Download this presentation and view in Microsoft powerpoint. Animation effects make it difficult to understand on Slideshare.
REFERENCE:
R. Wang, D. Butnariu, and J. Rexford, “OpenFlow-based server load balancing gonewild,” In Hot-ICE, 2011.
Tech Talk: ONOS- A Distributed SDN Network Operating Systemnvirters
This event takes us to the cusp of Distributed Software Development and SDN Controllers. We will be hosting Madan and Brian who have been involved in the architecture and development of ONOS (Open Network Operating System).
Synopsis
ONOS is a distributed SDN network operating system architected to provide performance, scale-out, resiliency, and well-defined northbound and southbound abstractions. Madan and Brian, both from ON.Lab, will start the talk with a deep-dive into ONOS architecture, including the key technical challenges that were solved to build this platform. They will also walk us through a live demo of building a SDN application on ONOS.
Details:
ONOS Architecture
ONOS Abstractions and Modularity
ONOS Distributed architecture
ONOS APIs and their usage
Live demo- Building a SDN app on ONOS
Speaker Bios
Madan Jampani, Distributed Systems Architect, ONOS
Madan is Distributed Systems Architect at ON.Lab focusing on the core distributed systems problems for ONOS. Prior to joining ON.Lab in Sep 2014, Madan worked at Amazon for around 10 years. At Amazon, Madan was instrumental in building several key technologies ranging from Amazon retail ordering systems, distributed data stores and shared compute clusters for running large-scale data processing and machine learning workloads.
Brian O’Connor, Lead Developer, ONOS
Brian is the ONOS Application Intent Framework lead and a core developer at ON.Lab, working on ONOS and Mininet. Brian O’Connor received Bachelor’s and Master’s degrees in Computer Science from Stanford University. At Stanford, he helped develop “An Introduction to Computer Networking,” one of Stanford’s first MOOCs (Massively Open Online Courses).
ABOUT ON.LAB and ONOS
Open Networking Lab (ON.Lab) is a non-profit organization founded by SDN inventors and leaders from Stanford University and UC Berkeley to foster an open source community for developing tools and platforms to realize the full potential of SDN. ON.Lab brings innovative ideas from leading edge research and delivers high quality open source platforms on which members of its ecosystem and the industry can build real products and solutions.
ONOS, a SDN network operating system for service provider and mission critical networks, was open sourced on Dec 5th, 2014. ONOS delivers a highly available, scalable SDN control plane featuring northbound and southbound abstractions and interfaces for a diversity of management, control, service applications and network devices. ONOS ecosystem comprises of ON.Lab, organizations who are funding and contributing to the ONOS initiative including AT&T, NTT Communications, SK Telecom, Ciena, Cisco, Ericsson, Fujitsu, Huawei, Intel, NEC; members who are collaborating and contributing to ONOS include ONF, Infoblox, SRI, Internet2, Happiest Minds, CNIT, Black Duck, Create-Net and the broader ONOS community. Learn how you can get involved with ONOS at onosproject.org.
The document is a scientific composition about the
system architecture of OpenFlow and describes their principles of data handling, the types of messages and operations on the network.
Integrating Active Networking and Commercial-Grade Routing PlatformsTal Lavian Ph.D.
Implement flow performance enhancement mechanisms without introducing software into data forwarding path
Service defined packet processing in a silicon-based forwarding engine
Policy-based Dynamic packet classifier
Create OPEN platform for introduction of new services
Specify OPEN interfaces for Java applications to control a generic, platform-neutral forwarding plane
Enable downloading of services to network node
Allow object sharing and inter-service communication
Pushing Packets - How do the ML2 Mechanism Drivers Stack UpJames Denton
Architecting a private cloud to meet the use cases of its users can be a daunting task. How do you determine which of the many L2/L3 Neutron plugins and drivers to implement? Does network performance outweigh reliability? Are overlay networks just as performant as VLAN networks? The answers to these questions will drive the appropriate technology choice.
In this presentation, we will look at many of the common drivers built around the ML2 framework, including LinuxBridge, OVS, OVS+DPDK, SR-IOV, and more, and will provide performance data to help drive decisions around selecting a technology that's right for the situation. We will discuss our experience with some of these technologies, and the pros and cons of one technology over another in a production environment.
DevOops - Lessons Learned from an OpenStack Network ArchitectJames Denton
Join as we discuss various OpenStack Neutron network configuration options and issues experienced with VLAN, VXLAN, L2population, multicast, Neutron routers, Open vSwitch and more.
This is an overview of OpenFlow Networking. Derived from a talk presented at the Open Networking Summit, it talks about the motivations for OpenFlow, the details of the protocol, and the current state of hardware and software.
Pyretic - A new programmer friendly language for SDNnvirters
Managing a network requires support for multiple concurrent tasks, from routing and traffic monitoring, to access control and server load balancing. Software-Defined Networking (SDN) allows applications to realize these tasks directly, by installing packet-processing rules on switches. However, today's SDN platforms provide limited support for creating modular applications.
Join Bay Area Network Virtualization as Dr. Joshua Reich, Postdoctoral Research Scientist and Computing Innovation Fellow at Princeton University presents Pyretic - a new programmer-friendly domain-specific language embedded in Python that enables modular programming for SDN applications. Pyretic is part of the Frenetic Network Programming Language initiative sponsored by Princeton University and Cornell University, with support from the National Science Foundation, the Office of Naval Research, Google, Intel and Dell.
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.
Tech Talk: ONOS- A Distributed SDN Network Operating Systemnvirters
This event takes us to the cusp of Distributed Software Development and SDN Controllers. We will be hosting Madan and Brian who have been involved in the architecture and development of ONOS (Open Network Operating System).
Synopsis
ONOS is a distributed SDN network operating system architected to provide performance, scale-out, resiliency, and well-defined northbound and southbound abstractions. Madan and Brian, both from ON.Lab, will start the talk with a deep-dive into ONOS architecture, including the key technical challenges that were solved to build this platform. They will also walk us through a live demo of building a SDN application on ONOS.
Details:
ONOS Architecture
ONOS Abstractions and Modularity
ONOS Distributed architecture
ONOS APIs and their usage
Live demo- Building a SDN app on ONOS
Speaker Bios
Madan Jampani, Distributed Systems Architect, ONOS
Madan is Distributed Systems Architect at ON.Lab focusing on the core distributed systems problems for ONOS. Prior to joining ON.Lab in Sep 2014, Madan worked at Amazon for around 10 years. At Amazon, Madan was instrumental in building several key technologies ranging from Amazon retail ordering systems, distributed data stores and shared compute clusters for running large-scale data processing and machine learning workloads.
Brian O’Connor, Lead Developer, ONOS
Brian is the ONOS Application Intent Framework lead and a core developer at ON.Lab, working on ONOS and Mininet. Brian O’Connor received Bachelor’s and Master’s degrees in Computer Science from Stanford University. At Stanford, he helped develop “An Introduction to Computer Networking,” one of Stanford’s first MOOCs (Massively Open Online Courses).
ABOUT ON.LAB and ONOS
Open Networking Lab (ON.Lab) is a non-profit organization founded by SDN inventors and leaders from Stanford University and UC Berkeley to foster an open source community for developing tools and platforms to realize the full potential of SDN. ON.Lab brings innovative ideas from leading edge research and delivers high quality open source platforms on which members of its ecosystem and the industry can build real products and solutions.
ONOS, a SDN network operating system for service provider and mission critical networks, was open sourced on Dec 5th, 2014. ONOS delivers a highly available, scalable SDN control plane featuring northbound and southbound abstractions and interfaces for a diversity of management, control, service applications and network devices. ONOS ecosystem comprises of ON.Lab, organizations who are funding and contributing to the ONOS initiative including AT&T, NTT Communications, SK Telecom, Ciena, Cisco, Ericsson, Fujitsu, Huawei, Intel, NEC; members who are collaborating and contributing to ONOS include ONF, Infoblox, SRI, Internet2, Happiest Minds, CNIT, Black Duck, Create-Net and the broader ONOS community. Learn how you can get involved with ONOS at onosproject.org.
The document is a scientific composition about the
system architecture of OpenFlow and describes their principles of data handling, the types of messages and operations on the network.
Integrating Active Networking and Commercial-Grade Routing PlatformsTal Lavian Ph.D.
Implement flow performance enhancement mechanisms without introducing software into data forwarding path
Service defined packet processing in a silicon-based forwarding engine
Policy-based Dynamic packet classifier
Create OPEN platform for introduction of new services
Specify OPEN interfaces for Java applications to control a generic, platform-neutral forwarding plane
Enable downloading of services to network node
Allow object sharing and inter-service communication
Pushing Packets - How do the ML2 Mechanism Drivers Stack UpJames Denton
Architecting a private cloud to meet the use cases of its users can be a daunting task. How do you determine which of the many L2/L3 Neutron plugins and drivers to implement? Does network performance outweigh reliability? Are overlay networks just as performant as VLAN networks? The answers to these questions will drive the appropriate technology choice.
In this presentation, we will look at many of the common drivers built around the ML2 framework, including LinuxBridge, OVS, OVS+DPDK, SR-IOV, and more, and will provide performance data to help drive decisions around selecting a technology that's right for the situation. We will discuss our experience with some of these technologies, and the pros and cons of one technology over another in a production environment.
DevOops - Lessons Learned from an OpenStack Network ArchitectJames Denton
Join as we discuss various OpenStack Neutron network configuration options and issues experienced with VLAN, VXLAN, L2population, multicast, Neutron routers, Open vSwitch and more.
This is an overview of OpenFlow Networking. Derived from a talk presented at the Open Networking Summit, it talks about the motivations for OpenFlow, the details of the protocol, and the current state of hardware and software.
Pyretic - A new programmer friendly language for SDNnvirters
Managing a network requires support for multiple concurrent tasks, from routing and traffic monitoring, to access control and server load balancing. Software-Defined Networking (SDN) allows applications to realize these tasks directly, by installing packet-processing rules on switches. However, today's SDN platforms provide limited support for creating modular applications.
Join Bay Area Network Virtualization as Dr. Joshua Reich, Postdoctoral Research Scientist and Computing Innovation Fellow at Princeton University presents Pyretic - a new programmer-friendly domain-specific language embedded in Python that enables modular programming for SDN applications. Pyretic is part of the Frenetic Network Programming Language initiative sponsored by Princeton University and Cornell University, with support from the National Science Foundation, the Office of Naval Research, Google, Intel and Dell.
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.
Xin Jin
Princeton University
Research Track Part 1
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
Analysis and Design for Intrusion Detection System Based on Data MiningPritesh Ranjan
Reference:
Dyuanyang Zhao, Zhilin Feng, Qingxiang Xu, “Analysis and design for Intrusion detection system based on data mining” in proceedings of 2010 IEEE second international workshop on education technology and computer science
Presentation detailed about SDN (Software Defined Network) overview . It covers from basics like different controllers and touches upon some technical details.
Covers Terminologies used, OpenFlow, Controllers, Open Day light, Cisco ONE, Google B4, NFV,etc
SDN 101: Software Defined Networking Course - Sameh Zaghloul/IBM - 2014SAMeh Zaghloul
Sameh Zaghloul
Technology Manager @ IBM
+2 0100 6066012
zaghloul@eg.ibm.com
SDN: Technology that enables data center team to use software to efficiently control network resources
SDN Overview
SDN Standards
NFV – Network Function Virtualization
SDN Scenarios and Use Cases
SDN Sample Research Projects
SDN Technology Survey
SDN Case Study
SDN Online Courses
SDN Lab SW Tools
- OpenStack Framework
- OpenDayLighyt – SDN Controller
- FloodLight – SDN Controller
- Open vSwitch – Virtual Switch
- MiniNet – Virtual Network: OpenFlow Switches, SDN Controllers, and Servers/Hosts
- OMNet++ Network Simulator
- Avior – Sample FloodLight Java Application
- netem - Network Emulation
- NOX/POX - C++/ Python OpenFlow API for building network control applications
- Pyretic = Python + Frenetic - Enables network programmers and operators to write modular network applications by providing powerful abstractions
- Resonance - Event-Driven Control for Software-Defined Networks (written in Pyretic)
SDN Project
Oracle Drivers configuration for High Availability, is it a developer's job?Ludovico Caldara
UCP, GridLink, TAF, AC, TAC, FAN… The configuration of Oracle Drivers for application high availability is not an easy job. The developers often care about the minimal working configuration, while the DBAs are busy with the operations. In this session I will try to demystify application server’s connectivity to the database and give a direction toward the highest availability, using Real Application Clusters and new Oracle features like TAC and CMAN TDM.
Many applications are network I/O bound, including common database-based applications and service-based architectures. But operating systems and applications are often untuned to deliver high performance. This session uncovers hidden issues that lead to low network performance, and shows you how to overcome them to obtain the best network performance possible.
Many applications are network I/O bound, including common database-based applications and service-based architectures. But operating systems and applications are often untuned to deliver high performance. This session uncovers hidden issues that lead to low network performance, and shows you how to overcome them to obtain the best network performance possible.
Detailed presentation on how queries and updates behave on updateable secondary. A few nuggets of best practices to make sure your HDR configuration works well.
Kubernetes currently has two load balancing mode: userspace and IPTables. They both have limitation on scalability and performance. We introduced IPVS as third kube-proxy mode which scales kubernetes load balancer to support 50,000 services. Beyond that, control plane needs to be optimized in order to deploy 50,000 services. We will introduce alternative solutions and our prototypes with detailed performance data.
Linux Kernel vs DPDK: HTTP Performance ShowdownScyllaDB
In this session I will use a simple HTTP benchmark to compare the performance of the Linux kernel networking stack with userspace networking powered by DPDK (kernel-bypass).
It is said that kernel-bypass technologies avoid the kernel because it is "slow", but in reality, a lot of the performance advantages that they bring just come from enforcing certain constraints.
As it turns out, many of these constraints can be enforced without bypassing the kernel. If the system is tuned just right, one can achieve performance that approaches kernel-bypass speeds, while still benefiting from the kernel's battle-tested compatibility, and rich ecosystem of tools.
Tungsten Connector / Proxy is truly the secret sauce for the Tungsten Clustering solution. Watch this webinar to learn how the Tungsten Connector enables zero-downtime MySQL maintenance via the manual switch operation, and gain an understanding of the various configuration options for doing local reads in remote composite clusters.
AGENDA
- Review the cluster architecture
- Understand the role of the Connector
- Describe Connector deployment best practices (app, dedicated with lb, db with lb)
- Explore zero-downtime MySQL maintenance using the manual role switch procedure
- Learn about Connector routing patterns inside a composite cluster
- Illustrate a manual site switch
- Explain read affinity and the vast performance improvement of local reads
- Examine Connector multi-cluster support
Kube-proxy enables access to Kubernetes services (virtual IPs backed by pods) by configuring client-side load-balancing on nodes. The first implementation relied on a userspace proxy which was not very performant. The second implementation used iptables and is still the one used in most Kubernetes clusters. Recently, the community introduced an alternative based on IPVS. This talk will start with a description of the different modes and how they work. It will then focus on the IPVS implementation, the improvements it brings, the issues we encountered and how we fixed them as well as the remaining challenges and how they could be addressed. Finally, the talk will present alternative solutions based on eBPF such as Cilium.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Software Load Balancer for OpenFlow Complaint SDN architecture
1. Enhancing Load Balancer
For OpenFlow Compliant
SDN Architecture
MIT COLLEGE OF ENGINEERING
BY-
Pritesh Ranjan
Pankaj Pande
Ramesh Oswal
Zainab Qurani
3. Packet Forwarding
Hardware
Ap
p
Ap
p
Ap
p
Packet Forwarding
Hardware
Ap
p
Ap
p
Ap
p
Hardware Packet
Forwarding
Ap
p
Ap
p
Ap
p
Packet Forwarding
Hardware
Operating
System
Operating
System
Operating
System
Operating
System
Ap
p
Ap
p
Ap
p
Network Operating System
App App App
INTRODUCTION TO SDN
1. Open Interface to HW (South Bound API)
3. Open API for
business Applications
(NorthBound API)
2. Operating System
(controller Platforms)
8. Coming
Up Next
Environment Setup
Setup a network with 10 hosts on 1 switch
Time Required ??
Setup a network with 100 hosts on 5 switches.
Time Required ??
Tired / Bored ??
Solution : “Mininet”
9. Switch
10.0.0.10 Port 2 Low
Source Subnet Forward to Priority
Rule Table
Default
Controller
h1 h2
Srcip=10.0.0.10 Srcip=10.0.0.20
s1
Mininet : Using Inbuilt Wrapper “mn”
12. Controller Design
To redirect traffic destined for “Service IP” to one of the
backend replica servers A/c to assigned weighted load.
Network Design Decisions
Distributed or Centralized ?
Goal of the Application
Flow Based or Aggregated?
Reactive or Proactive?
Centralized
Both – Microflow and wildcard rules
Proactive
13. Match
(exact & wildcard)
Action Statistics
Match
(exact & wildcard)
Action Statistics
Match
(exact & wildcard)
Action Statistics
Match
(exact & wildcard)
Action Statistics
---------------
Srcip=10.0.23.23) Output port = 2 No. of Packets=10
Srcip=10.0.0.0/10
Priority=Low
Output port = 4 No. of bytes
Srcip=10.0.0.0/10
Priority=High
Send to controller No of received packets
Microflow
rules
Wildcard
rules
Rules/Flow Entries
14. Controller
Switch
Source Forward to Priority
Rule Table
A R4 Medium
B R2 High
R1
R2
R3
R4
Load Balancer
Reactive Approach
Drawback:
High Setup time
15. Controller
Switch
Source Subnet Forward to Priority
Rule Table
10.0.0.0/11 R4 Medium
10.32.0.0/11 R2 High
R1
R2
R3
R4
Load Balancer
10.64.0.0/11 R1 Low
10.224.0.0/11 R4 Medium
Configuring switch
Table Generated
Proactive Approach
Drawback:
Wildcard rules are
expensive
16. Implementation Details
AIM:
Reduce initial setup time
Servers get load in proportion to the assigned weights
Minimum number of wildcard rules
APPROACH:
Proactively install wildcard rules to smaller sub-subnets
Assign each server some subnets according to weighted load
Minimization technique
Coming Up Next
Partitioning Algorithm
17. Partitioning Algorithm
Deciding the no of subnets:
Server R1
Alpha = 2
Server R2
Alpha = 3
Server R3
Alpha = 1
Total alpha = 2 + 3 + 1 = 6
Nearest 2n = 8
Normalization Factor = 8/6 = 1.333
Weighted
Load = 3
Weighted
Load = 4
Weighted
Load = 1
Weighted Load:
R1 = 1.333 * 2 = 2.666 = 3 R2 = 1.333 * 3 = 3.999 = 4 R3 = 1.333 * 1 = 1.333 = 1
No of subnet = 3 + 4 + 1 = 8
Partition The subnet into 8
subgroups
18. 10.0.0.0/8
10.128.0.0/910.0.0.0/9
10.0.0.0/10 10.64.0.0/10 10.128.0.0/10 10.192.0.0/10
10.0.0.0/11 10.32.0.0/11
10.64.0.0/11 10.96.0.0/11
10.128.0.0/11 10.160.0.0/11
10.192.0.0/11 10.224.0.0/11
Server R1
Weighted Load
= 3
Server R3
WL = 1
Server R2
Weighted Load = 4
Company Network
Partitioning Algorithm
23. Load Shift Operation
Situation:
Goal:
Conditions:
Solution:
Server R1 needs to be taken down for maintenance.
Traffic of R1 (old) should be allocated to R2 (New)
Ongoing connections should be continued with old server(R1)
New connections should be forwarded to new server(R2)
R1 can be taken down only when all the connections have expired.
Transitioning
Algorithm
24. Subnet A
Subnet B
R1(Old)
R2(New)
Server R1 is to be taken
down, Shift its load
To R2
Ok, let me check
the connections
for SYN
Rule Table
Source Subnet Forward to Priority
A R1 Low
B R2 Low
Transitioning Algorithm
25. Subnet A
Subnet B
R1(Old)
R2(New)
Rule Table
Source Subnet Forward to Priority
A R1 Low
B R2 Low
1. Adds new flow entry
2. Modify Old Flow Entry
R2
A Controller High
Rule Table
Source Subnet Forward to Priority
A R2 Low
B R2 Low
A Controller High
Transitioning Algorithm
26. IP=10.0.0.1
Subnet A
Subnet B
R1(Old)
R2(New)
Add micro flow rule
Rule Table
Source Subnet Forward to Priority
A R2 Low
B R2 Low
A Controller High
SYN flag NOT SET
10.0.0.1 R1 Highest
Transitioning Algorithm
27. IP=10.0.0.1
Subnet A
R1(Old)
R2(New)
Add micro flow rule
Rule Table
Source Subnet Forward to Priority
A R2 Low
B R2 Low
A Controller High
SYN flag SET
10.100.0.1 R2 Highest
IP=10.100.0.1
Subnet A
Transitioning Algorithm
28. IP=10.0.0.1
Subnet A
R1(Old)
R2(New)
Flow Entries get deleted after
Idle time-out
Rule Table
Source Subnet Forward to Priority
A R2 Low
B R2 Low
A Controller High
10.100.0.1 R2 Highest
IP=10.100.0.1
Subnet A
Now R1 can be taken down
Transitioning Algorithm
29. R1 (X=2)
R2 (X=2)
2*x
2*x
00*
01*
10*
11*
x
x
x
x
00* R1
01* R1
10* R2
11* R2
Uniform Client traffic pattern
Each subnet has same
no of Connections Each server gets proportional no
of Connections (weighted Load)
31. R1 (X=2)
R2(X=2)
3*x
1*x
00*
01*
10*
11*
2*x
x
x
0*x
00* R1
Overloaded
Server
Underloaded
Server
Read
Statistics
Find over and
underloaded server
Shift appropriate load from
over to under loaded server
01* R1
10* R2
11* R2
01* R2
2*x
2*x
Load Redistribution Algorithm
32. Project Demo: Videos
Topology Creation
Partitioning Algorithm- Video 1
Transitioning Algorithm
Load Redistribution Algorithm
Partitioning Algorithm- Video 2
43. Scapy
• Scapy is a Python framework for crafting and
transmitting arbitrary packets
• Scapy also performs very well on a lot of other
specific tasks that most other tools can’t handle,
like sending invalid frames, injecting your own
802.11 frames