Neutron Done the SDN Way
Dragonflow is an open source distributed control plane implementation of Neutron which is an integral part of OpenStack. Dragonflow introduces innovative solutions and features to implement networking and distributed network services in a manner that is both lightweight and simple to extend, yet targeted towards performance-intensive and latency-sensitive applications. Dragonflow aims at solving the performance
DragonFlow sdn based distributed virtual router for openstack neutronEran Gampel
Dragonflow is an implementation of a fully distributed virtual router for OpenStack® Neutron™ that is based on a light weight SDN controller
blog.gampel.net
Topology Service Injection using Dragonflow & KuryrEshed Gal-Or
Container-based Dynamic Service Chain using Distributed SDN Pipeline Injection (Openstack, Dragonflow, Kuryr).
With NFV becoming a reality, OpenStack Cloud deployments grow in number and size, giving birth to issues of scale, performance and service flexibility.
Neutron Done the SDN Way
Dragonflow is an open source distributed control plane implementation of Neutron which is an integral part of OpenStack. Dragonflow introduces innovative solutions and features to implement networking and distributed network services in a manner that is both lightweight and simple to extend, yet targeted towards performance-intensive and latency-sensitive applications. Dragonflow aims at solving the performance
DragonFlow sdn based distributed virtual router for openstack neutronEran Gampel
Dragonflow is an implementation of a fully distributed virtual router for OpenStack® Neutron™ that is based on a light weight SDN controller
blog.gampel.net
Topology Service Injection using Dragonflow & KuryrEshed Gal-Or
Container-based Dynamic Service Chain using Distributed SDN Pipeline Injection (Openstack, Dragonflow, Kuryr).
With NFV becoming a reality, OpenStack Cloud deployments grow in number and size, giving birth to issues of scale, performance and service flexibility.
Running Neutron at Scale - Gal Sagie & Eran Gampel - OpenStack Day Israel 2016Cloud Native Day Tel Aviv
For the past 2 years we’ve been working on running OpenStack in ever growing scales. In Juno we started Dragonflow, a Neutron integrated distributed SDN project with an ambitious goal – scaling to 10,000 physical servers in a single zone. Although we’re not there yet, we’re definitely on the right track.
Dragonflow employs the following principles
• Pluggable NoSQL DB – to adapt for different size deployments and SLAs
• Distribution of Policies rather than flows – moving the “brain” to the edges
• Distributed architecture – enforcing network policies in the compute nodes
• Hybrid flow pipeline – Utilizing both proactive and reactive flows to easily allow built-in distributed smart apps (e.g. distributed DHCP)
Cumulus Linux Network OS Brings Modern Data Center Networking to the Enterprise
Cumulus® Linux® 2.2 brings greater flexibility, simplified operations and end-to-end resiliency along with a new hardware architecture and new ecosystem solutions
Hagen Toennies from Gaikai Inc. presented this deck at the 2017 HPC Advisory Council Stanford Conference.
"In this talk we will present how we enable distributed, Unix style programming using Docker and Apache Kafka. We will show how we can take the famous Unix Pipe Pattern and apply it to a Distributed Computing System. We will demonstrate the development of two simple applications with the focus on "Do One Thing and Do It Well." Afterwards we demonstrate how we make these two programs work to together using Apache Kafka. By encapsulating our applications in containers we will also show how that enables us to go from the limited resources of a development machine to cluster of computers in a data center without changing our applications or containers."
Watch the video: http://wp.me/p3RLHQ-goG
Learn more: http://www.hpcadvisorycouncil.com/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
A study and practice of OpenStack release Kilo HA deployment. The Kilo document has some errors, and it's hardly find a detailed document to describe how to deploy a HA cloud based on Kilo release. Hope this slides can provide some clues.
OpenStack Networks the Web-Scale Way - Scott Laffer, Cumulus NetworksOpenStack
Audience Level
Beginner
Synopsis
Layer 2 versus Layer 3, MLAG, Spanning-Tree, switch mechanism drivers, overlays and routing-on-the-host — What scales and what does not? The underlying plumbing of an OpenStack network is something you’d rather not have to think about. This presentation examines the network architectures of web-scale and large enterprise OpenStack users and how those same efficiencies can be used in deployments of all sizes.
Speaker Bio:
Scott is a Member of Technical Staff at Cumulus Networks where he designs, supports and deploys web-scale technologies and architectures in enterprise networks globally. Prior to becoming a founding member of the Cumulus office in Australia, Scott started his career as a network administrator before joining Cisco Systems to support their data centre products.
OpenStack Australia Day Melbourne 2017
https://events.aptira.com/openstack-australia-day-melbourne-2017/
Advanced Data Retrieval and Analytics with Apache Spark and Openstack SwiftDaniel Krook
Lightning talk from the OpenStack NYC meetup on October 8, 2014.
http://bit.ly/ibm-os-meetup
By Gil Vernik
The integration between Apache Spark and Swift, and the use of Storlets for smart retrieval via filtering and privacy-support.
The content of this talk is a statement from the IBM Research division, not IBM product divisions, and is not a statement from IBM regarding its plans, directions or product intents. Any activities described by this talk are subject to change.
While every new release of OpenStack offers improvements in functionality and the user experience, one thing’s for certain: troubleshooting is hard if you don’t know where to start.
Join us as we cover some common and not-so-common issues with Nova and Neutron that lead to some of our favorite error messages, including “No valid host was found”. Participants will learn basic troubleshooting procedures, including tips, tricks, and processes of elimination, to get their cloud back on track.
Running Neutron at Scale - Gal Sagie & Eran Gampel - OpenStack Day Israel 2016Cloud Native Day Tel Aviv
For the past 2 years we’ve been working on running OpenStack in ever growing scales. In Juno we started Dragonflow, a Neutron integrated distributed SDN project with an ambitious goal – scaling to 10,000 physical servers in a single zone. Although we’re not there yet, we’re definitely on the right track.
Dragonflow employs the following principles
• Pluggable NoSQL DB – to adapt for different size deployments and SLAs
• Distribution of Policies rather than flows – moving the “brain” to the edges
• Distributed architecture – enforcing network policies in the compute nodes
• Hybrid flow pipeline – Utilizing both proactive and reactive flows to easily allow built-in distributed smart apps (e.g. distributed DHCP)
Cumulus Linux Network OS Brings Modern Data Center Networking to the Enterprise
Cumulus® Linux® 2.2 brings greater flexibility, simplified operations and end-to-end resiliency along with a new hardware architecture and new ecosystem solutions
Hagen Toennies from Gaikai Inc. presented this deck at the 2017 HPC Advisory Council Stanford Conference.
"In this talk we will present how we enable distributed, Unix style programming using Docker and Apache Kafka. We will show how we can take the famous Unix Pipe Pattern and apply it to a Distributed Computing System. We will demonstrate the development of two simple applications with the focus on "Do One Thing and Do It Well." Afterwards we demonstrate how we make these two programs work to together using Apache Kafka. By encapsulating our applications in containers we will also show how that enables us to go from the limited resources of a development machine to cluster of computers in a data center without changing our applications or containers."
Watch the video: http://wp.me/p3RLHQ-goG
Learn more: http://www.hpcadvisorycouncil.com/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
A study and practice of OpenStack release Kilo HA deployment. The Kilo document has some errors, and it's hardly find a detailed document to describe how to deploy a HA cloud based on Kilo release. Hope this slides can provide some clues.
OpenStack Networks the Web-Scale Way - Scott Laffer, Cumulus NetworksOpenStack
Audience Level
Beginner
Synopsis
Layer 2 versus Layer 3, MLAG, Spanning-Tree, switch mechanism drivers, overlays and routing-on-the-host — What scales and what does not? The underlying plumbing of an OpenStack network is something you’d rather not have to think about. This presentation examines the network architectures of web-scale and large enterprise OpenStack users and how those same efficiencies can be used in deployments of all sizes.
Speaker Bio:
Scott is a Member of Technical Staff at Cumulus Networks where he designs, supports and deploys web-scale technologies and architectures in enterprise networks globally. Prior to becoming a founding member of the Cumulus office in Australia, Scott started his career as a network administrator before joining Cisco Systems to support their data centre products.
OpenStack Australia Day Melbourne 2017
https://events.aptira.com/openstack-australia-day-melbourne-2017/
Advanced Data Retrieval and Analytics with Apache Spark and Openstack SwiftDaniel Krook
Lightning talk from the OpenStack NYC meetup on October 8, 2014.
http://bit.ly/ibm-os-meetup
By Gil Vernik
The integration between Apache Spark and Swift, and the use of Storlets for smart retrieval via filtering and privacy-support.
The content of this talk is a statement from the IBM Research division, not IBM product divisions, and is not a statement from IBM regarding its plans, directions or product intents. Any activities described by this talk are subject to change.
While every new release of OpenStack offers improvements in functionality and the user experience, one thing’s for certain: troubleshooting is hard if you don’t know where to start.
Join us as we cover some common and not-so-common issues with Nova and Neutron that lead to some of our favorite error messages, including “No valid host was found”. Participants will learn basic troubleshooting procedures, including tips, tricks, and processes of elimination, to get their cloud back on track.
OpenStack Neutron Havana Overview - Oct 2013Edgar Magana
Presentation about OpenStack Neutron Overview presented during three meet-ups in NYC, Connecticut and Philadelphia during October 2013 by Edgar Magana from PLUMgrid
Scaling OpenStack Networking Beyond 4000 Nodes with Dragonflow - Eshed Gal-Or...Cloud Native Day Tel Aviv
As OpenStack matures, more users move from “dipping a toe” to deploying at large scale, with 1000's of nodes.
OpenStack networking has long been a limiting factor in scaling beyond a few hundreds of nodes, forcing users to turn to cell splitting, or to complete offloading of the networking to the underlay systems and forfeit the overlay network altogether.
Dragonflow is a fully distributed, open source, SDN implementation of Neutron, that handles large scale deployments without splitting to cells.
In testing we've conducted, we were able to scale to 4000+ controllers (each controller is typically deployed on a compute node), while maintaining the same performance we had on a small 30 node environment.
Building Multi-Site and Multi-OpenStack Cloud with OpenStack CascadingJoe Huang
The slides used in the speech "Building multi-site and multi-openstack cloud with OpenStack cascading" in OpenStack Paris summit 2014. The slides cover the requirement and driving forces, case study of VDF, technologies eloboration and demo of OpenStack cascading.
HTTP/2 Comes to Java: Servlet 4.0 and what it means for the Java/Jakarta EE e...Edward Burns
Servlet is very easily the most important standard in server-side Java. The much awaited HTTP/2 standard is now complete, was fifteen years in the making and promises to radically speed up the entire web through a series of fundamental protocol optimizations.
In this session we will take a detailed look at the changes in HTTP/2 and discuss how it may change the Java ecosystem including the foundational Servlet 4 specification included in Java/Jakarta EE 8.
Cloud providers like Amazon or Goggle have great user experience to create and manage PaaS and IaaS services. But is it possible to reproduce same experience and flexibility locally, in on premise datacenter? This talk describes success story of creation private cloud based on DC/OS cluster. It is used to host and share different services like hadoop or kafka for development teams, dynamically manage services and resource pools with GKE integration.
SDN, Network Virtualization and the Software Defined Data Center – Brad HedlundChef Software, Inc.
IT organizations around the world are transforming data center operations and economics by virtualizing their networks. Much like server virtualization decoupled VMs from the underlying X86 server hardware transforming the operational model of compute, network virtualization decouples software-based virtual networks from the underlying network hardware to enable a new operational model for networking. Deployed non-disruptively on any existing network without change, network virtualization transforms the physical network into a pool of capacity that can be consumed and repurposed on demand.
You will learn how, today, companies like AT&T, NTT, eBay and Rackspace have transformed their operational model and reduced network provisioning time from days/weeks to seconds. You will learn how network virtualization, OpenStack cloud management and Chef automation can be leveraged together and examine the architectural decisions you should be considering now to prepare for this transformation
Albert Greenberg
Director of Development
Microsoft
Keynotes Session
Summary
• Scenario: BYO Virtual Network to the Cloud
• Per customer, with capabilities equivalent to on premise counterpart
• Challenge: How do we scale virtual networks across millions of servers?
• Solution: Host SDN solves it: scale, flexibility, timely feature rollout, debuggabililty
• Virtual networks, software load balancing, …
• How: Scaling flow processing to millions of nodes
• Flow tables on the host, with on-demand rule dissemination
• RDMA to storage
• Demo: ExpressRoute to the Cloud (Bing it!)
ONS2015: http://bit.ly/ons2015sd
ONS Inspire! Webinars: http://bit.ly/oiw-sd
Watch the talk (video) on ONS Content Archives: http://bit.ly/ons-archives-sd
Presentation given at the 2017 LinuxCon China
With the booming of Container technology, it brings obvious advantages for cloud: simple and faster deployment, portability and lightweight cost. But the networking challenges are significant. Users need to restructure their network and support container deployment with current cloud framework, like container and VMs.
In this presentation, we will introduce new container networking solution, which provides one management framework to work with different network componenets through Open/friendly modelling mechnism. iCAN can simplify network deployment and management with most orchestration systems and a variety of data plane components, and design extendsible architect to define and validate Service Level Agreement(SLA) for cloud native applications, which is important factor for enterprise to deliver successful and stable service via containers.
Solved: Your Most Dreaded Test Environment Management ChallengesDevOps.com
Modern application delivery pipelines rely on series of increasingly complex test environments. Manual processes and ad-hoc management typically lead to misconfigured environments, scheduling conflicts, and project delays. You know automation and transitioning resources to the cloud can help, but don’t know where to start and unclear how to prove the value.
Join Amazon Web Services (AWS) and Plutora in this joint presentation on managing test environments and transitioning them to the cloud. Learn secrets about
Why you need a single source of truth to smooth out scheduling kinks.
How to improve configuration tracking and management to enhance validation efforts.
The best way to manage the complexity of large-scale test environments.
How to reduce costs and eliminate conflicts by identifying and moving environments to the cloud.
The Good, the Bad and the Ugly of Migrating Hundreds of Legacy Applications ...Josef Adersberger
Running applications on Kubernetes can provide a lot of benefits: more dev speed, lower ops costs, and a higher elasticity & resiliency in production. Kubernetes is the place to be for cloud native apps. But what to do if you’ve no shiny new cloud native apps but a whole bunch of JEE legacy systems? No chance to leverage the advantages of Kubernetes? Yes you can!
We’re facing the challenge of migrating hundreds of JEE legacy applications of a major German insurance company onto a Kubernetes cluster within one year. We're now close to the finish line and it worked pretty well so far.
The talk will be about the lessons we've learned - the best practices and pitfalls we've discovered along our way. We'll provide our answers to life, the universe and a cloud native journey like:
- What technical constraints of Kubernetes can be obstacles for applications and how to tackle these?
- How to architect a landscape of hundreds of containerized applications with their surrounding infrastructure like DBs MQs and IAM and heavy requirements on security?
- How to industrialize and govern the migration process?
- How to leverage the possibilities of a cloud native platform like Kubernetes without challenging the tight timeline?
Migrating Hundreds of Legacy Applications to Kubernetes - The Good, the Bad, ...QAware GmbH
CloudNativeCon North America 2017, Austin (Texas, USA): Talk by Josef Adersberger (@adersberger, CTO at QAware)
Abstract:
Running applications on Kubernetes can provide a lot of benefits: more dev speed, lower ops costs, and a higher elasticity & resiliency in production. Kubernetes is the place to be for cloud native apps. But what to do if you’ve no shiny new cloud native apps but a whole bunch of JEE legacy systems? No chance to leverage the advantages of Kubernetes? Yes you can!
We’re facing the challenge of migrating hundreds of JEE legacy applications of a major German insurance company onto a Kubernetes cluster within one year. We're now close to the finish line and it worked pretty well so far.
The talk will be about the lessons we've learned - the best practices and pitfalls we've discovered along our way. We'll provide our answers to life, the universe and a cloud native journey like:
- What technical constraints of Kubernetes can be obstacles for applications and how to tackle these?
- How to architect a landscape of hundreds of containerized applications with their surrounding infrastructure like DBs MQs and IAM and heavy requirements on security?
- How to industrialize and govern the migration process?
- How to leverage the possibilities of a cloud native platform like Kubernetes without challenging the tight timeline?
Atmosphere 2016 - Diptanu Choudhury - Taming the public clouds with nomadPROIDEA
Distributed Cluster Schedulers are becoming increasingly popular. They present a good abstraction for running workloads at a “warehouse-scale” on the public and private clouds by decoupling workload from compute, network and storage resources.
In this talk, we will talk about the operational challenges of running a Cluster Scheduler to serve highly available services across multiple geographies and in a heterogeneous runtime environment. We will go into details of the needs from a cluster scheduler with respect to managing multiple runtime/virtualization platforms, provide observability, running maintenance on hardware and software, etc.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
2. Dragonflow
Page 2
• Integral part of OpenStack
• Fully Open Source
• Scale, Performance and Latency
• Lightweight and Simple
• Easily Extendable
• Distributed SDN Control Plane
• Sync Policy Level abstraction to the CN
3. Dragonflow - Distributed SDN
Neutron-Server
Dragonflow Plugin
DB
OVS
Dragonflow
DB
Driver
Compute Node
OVS
Dragonflow
DB
Driver
Compute Node
OVS
Dragonflow
DB
Driver
Compute Node
OVS
Dragonflow
DB
Driver
Compute Node
DB
VM VM
..
VM VM
..
VM VM
.. VM VM
..
4. Compute NodeCompute NodeCompute Node
Dragonflow
Network DB
OVS
Neutron
Server
OVSDB
OVSDB-Server
ETCD RethinkDBRAMCloud
Kernel Datapath Module
NIC
User Space
Kernel Space
Dragonflow DB Drivers
OVSDB ETCD RethinkDBRMC
Future
Dragonflow Plugin
Route
Core
API
SG
vswitchd
Container
VM Dragonflow Controller
Abstraction Layer
L2 App L3 App DHCP App
Fault
Detection
SG
LBaaS …FWaaS
Pluggable DB
Layer
NBDBDrivers
SB DB Drivers
smartNIC OVSDB
OVSDB
ETCD
RMC
RethinkDB
OpenFlow
Dragonflow – Under The Hood
5. Current Release Features (Liberty)
L2 core API, IPv4, IPv6
GRE/VxLAN/Geneve tunneling protocols
Distributed L3 Virtual Router
Hybrid proactive + reactive flow installation
North-South traffic is still centralized
Distributed DHCP
(with just 500 lines of code!)
Pluggable Distributed Database
ETCD, RethinkDB, RAMCloud, OVSDB
8. 1 VM Send DHCP_DISCOVER
2 Classify Flow as DHCP, Forward to Controller
3 DHCP App sends DHCP_OFFER back to VM
4 VM Send DHCP_REQUEST
5 Classify Flow as DHCP, Forward to Controller
6 DHCP App populates DHCP_OPTIONS from DB/CFG and send
DHCP_ACK
Dragonflow Distributed DHCP
VM DHCP SERVER
1
3
4
6
7
Compute Node
Dragonflow
VM
OVS
VM
1 2
br-int
qvoXXX qvoXXX
OpenFlow
1
4
2
5
7
Dragonflow Controller
Abstraction Layer
L2
App
L3
App
DHCP
App
SG
36
Pluggable DB
Layer
DB
9. Dragonflow Distributed DHCP
Match:
Broadcast +UDP +S_Port=68 +D_Port=67
Action:
Send to DHCP table
Service Table
DHCP Table
Match: in_port => Action:
Set metadata with port unique key
SEND TO CONTROLLER
(for every local port that its network has DHCP
enabled)
Default:
goto “L2 Lookup Table”
Compute Node
VM
OVS
br-int
qvoXXX
VM
qvoXXX
1 2
Dragonflow
Dragonflow Local Controller
Abstraction Layer
L2
App
L3
App
DHCP
App
SG
DB
OpenFlow
Ingress Port Security
Ingress Classification
Dispatch to Ports
11. Database Framework
Requirements
• HA + Scalability
• Different Environments have different requirements
• Performance, Latency, Scalability, etc.
Why Pluggable?
• Long time to productize
• Mature Open Source alternatives
• Allow us to focus on the networking services only
12. DB Driver API
Implementations
RAMCloud
ETCD
RethinkDB
Zookeeper
Dragonflow Pluggable Database
Compute NodeCompute NodeCompute Node
Dragonflow
Local
Controller
Pluggable
DB Layer
Applicative
DB Layer
Adapter
DB
Driver
API
Expose DB
Features
Neutron Server
Dragonflow
Neutron
Plugin
DB Operations
Database
Server
DB Adapter
DB Adapter
DB Adapter
13. Distributed
Database
DB Data 3
DB Data 2
DB Data 1
Full Distribution
Compute Node 1
Dragonflow
Local Cache
OVS
DB Data 1
Compute Node N
Dragonflow
OVS
Local Cache
DB Data 3
DB Data 2
Dragonflow DB Drivers
OVSDB ETCD RethinkDBRMC
18. DragonFlow Pipeline
Installed in every OVS
Service
Traffic
Classification
Ingress Processing
(NAT, BUM)
ARP DHCP
L2
Lookup
L3
Lookup
DVR
Egress
Dispatching outgoing
traffic to external
nodes or local ports
Ingress
Port
Security
(ARP spoofing , SG, …)
Egress
Port
Security
Egress
Processing
(NAT)
Fully Proactive
Has Reactive Flows to Controller
Security Groups
…
Outgoing from local
port Classification and
tagging
Dispatching Incoming
traffic from external
nodes to local ports
23. Join the project Dragonflow
• Documentation
https://wiki.openstack.org/wiki/Dragonflow
• Bugs & blueprints
https://launchpad.net/dragonflow
• DF IRC channel
#openstack-dragonflow
Weekly on Monday at 0900 UTC in #openstack-meeting-4 (IRC)
Editor's Notes
Why is this a good thing?
Common Applicative DB Adapter Layer
Same layer is used by all clients
Dragonflow Neutron plugin
Dragonflow local controller
External/Internal applications
Expressed in terms of the schema model
Converts model to “Key / Value”
Calls the DB Driver API for DB Operations
Leverage DB advance features
Knows to receive and wait for DB changes
According to a pre defined generic API with the driver
Selective publish-subscribe
Each local controller sync only relevant data according to its local ports
Depends on the virtual topology
Local controller gets all local ports information
DB framework must support waiting for changes on specific entry column values
The plugin tags the related objects with a special column value
Reduce the sync load and change rate
Each local controller only gets the subset of the data that is relevant for it
Each local controller sync only relevant data according to its local ports
Depends on the virtual topology
Local controller gets all local ports information
DB framework must support waiting for changes on specific entry column values
The plugin tags the related objects with a special column value
Reduce the sync load and change rate
Each local controller only gets the subset of the data that is relevant for it