port with non blockling 10 GE

support

► Custom hardware running Linux

Openflow support

Open source routing stacks

► Quagga BGP stack

► ISIS/BGP for internal connectivity

Features

► Fault tolerant

► Scale to multiple Tbps

► Support programming for traffic

engineering


SDN Controllers (1/4)

OpenDayLight (ODL)

OpenDaylight is an open platform by Linux foundation for

network programmability to enable SDN and create a solid

foundation for NFV for networks at any size and scale

OpenDaylight software is a combination of components including

a fully pluggable controller, interfaces, protocol plugins and

applications

With this common platform both customers and vendors can

innovate and collaborate in order to commercialize SDN- and

NFV-based solutions



ONOS

The Open Network Operating System (ONOS) is the first open

source SDN network operating system targeted specifically at the

Service Provider and mission critical networks

The major goals of ONOS are:

► Carrier grade features (scalability, availability, and performance) in the

control plane

► Web style agility

► Migration of existing networks to white boxes

► Innovation in both network hardware and software, independent of their own

time scales



RYU

Ryu means "flow" in Japanese

Ryu is a component-based software defined networking

framework

Ryu supports various protocols for managing network devices,

such as OpenFlow, Netconf, OF-config, etc.

Ryu fully supports OpenFlow 1.0, 1.2, 1.3, 1.4, 1.5 and Nicira

Extensions

The Ryu Controller is supported by NTT and is deployed in NTT

cloud data centers as well

Ryu uses Python for development



Floodlight

The Floodlight is an open enterprise-class, Java-based

OpenFlow Controller

Floodlight was introduced by Big Switch Networks, and is still

backed by the engineers in Big Switch networks

Floodlight offers a module loading system that make it simple to

extend and enhance

Floodlight can handle mixed OpenFlow and non-OpenFlow

networks – it can manage multiple “islands” of OpenFlow

hardware switches

Floodlight is designed to be high-performance – is multithreaded

from the ground up


OpenFlow (2/4)

Flow Table and Flow Entries (1/3)

An entry in the Flow-Table has three fields:► The rule

Defines the flow, and rule consists of mainly field in the packet header

► The action

Defines how the packets should be processed

► Statistics

Count the number of packets and bytes for each flow, and the time since

the last packet matched the flow


OpenFlow (1/4)

OpenFlow is an open API that provides a standard interface for

programming the data plane switches

Provides open interface to “black box” networking node (ie.

Routers, L2/L3 switch) to enable visibility and openness in

network


OpenFlow (3/4)


Flow table pipeline in OpenFlow switch, where packets are matched against multiple tables in the pipeline

Per-table packet processing


OpenFlow (4/4)



How OpenFlow works

Upon arrival on the OpenFlow switch, the packet will be match by

a Flow Entry in the Flow Table, so the action will be executed if

the header field is matched, and the counter is updated

If the packet doesn’t match any flow entry, it will be sent to the

controller over the secure channel


Introduction to Mininet (1/3)

A virtual network environment that can run on a single PC

► Mininet uses lightweight virtualization to make a single system look like a

complete network

► Runs real kernel, switch, and application code on a single machine

► Internally uses Linux containers to emulate hosts, switches, and links

Command-line, UI, Python interfaces

Many OpenFlow features are built-in

► Useful: developing, deploying, and sharing


Introduction of Mininet (2/3)

Platform Advantages Disadvantages

Hardware Testbed

realityaccurate: "ground truth"

expensiveshared resource?hard to reconfigurehard to change

Simulator inexpensive, flexibledetailed (or abstract!)virtual time (can be "faster" than reality)

may require app changesmight not run OS codemay not be "believable"may be slow/non-interactive

Emulator inexpensive, flexiblereal codereasonably accuratefast/interactive usage

slower than hardwareexperiments may not fitpossible inaccuracy


Introduction of Mininet (3/3)

Why Use Mininet

► Fast

► Possible to create custom topologies

► Can run real programs (anything that can run on Linux can run on a Mininet

host)

► Programmable OpenFlow switches

► Easy to use

► Open source

Alternatives to Mininet and their limitations

► Real system: Pain to configure

► Networked VMs: Scalability

► Simulator: No path to hardware deployment


SDN Testbeds (1/3)

GENI

GENI is an open infrastructure for at-scale networking and

distributed systems research and education that spans the US


SDN Testbeds (2/3)

OFELIA

OFELIA – Pan-European OpenFlow Testbed

• Berlin, Germany (TUB)

• Ghent, Belgium (IBBT)

• Zurich, Switzerland (ETH)

• Barcelona, Spain (i2CAT)

• Bristol, UK (UNIVBRIS)

• Catania, Italy (CNIT)

• Rome, Italy (CNIT)

• Trento, Italy (CREATE-NET)

• Pisa, Italy (CNIT, 2 locations)

• Uberlândia, Brazil (UFU)

• Castelldefels, Spain (CTTC)


SDN Testbeds (3/3)

KOREN

A multi purpose Korean

backbone network,

connecting 8 metropolitan

areas (Seoul, Suwon,

Daejeon, Gwangju,

Daegu, Busan, Gangwon,

Jeju) from 10Gbps to

160Gbps


Network Virtualization with SDN (1/3)

Network Virtualization

► The goal of network virtualization is to improve speed, automation and

network management by adding new software elements

► It divides a network into multiple segments or create software-only networks

between virtual machines (VMs)

► It's clear that network virtualization and SDN technologies share common

elements

► Whether network virtualization is a subset of SDN or SDN is a subset of

network virtualization, the two have overlapping sets of technologies with

similar goals

Virtualization Platform

Physical Network

Virtual Networks



OpenVirteX (OVX) (1/2)

Network virtualization allows multiple tenants to occupy the same

network infrastructure

Each tenant has an illusion that they have a full network to their

own disposal

OVX achieves this by giving each tenant access to a virtualized

network topology

OVX implements network virtualization as a proxy that sits

between the network and the tenant’s Controller

OVX rewrites OpenFlow messages to translate between what a

tenant’s Controller sends and receives from its virtual network



OpenVirteX (OVX) (2/2)

Mapping between

them and the

physical and

virtual network

Virtual topologies

Physical switches


SoftRAN [Gudipati et al.’13] – Improved

RAN (Radio Access Network) Design.

► Improved radio resource management while

maintaining connectivity for users

► Abstracts all radio resources of a geographical

area (cell)

► Suggests moving base station closer to mobile

client

SDN in Cellular Networks (1/3)

CellSDN [Li et al.’12] and SoftCell [Jin et

al.’13] are efforts to improve the cellular

core network using SDN.

► Current cellular networks consist of Base

Stations (BS)

► BS connects to mobility manager – Serving-

GW(S-GW)



SDN in Cellular Networks, SDN in 5G

► MobileFlow1, CellSDN2 – Software Defined

Mobile Networks (Architecture/Framework)1

► The authors of 3 have proposed to build and

deploy an open—but backward

compatible—wireless network infrastructure

that can be easily deployed on college

campuses worldwide.

► Radio resource sharing between different

cellular operators as well as cellular and

WiFi operators4.

1 Pentikousis K et al, "Mobileflow: Toward software-defined mobile networks", IEEE Communication Magazine 2013.2 L. Li et al, “Towards Software Defined Cellular Networks”, EWSDN 2012.3 K. Yap et al., Blueprint for Introducing Innovation into Wireless Mobile Networks, ACM VISA, 2010.4 Toktam Mahmoodi et al, “Radio resource sharing as a service in 5G: SDN approach”, in IEEE Trans. NSM 2015 (under review).



Future Trends - SDN in Cellular Networks, SDN in 5G

► SDN based control plane in 5G.

► Handling of Increased Mobile traffic volume using SDN paradigm.

► SDN assisted data offloading for 5G networks.


OpenFlow Wireless [Yap et al.’10] envisage a future where end

user is free from worrying about the details as to which wireless

network is serving him/her.

► Open Research problem: The design of a mobility manager capable of

servicing each customer.

OpenFlow in Wireless Networks (1/3)


[FlowVisor - Sherwood et al.‘10] Another important feature of

SDN enabled WLANs is Slicing.

► Separates traffic flow into individual subspaces / slices. Each slice

managed by a separate controller.




Aeroflux [Schulz-Zander et al. ‘14] improvised Odin1 by proposing

a two tier control plane

► Near-Sight Controller – Frequently occurred events (latency critical tasks)

► Global Controller – Network monitoring or load balancing

► Radio Agent exposes all functionalities

Light Virtual AP (LVAP)1 abstraction

Handling WiFi data path entries

Provide interface for statistic collection

1 Lalith Suresh et al. “Towards Programmable Enterprise WLANs with Odin”

2012, HotSDN


Mesh Networks► [Dely et al. 2011] – Feasibility study of

Openflow in Wireless Mesh Network

► Primary challenge is frequently changing

topology

► Proposed – All nodes are Openflow

enabled mesh routers. Each wireless

interface is used for both control and data

traffic

► Plane Separation is achieved by using

different SSIDs (Service Set IDs)

Multi-Hop Wireless Networks (1/2)


MCC [Chen and Krishnamachari 2011] – A multi channel time-

scheduled protocol for real-time data collection in WSNs.

► MCC design features centralized channel allocation and time scheduling to

address co-channel interference, and routes from sources to the sink are

centrally computed

Multi-Hop Wireless Networks (2/2)

MCC – Multi Channel Collection Protocol uses improvised balanced

routing tree formation

► There is a centralized control

plane which obtains information

about the network topology in

order to configure the

transmission and routing

decisions for all nodes


Knowledge-Defined NetworkingMotivation

Rapid development of communication technologies lead to the

dramatic growth on complexity, heterogeneity, and scale of

networks

The need of an autonomous network that have ability

► To perform well in the partial or conflicting context

► To recognize and mediate conflicts in policies and goals

► To respond to problems and attacks as fast as possible

► To perform optimizations in high-dimensional environments that are too

complicated to be addressed by humans or any other analytical approaches

► To operate automatically or partly on behalf of human to minimize the role

of human in the network management

Machine Learning (ML) offers the most suitable approach for

implementing that autonomous network


Knowledge-Defined NetworkingDefinition

Most ML algorithms are not designed to work in a highly

distributed context like networking

Networks are inherently distributed systems where each node

(i.e. router), has only a partial view and control over the system

SDN provides a logically centralized control and view of the

whole network for ML algorithms to gather knowledge about the

network, and exploit that knowledge to control the network

The combination of ML and SDN leads to a new research topic

called Knowledge-Defined Networking (KDN)


A ML algorithm is an algorithm that is able to learn from data

There are 3 types of ML algorithms (as shown in the figure)

► Supervised learning: the dataset consists of “input, output” pairs and the

learning algorithm have to find out the function that map input to output

► Unsupervised learning: the dataset contains only input and the learning

algorithm has to find out the relation between samples in the dataset

► Reinforcement learning: a learning algorithm that interacts with an

environment and act in a suitable way to maximize received rewards from

that environment

Knowledge-Defined NetworkingOverview of Machine Learning (1/2)

Machine Learning

Reinforcement

LearningUnsupervised

Learning

Supervised

Learning


ML has been gaining a lot of attention from many computer

science areas and has achieved remarkable successes

► Computer vision (object recognition, object detection, etc.)

► Natural language processing (text generation, machine translation, etc.)

► Robotics (self-driving car, humanoid, etc.)

► Social network (search engine, recommendation system, etc.)

► Network security (intrusion detection, etc.)

However, the deployment of ML in network related research is

still in the early phase

Knowledge-Defined NetworkingOverview of Machine Learning (2/2)


Knowledge-Defined NetworkingOverview of KDN system (1/3)

Albert Mestres et al., “Knowledge-Defined Networking”, ACM SIGCOMM 2017

KDN operational loop



Analytics platform

► Gathers information from forwarding elements to provide a complete view of

the network

► Obtains control and management state from the controller

► Keeps a historical record of the collected data

► Feeds data to ML algorithm in the Knowledge Plane (KP)

SDN controller

► Receives the decisions from the knowledge plane

► Generates policies based on these decisions to control the forwarding

elements from the data plane

Forwarding elements

► Contains routers, switches, etc.



Machine learning

► In closed loop, the knowledge plane makes decisions on behalf of the

network operator

► In open loop, the network operator is in charge of making the decisions with

the assistance of the knowledge plane

Closed loop Open loop

Supervised Automation Optimization

ValidationEstimation What-if analysis

Unsupervised Improvement Recommendation

Reinforcement Automation Optimization

N/A


Knowledge-Defined NetworkingBenefits

Fault diagnosis and mitigation

► Offers a better interaction between users and the network

► Exchanges information and predict when failures happen in the network

► Performs suitable action to mitigate the failure

Automatic (re)configuration

► Automatically or partly (re)configures when the condition changes

Performance management

► Continually monitors and performs prediction mechanism to reduce the

amount of traffic when the overhead occurs

Security management

► Detects abnormal behaviors and act accordingly


Knowledge-Defined NetworkingChallenges

Representative of the data

► The success of an ML algorithm much relies on the representative of data

Ground truth

► The difficulty of providing true labels for ML dataset

Routing of knowledge and ML techniques for networking

► The problem of how can we express knowledge from a KP to others

Requirements of new ML mechanism

► There is still no specific ML for networking

Security of ML

► The problem of how to avoid malicious components that poison the dataset


Network Function Virtualization (NFV) (1/3)

Network Functions Virtualization (NFV) is an initiative to

virtualize network functions, previously carried out by dedicated

hardware.



▪ Reduced Power

▪ Lower CapEx and OpEx

▪ Reduced Time To Market

▪ Open Market to SW Suppliers

▪ Flexible

▪ Standard Hardware



Source: Network Function Virtualization: State-of-the-Art and Research Challenges.

A practical example of NFV.

Traditional CPE implementations Possible CPE implementation with NFV


NFV Architectural Framework (1/3)

NFV Management and Orchestration (MANO): Responsibility for

management VNF and service.

• Orchestrator

• VNF Manager (VNFM)

• VIM

NFV Infrastructure (NFVI)



NFV Orchestrator

► Generates, maintains and tears down network services of VNF

themselves. If there are multiple VNFs, orchestrator will enable

creation of end to end service over multiple VNFs.

► Responsible for global resource management of NFVI resources.

For example managing the NFVI resources i.e. compute, storage

and networking resources among multiple VIMs in network.

► Performs its functions by NOT talking directly to VNFs but through

VNFM and VIM.



VNF Manager

► Manages a VNF or multiple VNFs

► Life cycle management of VNF instances

► Can do the same functions as EMS but through open

interface/reference point (Ve-Vnfm).

VIM (Virtualized Infrastructure Manager)

► The management system for NFVI.

► Responsible for controlling and managing the NFVI compute,

network and storage resources within one operator’s infrastructure

domain

► Responsible for collection of performance measurements and

events.


SDDC Introduction Video

Content

► Why SDDC?

► Features and Benefits of SDDC

► How to transform to SDDC?

Duration


VIDEO

TransformyourdatacenterwithSDN.mp4


RouteFlow (RF) (1/2)

Provides virtualized IP routing service over OpenFlow (OF)

enabled HW:

► OF HW needs Flow tables to make forwarding decisions

► Linux based routing engines populate the Forwarding Information Base

(FIB), used for destination lookup

► Provided mechanism to convert the FIB entries to OF Flow table entries

► https://www.youtube.com/watch?v=YduxuBTyjEw

https://www.youtube.com/watch?v=YduxuBTyjEw


RouteFlow (RF) (2/2)

Virtual network environment:

► Each VM maps to one OF HW

► Server virtualization technique used to create Virtual Machine (VM) to

reproduce the connectivity of a physical infrastructure

► Each VM runs one instance of IP routing engine (e.g. Linux-based

Quagga)

► All state information (e.g. routing tables) are exchanged in the virtual

network by the routing engines

► Linux Containers (LXC)1 used as the virtualization mechanism as it is a

lightweight mechanism.

1

http://rubenlaguna.com/blog/2014/09/15/comparing-hypervisors/

http://www.itworld.com/article/2915530/virtualization/containers-vs-virtual-machines-how-to-tell-which-is-the-right-choice-for-your-

enterprise.html

https://www.flockport.com/lxc-vs-docker/

https://www.scriptrock.com/articles/docker-vs-vagrant

Info. taken from https://www.clear.rice.edu/comp529/www/papers/tutorial_4.pdf

http://rubenlaguna.com/blog/2014/09/15/comparing-hypervisors/

http://www.itworld.com/article/2915530/virtualization/containers-vs-virtual-machines-how-to-tell-which-is-the-right-choice-for-your-enterprise.html

https://www.flockport.com/lxc-vs-docker/

https://www.scriptrock.com/articles/docker-vs-vagrant

https://www.clear.rice.edu/comp529/www/papers/tutorial_4.pdf


Route Flow Introduction Video

Content

► Introduction of Route Flow

► Introduction of different components in Route Flow

► Working of Route Flow

Duration


VIDEO

RFtutorial.mp4


RouteFlow (RF)- Architecture(1/4)

Key Features:

► Separation of data and control planes

► Loosely coupled architecture

► Three RouteFlow (RF) Components

Controller, Server and Slaves

► Unmodified routing protocol stacks

► Portable to multiple controllers

RF-Controller acts as “Proxy” app

► Multi-virtualization technologies

► Multi-vendor data plane hardware

Taken from

http://changeofelia.info.ucl.ac.be/pmwiki/uploads/SummerSchool/Program/session_007.pdf.


RouteFlow (RF) - Architecture(2/4)

RF Server:

► The “brain” of RF

► Manages available Virtual Machine (VM)

► Configures the virtual environment

► Receives events from RF-Controller

► Associates VMs and OF switches

► Determines packet delivery from/to VMs

► Requests flow installation / modification

in OF switches

Taken from




RF-Controller:

► App on NOX controller

► Sends packets and events it receives

from OF- Switch and OF HW to RF

Server

► Sends packets it receives from RF

Server to OF-Switch (control packets) or

OF HW (to deal with flow additions /

deletions)

► Provides and interface to the rest of the

framework to the OF HW

Taken from




RF-Slave:

► Runs as a daemon in Linux-based VM

► Registers the VM with RF-Server

► Configures the VM (e.g. interfaces)

► Listens to ARP and IP table updates via

Linux Netlink events

► Translates routing updates into flow

rules

► Translates ARP entries into flow rules

► Sends flow update commands to RF-

Server

► Agnostic to the Routing Engine

Taken from



SDN and NFV Introduction Video

Content

► Why and how to combine SDN and NFV

► Features and benefits of SDN and NFV

► How it works

Duration


VIDEO

NetworkVirtualizationIntroduction.mp4


SDN and NFV (1/2)


SDN and NFV (2/2)

Research Areas / Challenges:

► Two major aspects of SDN that may need to be improved in order to meet

the requirements of NFV: the Southbound API (mainly OpenFlow), and

controller designs

► OpenFlow supports only L2-L4 flow handling. L5-L7 are required to

support NFV

► Improve SDN by considering distributed architectures

► Controller design should address/include distributed network management

and scalability.


OpenFlow provides a capability which can be used to route

flows based on any arbitrary values in the match field of the

flow entries

[Agarwal INFOCOM’13] showed how the SDN can be used

effectively for traffic engineering during deployment stage

Load balancing is a classical application of SDN traffic

engineering

► [Costanzo12] proposed energy-aware routing. By measuring the energy

output, the controller could easily route (in low traffic situation)

► Controller gauges the usage levels of devices by analyzing the statistics

► If crossing threshold, controller could propagate a new rule to relieve

heavily used node/link and distributing it to other devices

► As a result, the SDN controller balances the load in wireless network

Traffic Engineering in SDN Based Wireless

Networks

Information taken from paper – “A Survey of Software-Defined Wireless Networks” by Adam Drescher.


In Nutshell: Research Areas in SDN (1/4)

Controller and switch design

► SDN has significant scalability, performance, robustness, and security

challenges.

Scalability and performance in SDN

► Decentralized, like the Internet, call for a control plane that is logically

distributed (in order to boost Scalability).

Controller-service interfacing (North Bound Interfaces)

► Various controllers exist but their application interfaces are still in the early

stages and independent from each other. (Frenetic, Procera, Nettle, …).


In Nutshell: Research Areas in SDN (2/4)

Virtualization and Cloud service applications

► High demand for virtualization and cloud services. The challenges include

rapid provisioning and efficient resource management.

Handoffs

► Seamless handoff between service providers (Vertical Handover)

Monitoring and Status Report

► Estimating the different wireless channels status (delay, loss rate)

► Topology Discovery, including close access points identification


SDN-based Cloud Introduction Video

Content

► Why SDN and Cloud

► Features and benefits of SDN-based Cloud

► How it works

Duration


VIDEO

EricssonCloudSystem.mp4


In NutShell: Research Areas in SDN (3/4)

Information Centric Networking (ICN)

► A new paradigm proposed for the future architecture of the Internet, which

aims to increase the efficiency of content delivery and content availability.

► The driving motivation is that the current Internet is information-driven, yet

networking technology is still focused on the idea of location-based

addressing and host-to-host communication. Shift from “named hosts”

based architecture to “named data”.

► How to combine ICN with SDN towards “Software-Defined Information-

Centric Networks”?


In NutShell: Research Areas in SDN (4/4)

Enabling heterogeneous networking with SDN

► A major challenge is efficient utilization of resources (Capacity, Radio).

► The heterogeneous characteristics of the underlying networks (e.g.,

physical medium, topology, stability) and nodes (e.g., buffer size, power

limitations, mobility) also add another important factor when considering

routing and resource allocation.

► SDN techniques largely target infrastructure–based networks.

Centralized control mechanism that is ill–suited to the level of

decentralization, disruption, and delay present in infrastructure-less

environments.


Case study: Autonomic Computing

Attributes (1/4)

The function of any autonomic capability is a control loop that

collects details from the system and acts accordingly

Autonomic systems is a self-managing autonomous and

ubiquitous computing environment that completely hides its

complexity and provides the user with an interface that exactly

meets his needs

Four aspects of self-management often cited by IBM are,

► Self-Configuring: The ability to readjust itself “on-the fly”

► Self-Protecting: Anticipate, detect, identify, and protect itself from attacks

► Self-Optimizing: Maximize resource utilization to meet end-user needs

► Self-Healing: Discover, diagnose, and react to disruptions


Case study: Autonomic Computing Attributes (2/4)

Origin of Self-healing Networks

It is described as a combination of self-diagnosing and self-

repairing with the capabilities to diagnose and recover from

malfunctions

Research on self-healing systems has its origin in fault-tolerant

and self-stabilizing systems research

Fault-tolerant systems handle transient and mask permanent

failures in order to return to a valid state

These systems have two distinct properties.

► The system is guaranteed to return to a legal state in a finite amount of time

regardless of interferences

► Once in legal state it tries to remain in the same



Properties of Self-healing



Architecture for Self-healing in SDN

We propose a self-healing system for SDN which is capable of

optimally handling the failures in SDN

SDN Controller (NOX)

Rapid

Recovery

Module

Flow

Management

Action

Management

Resource

Management

Co

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ol P

lan

e

Da

ta

Pla

ne

Optimized

Self-healing

Management

Topology

Discovery &

Management

Policy

Management

Load

Balancing

Routing

Management

Netw

ork

Ma

nag

em

en

t

Ap

plic

atio

n

Sw

itch

Lev

el

Mg

mt.

Network

Statistics

Management

Forwarding

Information

Base

OpenFlow

Notification

Module


References ONF – [https://www.opennetworking.org]

OMG – [http://sdn.omg.org]

ITU-T – [http://itu.int/en/ITU-T/Pages/default.aspx]

TTA – [http://www.tta.or.kr/English/index.jsp]

IEEE – [http://sdn.ieee.org/standardization]

BBF – [https://www.broadband-forum.org]

MEF – [https://www.mef.net/ ]

ODCA – [http://www.opendatacenteralliance.org]

OIF – [http://www.oiforum.com]

3GPP – [http://www.3gpp.org]

ODL – [http://www.opendaylight.org]

ONOS – [http://onosproject.org]

Floodlight – [http://www.projectfloodlight.org/floodlight]

OpenStack – [http://www.openstack.org]

https://www.opennetworking.org/

http://sdn.omg.org/

http://itu.int/en/ITU-T/Pages/default.aspx

http://www.tta.or.kr/English/index.jsp

http://sdn.ieee.org/standardization

https://www.broadband-forum.org/

https://www.mef.net/

http://www.opendatacenteralliance.org/

http://www.oiforum.com/

http://www.3gpp.org/

http://www.opendaylight.org/

http://www.3gpp.org/

http://www.projectfloodlight.org/floodlight/

http://www.openstack.org/


Standardization Activities (1/4)

Open Networking Foundation (ONF)

► A user-driven organization for SDN standardization. ONF working groups

analyze SDN requirements, evolve the OpenFlow Standard and research

new SDN standards.

Object Management Group (OMG)

► SDN Working Group for vendor neutral Global Standard.

Telecommunication Technology Association (TTA)

► A non-profit organization provides certification services to establish new

standards for the ICT industry in Korea.



ITU-T Study Groups (SG) work

► SG11- develops signaling requirements and protocols for SDN.

► SG13- develops the SDN framework, as baseline of all ITU-T SDN

standardization.

► SG15- lays recommendations for "Architecture for SDN control of

Transport Networks".

► SG16- governs the OpenFlow based Virtual Content Delivery Networks.

► SG17- security aspects of SDN.

ITU-T International Telecommunication Union - Telecom Standard Sector



The Metro Ethernet Forum (MEF)

► This forum focuses on defining service orchestration with APIs for existing

networks.

ODCA

► An organization working on unifying data center in the migration to cloud

computing environments through interoperable solutions.

OIF

► Goal is to describe the features / functionalities needed to support the

deployment of SDN capabilities in carrier transport networks.

ODCA Open Data Center Alliance

OIF Optical Internetworking Forum



3GPP

► The mobile networking industry 3GPP consortium is studying the

management of virtualized networks, an effort aligned with the ETSI NFV

architecture to leverage SDN

IEEE (P802.1CF project)

► SDN standardization project to drive the functionality and interoperability

of SDN.

The Broadband Forum (BBF)

► Objective is to release recommendations for supporting SDN in multi-

service broadband networks.

IEEE Institute of Electrical and Electronics Engineers

3GPP 3rd Generation Partnership Project


SDN Current Projects (1/2)

OpenDayLight (ODL)

► A collaborative, open source project to advance SDN. ODL is a

community-led, open and industry-supported framework for Software-

Defined Networking.

OpenStack

► An open source software project that aims to produce an open source

cloud operating system. It provides multi-tenant IaaS, and aims to meets

the needs of public and private clouds regardless of size, by being simple

to implement and massively scalable.


SDN Current Projects (2/2)

Open Network Operating System (ONOS)

► The SDN network operating system for service providers architected for

performance, high availability, scale-out and well-defined northbound and

southbound abstractions and interfaces.

Floodlight

► An Open SDN Controller is an enterprise-class, high performance

OpenFlow Controller which can handle mixed OpenFlow and non-

OpenFlow networks. It is the core of a commercial produce from Big

Switch Networks.

SDN and NFV - SKKUmonet.skku.edu/wp-content/uploads/2018/08/W15.SDN_NFV.pdf · 2018-12-12 ·...

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