OpenCV: Haartraining and all that jazz

Object detection in OpenCV can be done through Haartraining. OpenCV haartraining applications provide the ability to detect objects of interest to us like faces, eyes, moving cars etc. What is required is that we need to identify positive and negative samples and use the utilities that OpenCV provides us to train the application to be able to recognize the objects we want. The positive and negative samples are used to create classifiers which are then utilized to detect the objects that we intend to.

Fortunately the tar file (OpenCV-2.3.1a.tar.bz2) which can be downloaded from the OpenCV site already comes bundled with all the necessary utilities to create a fully trained haar classifier. We just to have to build and run the commands to create our our classifiers.

This post will look at the necessary steps for creating a haarclassifier. To get started with OpenCV please look at my earlier post Computer Vision:Getting started with OpenCV.

Once you have installed OpenCV look under modules/haartraining. All the necessary files are included.

There are 3 steps in this process
1) Create samples 2) Train and create a classified haar 3) Performance testing

Create Samples (createsamples.cpp) : This is the first utility that has to be executed. This utility takes as input positive samples (images of the object that we are interested in) and negative samples (images that do not include the object that we are interested in). The createsamples utility superimposes the objects that we want to recognize in various degrees of rotation against the background of the negative samples. The composite images file is then used to train the haar application.

The first step is to build createsamples.cpp. Make sure you include ../OpenCV-2.3./modules/haartraining. The build should include the following files
(createsamples.cpp, cvboost.cpp, cvhaarclassifier.cpp, cvhaartarining.cpp, cvsamples.cpp, cvcommon.cpp)

Once createsamples.cpp successfully builds we can create samples required for the training.
The command is
$./myhaartraining -img logo.png -vec samples1.jpg -bg bg.txt -w 20 -h 20

Where I have chosen the OpenCV logo as my object of interest.

The samples are created in samples1.jpg
The bg.txt contains a list of the negative samples included as below

./img/airplane.jpg
./img/baboon.jpg
./img/kid.jpg
./img/lena.jpg
-w stands for the width and -h stands for the height of the samples.


This will create superimposed positive samples in the negative sample background. A few of these a are shown below.

Make sure that the width and height are small i.e < 50 otherwise the haartraining application core dumps because of lack of memory. To check if the samples are created properly run a test round as follows run the command with the following options $./myhaartraining -img logo.png -vec samples1.jpg -bg bg.txt -n 10 - show -w 20 -h 20 where -n is the number of samples to be generated (default is 1000) -show will show a series of images with the positive samples superimposed on the negative samples. This can be used to check if createsamples utility is working properly. For a more thorough and detailed explanation see my post  Hand detection through haartraining: A Hands on approach

Haartrainer (haartraining.cpp): This utility takes as input the samples from the createsamples utility and creates a trained haar classifier. To build the haartrained files, build the haartraining.cpp. As before make sure you include the appropriate files along with the all the opencv libraries. The build files are
(haartraining.cpp, cvboost.cpp, cvhaarclassifier.cpp, cvhaartarining.cpp, cvsamples.cpp, cvcommon.cpp)

The command to use is
./haartrainer -data test2 -vec samples1.jpg -bg bg.txt -npos 1 -nneg 4 -nstages 20 -mem 500 -w 20 -h 20

In the above command test2 is the directory name in which the trained classifier is stored. The -vec option denotes the samples that were captured by the createsamples utility above. The bg.txt contains the negative samples file. The width and height have to be the same as used in the create samples utility. As mentioned before if the width and height are too large you the haartrainer will bail out with “Insufficient memory”

If the haartrainer executes successfully the test2 directory will have all the trained files and the directory will also have the haarclassifier as a test2.xml.

Once these two steps go through successfully we have to run the performance step

Performance (performance.cpp: This step is run to ensure that we have trained our application to properly recognize the object we intended to. As before the necessary file to build is performance.cpp. The files to include in the build are
(performance.cpp, cvboost.cpp, cvhaarclassifier.cpp, cvhaartarining.cpp, cvsamples.cpp, cvcommon.cpp)

Once this built successfuly you can now run using the command

./performance -data test2.xml -info bg.txt -w 20 -h 20

Make sure that the bg.txt contains the images from which the object has to be detected in the following
format
[positive filename] [# of objects] [[x y width height]

bg.txt
/img/logo5.jpg 1 145 100 20 20
./img/logo6.jpg 1 145 100 20 20
./img/logo3.jpg 1 145 100 45 45
./img/logo4.jpg 1 145 100 45 45
./img/airplane.jpg 1 145 100 45 45
./img/baboon.jpg 1 145 100 45 45
./img/kid.jpg 1 145 100 45 45
./img/lena.jpg 1 145 100 45 45
./img/opencv-logo2.png 1 145 100 35 35
./img/logo.png 1 145 100 45 45

When this is run we get


ganesh@localhost Debug]$ ./performance -data test3.xml -info bg.txt -w 20 -h 20
+================================+======+======+======+
| File Name | Hits |Missed| False|
+================================+======+======+======+
| ./img/logo5.jpg| 0| 1| 43|
+--------------------------------+------+------+------+
| ./img/logo6.jpg| 0| 1| 51|
+--------------------------------+------+------+------+
| ./img/logo3.jpg| 1| 0| 37|
+--------------------------------+------+------+------+
| ./img/logo4.jpg| 0| 1| 7|
+--------------------------------+------+------+------+
| ./img/airplane.jpg| 1| 0| 226|
+--------------------------------+------+------+------+
| ./img/baboon.jpg| 0| 1| 236|
+--------------------------------+------+------+------+
| ./img/kid.jpg| 0| 1| 1291|
+--------------------------------+------+------+------+
| ./img/lena.jpg| 0| 1| 188|
+--------------------------------+------+------+------+
| ./img/opencv-logo2.png| 0| 1| 3|
+--------------------------------+------+------+------+
| ./img/logo.png| 0| 1| 33|
+--------------------------------+------+------+------+
| Total| 2| 8| 2115|
+================================+======+======+======+

As can be seen the training has been too accurate. There are hits and misses along with a false
positive.

The number of positive and negative samples, the co-ordinates and the number of stages
all have to fine tuned to get the correct result.

Watch this space!

I will be back! Hasta la vista!

Please do take a look at my sequel to this post Hand detection through Haartraining: A Hands-on approach for a more robust haartraining method

INWARDi Technologies

Adding the OpenFlow variable in the IMS equation

IMS a non-starter: IP Multimedia Systems (IMS) has been the grand vision of this decade. Unfortunately it has remained just that, a vision, with sporadic deployments. IMS has been a non-starter in many respects. Operators and Network Providers somehow don’t find any compelling reason to re-architect the network with IMS network elements. There have been no killer applications too. But IP Multimedia Systems definitely hold enormous potential and a couple of breakthroughs in key technologies can result in the ‘tipping point’ of this great technology which promises access agnostic services including applications like video-conferencing, multi-player gaming, white boarding all using an all-IP backbone. In this context please do read my post "The Case for a Cloud based IMS solution"

SDNs, truly revolutionary: In this scenario a radically new, emerging concept is the Software Define Networks (SDNs). SDN is the result of pioneering effort by Stanford University and University of California, Berkeley and is based on the Open Flow Protocol and represents a paradigm shift to the way networking elements operate. SDNs consist of the OpenFlow Controller which is able to control network resources in a programmatic manner. These network resources can span routers, hubs and switches, known as a slice, and can be controlled through the OpenFlow Controller. The key aspect of the OpenFlow protocol is the ability to manage slices of virtualized resources from end-to-end. It is this particular aspect of Software Defined Networks (SDNs) and the OpenFlow protocol which can be leveraged in IMS networks.Do read my post Software Defined Networks: A glimpse of tomorrow for more details

This article looks at a way in which OpenFlow protocol can be included in the IMS fabric and provide for QoS in the IMS network. However, please note that this post is exploratory in nature and does not purport to be a well researched article. Nevertheless the idea is well worth mulling over.

QoS in IMS: The current method of ensuring differentiated QoS in IMS networks is through two key network elements, namely the Policy Decision Point (PDP) and the Policy Enforcement Point (PEP). The PDP retrieves the necessary policy rules (flow parameters), in response to a RSVP message, which it then sends to the PEP. The PEP then executes these instructions. In the IMS the Policy Control Function (PCF), in the P-CSCF, plays the role of the PDP. The PEP resides in the GGSN. In an IMS call flow, the SDP message is encapsulated within SIP and carries the QoS parameters. The PCF examines the parameters, retrieves appropriate policies and informs the PEP in the GGSN for that traffic flow.

OpenFlow Controller in P-CSCF: This post looks at a technique in which the OpenFlow Controller can be a part of the P-CSCF. The QoS parameters which come in the SDP message can be similarly examined. Instead of retrieving fixed policy rules, the OpenFlow Controller in the P-CSCF can be made to programmatically identify bandwidth speeds, the routers and the network slice through which the flow should flow. It would then inform the equivalent of the OpenFlow Switch in the GGSN which would control the necessary network resources end-to-end. The advantage of using OpenFlow Controller/OpenFlow switch to the PDP/PEP combination would be the ability to adapt the network flow according to bandwidth changes and traffic. The OpenFlow Controller will be able to dynamically re-route the traffic to alternate network resources, or a different ‘network slice’ in cases of congestion.

Conclusion: In my opinion, adding the OpenFlow Protocol to the IP Multimedia Switching fabric can provide for a much more control and better QoS in the network. It may also be able to provide for a lot more interesting applications to the already existing set of powerful applications

Assistive Technology and OpenCV

Assistive technology (AT) or Adaptive technology refers to technology or devices that assist people with disabilities. AT provides for a greater degree of freedom and independence by enabling disabled people to perform tasks that they were formerly unable to perform.
Examples of assistive technology include large computer keyboards for visually impaired, Braille buttons in elevators, hearing aids etc. People with learning abilities, for e.g dyslexics find text –to –speech (TTS) technology is useful.

In the context of Assistive technology, OpenCV can be used in multiple ways. OpenCV (Open Source Computer Vision) is a set of powerful APIs that can perform real time computer image processing. OpenCV is being used by many organizations in complex application like biometrics for recognizing fingerprints, face, to medical imaging for detection of tumors and cancerous cells. Applications of OpenCV have also been developed in office security for the detection of intruders to terrain mapping by spy planes and drones. OpenCV is truly a powerful tool for performing complex image processing operation.

One such application of OpenCV is the area of gesture detection and recognition. A successful implementation of gesture detection and recognition can have significant implications. It can be used to interpret sign language, the language of the deaf, and those with motor and speech disabilities.

Clearly the ability to recognize gestures is no easy task. The software has to be trained to initially recognize different gestures of sign language. An image processing tool that can recognize the symbols of sign language will be a boon to those with severe motor disabilities that they can use the keyboard for typing.

Imagine somebody who has motor disabilities being able to use the PC for browsing, accessing social networks all through sign language. Such a tool will really allow such people to live life in a much more regular way. Assuming the software is powerful enough the disable person will also be able to create documents through sign language.

If the power of OpenCV can be harnessed for gesture detection and interpretation the applications can be manifold. One such application would be sign language which would be a boon for many disabled people.

Added as an afterthought
While the post is about using OpenCV for recognition of sign language to empower disabled people, I think gesture recognition would be really welcome for everybody. Imagine being able to browse websites using gestures, scrolling up/down, going back/forward and pointing and clicking on links by simply waving the hand appropriately. If we could use gestures to flip through TV channels by snapping our fingers or waving left to right or right to left, that would be really cool. The possibilities are endless...

Do read my futuristic short story – The Anomaly for an interesting application of the above technology

INWARDi Technologies

Computer Vision: Getting started with OpenCV

OpenCV (Open Source Computer Vision) is a library of APIs aimed at enabling real time computer vision. OpenCV had Intel as the champion during its early developmental stages from 1999 to its first formal release in 2006. This post looks at the steps involved in installing OpenCV on your Linux machine and getting started with some simple programs. For the steps for installing OpenCV in Windows look at my post "Installing and using OpenCV with Visual Studio 2010 express"

As a first step download the tarball OpenCV-2.3.1a.tar.bz2 from the link below to directory
$HOME/opencv
http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/2.3.1/

Unzip and and untar the bzipped file using

$ tar jxvf OpenCV-2.3.1a.tar.bz2
Now
$ cd OpenCV-2.3.1

You have to run cmake to configure the directories before running make. I personally found it
easier to run the cmake wizard using
$ cmake -i

Follow through all the prompts that the cmake wizard gives and make appropriate choices.

Once this complete

Run
$ make

Login as root

$ su – root
password: *******

Run
$ make install

This should install all the appropriate files and libraries in /usr/local/lib

Now assuming that everything is fine you should be good to go.

Start Eclipse, open a new C project.

Under Project->Properties->Settings->GCC Compiler ->Directories include the following 2 include paths

../opencv/OpenCV-2.3.1/include/opencv
../opencv/OpenCV-2.3.1/include/opencv2

Under
Project->Properties->Settings->GCC Linker ->Libraries in the library search path
include /usr/local/lib

Under libraries include the following
opencv_highgui , opencv_core , opencv_imgproc , opencv_highgui , opencv_ml , opencv_video, opencv_features2d , opencv_calib3d , opencv_objdetect , opencv_contrib , opencv_legacy , opencv_flann

(To get the list of libraries you could also run the following command)
pkg-config --libs /usr/local/lib/pkgconfig/opencv.pc
-L/usr/local/lib -lopencv_core -lopencv_imgproc -lopencv_highgui -lopencv_ml -lopencv_video -lopencv_features2d -lopencv_calib3d -lopencv_objdetect -lopencv_contrib -lopencv_legacy -lopencv_flann

Now you are ready to create your first OpenCV program

The one below will convert an image to test.png
#include "highgui.h"

int main( int argc, char** argv ) {
IplImage* img = cvLoadImage( argv[1],1);

cvSaveImage( "test.png", img, 0);
cvReleaseImage( &img );
return 0;
}

If you get a runtime error cannot find shared library
“ibopencv_core.so.2.3: cannot open shared object file: No such file or directory”
then you need to ensure that the linker knows the paths of the libraries.

The commands are as follows

$vi /etc/ld.so.conf.d/opencv.conf
Enter
/usr/local/lib
and save file

Now execute
$ldconfig /etc/ld.so.conf

You can check if everything is fine by running
$[root@localhost mycode]# ldconfig -v | grep open
ldconfig: /etc/ld.so.conf.d/kernel-2.6.32.26-175.fc12.i686.PAE.conf:6: duplicate hwcap 0 nosegneg
libopencv_gpu.so.2.3 -> libopencv_gpu.so.2.3.1
libopencv_ml.so.2.3 -> libopencv_ml.so.2.3.1
libopencv_legacy.so.2.3 -> libopencv_legacy.so.2.3.1
libopencv_objdetect.so.2.3 -> libopencv_objdetect.so.2.3.1
libopencv_video.so.2.3 -> libopencv_video.so.2.3.1
.....

Now re-build the code and everything should be fine.

Here's a second program to run various transformations to an image
IplImage* img = cvLoadImage( argv[1],1);
// create a window. Window name is determined by a supplied argument
cvNamedWindow( argv[1], CV_WINDOW_AUTOSIZE );
// Apply Gaussian smooth
//cvSmooth( img, img, CV_GAUSSIAN, 9, 9, 0, 0 );

cvErode (img,img,NULL,2);
// Display an image inside and window.
cvShowImage( argv[1], img );
//Save image
cvSaveImage( "/home/ganesh/Desktop/baby2.png", img, 0);

....

There are many samples also downloaded along with the installation. You can try them out. I found the facedetect.cpp sample interesting. It is based on Haar cacades and works really well. Compile facedetect.cpp under samples/c

Check it out. Including facedetect.cpp detecting my face in real time ...

Have fun with OpenCV.

Get going! Get hooked!

INWARDi Technologies

Technologies to watch: 2012 and beyond

Published in Telecom Asia - Technologies to watch:2012 and beyond
Published in Telecoms Europe - Hot technologies for 2012 and beyond

A keen observer of the technological firmament, today, will observe a grand spectacle of diverse technological events. Some technological trends will blaze a trail and will become trend setters while others will vanish without a trace. The factors that make certain technologies to endure in comparison to others could be many, ranging from pure necessity to a coolness factor, from innovativeness to a cost factor. This article looks at some of the technologies that are certain to be trail blazers in the years to come.

Software Defined Networks (SDNs): Software Defined Networks (SDNs) are based on the path breaking paradigm of separating the control of a network flow from the actual flow of data. SDN is the result of pioneering effort by Stanford University and University of California, Berkeley and is based on the Open Flow Protocol and represents a paradigm shift to the way networking elements operate. Software Defined Networks (SDN) decouples the routing and switching of the data flows and moves the control of the flow to a separate network element namely, the Flow controller. The motivation for this is that the flow of data packets through the network can be controlled in a programmatic manner. The OpenFlow Protocol has 3 components to it. The Flow Controller that controls the flows, the OpenFlow switch and the Flow Table and a secure connection between the Flow Controller and the OpenFlow switch. Software Define Networks (SDNs) also include the ability to virtualize the network resources. Virtualized network resources are known as a “network slice”. A slice can span several network elements including the network backbone, routers and hosts. The ability to control multiple traffic flows programmatically provides enormous flexibility and power in the hands of users. SDNs are bound to be the networks elements of the future.

Smart Grids: The energy industry is delicately poised for a complete transformation with the evolution of the smart grid concept. There is now an imminent need for an increased efficiency in power generation, transmission and distribution coupled with a reduction of energy losses. In this context many leading players in the energy industry are coming up with a connected end-to-end digital grid to smartly manage energy transmission and distribution. The digital grid will have smart meters, sensors and other devices distributed throughout the grid capable of sensing, collecting, analyzing and distributing the data to devices that can take action on them. The huge volume of collected data will be sent to intelligent device which will use the wireless 3G networks to transmit the data. Appropriate action like alternate routing and optimal energy distribution would then happen. Smart Grids are a certainty given that this technology addresses the dire need of efficient energy management. Smart Grids besides managing energy efficiently also save costs by preventing inefficiency and energy losses.

The NoSQL Paradigm: In large web applications where performance and scalability are key concerns a non –relational database like NoSQL is a better choice to the more traditional relational databases. There are several examples of such databases – the more reputed are Google’s BigTable, HBase, Amazon’s Dynamo, CouchDB & MongoDB. These databases partition the data horizontally and distribute it among many regular commodity servers. Accesses to the data are based on get(key) or set(key, value) type of APIs. Accesses to the data are based on a consistent hashing scheme for example the Distributed Hash Table (DHT) method. The ability to distribute data and the queries to one of several servers provides the key benefit of scalability. Clearly having a single database handling an enormous amount of transactions will result in performance degradation as the number of transaction increases. Applications that have to frequently access and manage petabytes of data will clearly have to move to the NoSQL paradigm of databases.

Near Field Communications (NFC): Near Field Communications (NFC) is a technology whose time has come. Mobile phones enabled with NFC technology can be used for a variety of purposes. One such purpose is integrating credit card functionality into mobile phones using NFC. Already the major players in mobile are integrating NFC into their newer versions of mobile phones including Apple’s iPhone, Google’s Android, and Nokia. We will never again have to carry in our wallets with a stack of credit cards. Our mobile phone will double up as a Visa, MasterCard, etc. NFC also allows retail stores to send promotional coupons to subscribers who are in the vicinity of the shopping mall. Posters or trailers of movies running in a theatre can be sent as multi-media clips when travelling near a movie hall. NFC also allows retail stores to send promotional coupons to subscribers who are in the vicinity of the shopping mall besides allowing exchanging contact lists with friends when they are close proximity.

The Other Suspects: Besides the above we have other usual suspects

Long Term Evolution (LTE): LTE enables is latest wireless technology that enables wireless access speeds of up to 56 Mbps. With the burgeoning interest in tablets, smartphones with the countless apps LTE will be used heavily as we move along.For a vision of where telecom is headed, do read my post 'The Future of Telecom".

Cloud Computing: Cloud Computing is the other technology that is bound to gain momentum in the years ahead. Besides obviating the need for upfront capital expenditure the cloud enables quick and easy deployment of applications. Moreover the elasticity of the cloud will make it irresistible to large enterprises and corporations.

The above is a list of technologies to watch as create new paths and blaze new trails. All these technologies are bound to transform the world as we know it and make our lives easier, better and more comfortable. These are the technologies that we need to focus on as we move bravely into our future. Do read my post for the year 2011 "Technology Trends - 2011 and beyond"

INWARDi Technologies

Tomorrow's wireless ecosystem

The wireless networks of today had its humble beginnings in 1924 when the first mobile radio was demonstrated. It was many years since this beginning, that a completely functional cellular network was established. The earliest systems were the analog 1G system that was demonstrated in 1978 in US with great success. The initial mobile systems were primarily used for making mobile voice calls. This continued for the next 2 decades as the network evolved to digital based 2G systems.

It was around 1999-2000 that ETSI standardized GPRS or 2.5G technology to use the cellular network for data. Though the early data rates, of 144 kbps, were modest, the entry of GPRS proved to be a turning point in technological history. GPRS provided the triple benefits of wireless connectivity, mobility and internet access. Technological advancement enabled faster and higher speeds of wireless, mobile access to the internet. The deployments of 3G enabled speeds of up to 2 Mbps for fixed access while LTE promised speeds of almost 56 Mbps per second coupled with excellent spectral efficiency.

The large increase of bandwidth along with mobility has allowed different technologies to take advantage of the wireless infrastructure for their purposes. While Wi-Fi networks based on 802.11 and WiMAX based on 802.16 will play a part in the wireless ecosystem this post looks at the role that will be played by cellular networks from 2G to 4G.

The cellular network with its feature of wireless access, mobility and the ability to handle voice, video and data calls will be the host of multiple disparate technologies as we move forward into the future. Below are listed some of the major users of the wireless network in the future

Mobile Phones: The cellular network was created to handle voice calls originating from mobile phones. A large part of mobile traffic will still be for mobile to mobile calls. As the penetration of the cellular networks occurs in emerging economies we can expect that there will be considerable traffic from voice calls. It is likely that as the concept of IP Multimedia System (IMS) finds widespread acceptance the mobile phone will also be used for making video calls. With the advent of the Smartphone this is a distinct possibility in the future.

Smartphones, tablets and Laptops: These devices will be the next major users of the cellular network. Smartphones, besides being able to make calls, also allow for many new compelling data applications. Exciting apps on tablets like the iPad and laptops consume a lot of bandwidth and use the GPRS, 3G or LTE network for data transfer. In fact in a recent report it has been found that a majority of data traffic in the wireless network are video. Consumers use the iPad and the laptop for watching videos on Youtube and for browsing using the wireless network.

Internet of Things (IoT): The internet of things, also known as M2M, envisages a network in which passive or intelligent devices are spread throughout the network and collect and transmit data to back end database. RFIDs were the early enablers of this technology. These sensors and intelligent devices will collect data and transmit the data using the wireless network. Applications for the Internet of Things range from devices that monitor and transmit data about the health of cardiac patients to being able to monitor the structural integrity of bridges.

Smart Grid: The energy industry is delicately poised for a complete transformation with the evolution of the smart grid concept. There is now an imminent need for an increased efficiency in power generation, transmission and distribution coupled with a reduction of energy losses. In this context many leading players in the energy industry are coming up with a connected end-to-end digital grid to smartly manage energy transmission and distribution. The digital grid will have smart meters, sensors and other devices distributed throughout the grid capable of sensing, collecting, analyzing and distributing the data to devices that can take action on them. The huge volume of collected data will be sent to intelligent device which will use the wireless 3G networks to transmit the data. Appropriate action like alternate routing and optimal energy distribution would then happen. The Smart Grid will be a major user of the cellular wireless network in the future.

Hence it can be seen the users of the wireless network will increase dramatically as we move forward into the future. Multiple technologies will compete for the available bandwidth. For handling this exponential growth in traffic we not only need faster speeds for the traffic but also sufficient spectrum available for use and it is necessary that ITU addresses the spectrum needs on a war footing.

It is thus clear that the telecom network will have to become more sophisticated and more technologically advanced as we move forward into the future.

Profiting from a cloud deployment

Cloud computing does offer enterprises and organizations a mixed bag of goodies. For one it provides for a utility style computing, the ability to grow and shrink with changing loads, zero upfront costs etc. The benefits of cloud computing are many but does it all add up to profit for an enterprise? That is the critical question that needs to be answered.

This post will take a look on what it takes for a cloud deployment to be profitable for an organization.

The critical parameters for any web application are latency and throughput. A well designed web application whether it is an e-retail site or an ad serving application will try to minimize the latency or response time while at the same time maximizing the throughput of the application. For any application while the latency can be kept within specified limits the throughput will tend to plateau at a certain level and will not increase with increasing traffic. Utilizing a larger instance can improve the throughput plateau slightly. In any case the reality is that throughput tends to flatten as the traffic is increased.

A typical cloud application will be made of several compute instances, database instances, DNS services etc. Cloud usage is billed by the hour. Hence we can represent the cost of a cloud deployment as follows

Cost (cloud deployment) = m * compute instance + n * database instance + o * network bytes + P
Where P = cost of DNS + Elastic IPs + other costs.

This can be represented by the formula
C = a * D * t
where C = cost of cloud deployment
D = costs per hour of the deployment
and ‘a’ is some arbitrary constant and ‘t’ is the time

Let us assume that for the cloud deployment we get a throughput of T.
The revenue for a web application whether it is an e-commerce site, an e-ticketing site or an ad serving engine will all depend on the throughput i.e. larger the throughput, larger the revenue and hence profit. We can then say that ‘R’ the revenue is

R (revenue) α k * T * t

In others words the revenue is proportional to the throughput.
Hence to determine the profitability of a particular cloud deployment we need to compare the cost of the deployment for a given throughput versus a projected profit margin. As long the cost of the deployment is less than the revenue arising from the throughput, the deployment will be profitable. This can be represented pictorially as below.

The graph clearly shows that for a profitable deployment
d/dt (k * T *t) > d/dt (a * D * t) or
k * T > a * D

Hence as can seen from the picture as long as the slope of the cumulative deployment costs are less that the slope of the revenue the deployment will be profitable.

INWARDi Technologies

Software Defined Networks (SDNs); A glimpse of tomorrow

Published in Telecom Asia, Jul 28,2011 - A glimpse into the future of networking
Published in Telecoms Europe, Jul 28,2011 - SDNs are new era for networking

Networks and networking, as we know it, is on the verge of a momentous change, thanks to a path breaking technological concept known as Software Defined Networks (SDN). SDN is the result of pioneering effort by Stanford University and University of California, Berkeley and is based on the Open Flow Protocol and represents a paradigm shift to the way networking elements operate.

Networks and network elements, of today, have been largely closed and have been based on proprietary architecture. In today’s network and switching and routing of data packets happen in the same network elements for e.g. the router.

Software Defined Networks (SDN) decouples the routing and switching of the data flows and moves the control of the flow to a separate network element namely, the flow controller. The motivation for this is that the flow of data packets through the network can be controlled in a programmatic manner. A Flow Controller can be typically implemented in a standard PC. In some ways this is reminiscent of Intelligent Networks and Intelligent Network Protocol which delinked the service logic from the switching and moved it a network element known as the Service Control Point.

The OpenFlow Protocol has 3 components to it. The Flow Controller that controls the flows, the OpenFlow switch and the Flow Table and a secure connection between the Flow Controller and the OpenFlow switch. The OpenFlow Protocol is an open source API specification for modifying the flow table that exists in all routers, Ethernet switches and hubs. The ability to securely control the flow of traffic programmatically opens ups amazing possibilities.

Alternatively, existing branded routers can implement the OpenFlow Protocol as an added feature to their existing routers and Ethernet switches. This will enable these routers and Ethernet switches to support both production traffic and research based traffic using the same set of network resources.

The single greatest advantage of separating the control and data plane of network routers and Ethernet switches is the ability to modify and control different traffic flows through a set of network resources. In addition to this benefit Software Define Networks (SDNs) also include the ability to virtualize the network resources. Virtualized network resources are known as a “network slice”. A slice can span several network elements including the network backbone, routers and hosts.

Computing resources can be virtualized through the use of the Hypervisor which abstracts the hardware and enables several guest OS to run in complete isolation. Similarly when a network element a FlowVisor, experimentally demonstrated, is used along with the OpenFlow Controller it is possible to virtualize the network resources. Hence each traffic flow gets a combination of bandwidth, routers, traffic flows and computing resources. Hence Software Defined Networks (SDNs) are also known as Virtualized Programmable Networks owing to the ability of different traffic flows being able to co-exist in perfect isolation of one another allowing for traffic flows through the resources to be controlled by programs in the Flow Controller.

The ability to manage different types of traffic flows across network resources opens up endless possibilities. SDNs have been successfully demonstrated in wireless handoffs between networks and in running multiple different flows through a common set of resources. SDNs in public and private clouds allow appropriate resources to be pooled during different times of the day based on the geographical location of the requests. Telcos could optimize the usage of their backbone network based on peak and lean traffic periods through the Core Network.

The OpenFlow Protocol has already gained widespread support in the industry and has resulted in the formation of the Open Networking Foundation (ONF). The members of ONF include behemoths like Google, Facebook, Yahoo, and Deutsche Telekom to networking giants like Cisco, Juniper, IBM and Brocade etc. Currently the ONF has around 43 member companies.

Software Define Networks is a tectonic shift in the way networks operate and truly represent the dawn of a new networking era. A related post of interest is "Adding the OpenFlow variable in the IMS equation"

INWARDi Technologies

The Case for Cloud Based IMS solution

IP Multimedia Systems (IMS) has been in the wings for some time. There have been several deployments by the major equipment manufacturers, but IMS is simply not happening. The vision of IMS is truly grandiose. IMS envisages an all-IP core with several servers known as Call Session Control Function (CSCF) participating to setup, maintain and release call sessions.

In the 3GPP Release 5 Architecture IMS draws an architecture of Proxy CSCF (P-CSCF), Serving CSCF(S-CSCF), Interrogating CSCF(I-CSCF), Breakout CSCF(B-CSCF), Home Subscriber Server(HSS) and Application Servers (AS) acting in concert in setting up, maintaining and release media sessions. The main protocols used in IMS are SIP/SDP for managing media sessions which could be voice, data or video and DIAMETER for connecting to the HSS and the Application Servers.

IMS is also access agnostic and is capable of handling landline or wireless calls over multiple devices from the mobile, laptop, PDA, smartphones or tablet PCs. The application possibilities of IMS are endless from video calling, live multi-player games to video chatting and mobile handoffs of calls from mobile phones to laptop. Despite the numerous possibilities IMS has not made prime time. While IMS technology paints a grand picture it has somehow not caught on. IMS as a technology, holds a lot of promise but has remained just that – promising technology.

The technology has not made the inroads into people’s imaginations or turned into a money spinner for Operators. One of the reasons may be that Operators are averse to investing enormous amounts into new technology and turning their network upside down.

This article provides an innovative approach to introducing IMS in the network by taking advantage of the public cloud!

Since IMS is an all-IP network and the protocol between the CSCF servers is SIP/SDP over TCP IP it can be readily seen that IMS is a prime candidate for the public cloud. An IMS architecture that has to be deployed on the cloud would have several instances of P-CSCFs, S-CSCFs, B-CSCFs, HSS and ASes all sitting on the cloud. An architectural diagram is shown below.

Deploying the CSCFs on the public cloud has multiple benefits. For one it a cloud deployment will eliminate the upfront CAPEX costs for the Operator. The cost savings can be passed on to the consumers whose video, data or voice calls will be cheaper. Besides, the absence of CAPEX will provide better margins to the operator. Lower costs to the consumer and better margins for the Operator is truly an unbeatable combination.

Also the elasticity of the cloud can be taken advantage of by the operator who can start small and automatically scale as the user base grows.

Thus a cloud based IMS deployment is truly a great combination both for the subscriber, the operator and the equipment manufactures. The cloud’s elasticity will automatically provide for growth as the irresistibility of IMSes high speed video applications catches public imagination.

If IMS as a technology needs to become common place then Operators should plan on deploying their IMS on the public cloud and reap the manifold benefits.A related post of relevance is "Adding the OpenFlow variable to the IMS equation".

INWARDi Technologies

Cache-22

If you want performance you need to partition data. If you partition data you will not get performance! That sounded clever but is it true? Well, it can be if the architecture of your application is naïve.

The problem I am describing here is when there is a need to partition data across multiple geographical regions. Partitioning data essentially spreads data among several servers resulting in fast accesses. But when the data is spread across large geographical distances then this will result in significant network latencies. This is something that cannot be avoided.

Memcached is a common technique to store commonly read data into in-memory caches preventing frequent dips to the database. Memcached accesses data through “gets” and updates data through “sets”. Data is accessed based on a key which is hashed to one of several participating servers. Thus memcached distributes the data among several participating servers in a server list. Reads and writes are of the order of O(1) and extremely fast. This works fine as long as servers belong to a single region or if the data center is in the same region. The network latencies will be low and the latency of the application will not be severely affected.

Now consider a situation where the memcached servers have to be distributed to multi-region data centers. While this is an excellent scheme for Disaster Recovery (DR) it introduces its own set of attendant problems.

Memcached will hash the entire data set and distribute it over the entire server list. Now “gets” of data from one geographical region to another will have significant latency. Since the laws of physics mandate that nothing can exceed the speed of light, we will be stuck with appreciable latencies for inter-region reads and writes. So while a multi-region deployment provides for geographical resiliency it does introduce issues of latency and degraded throughput.

So what is the solution? One possible solution is to replicate the data across the regions. The solution to this problem is to replicate data in all the regions. One technique that I can think of is to have the application to implement “local reads & global writes”. This technique provides for the AP part of the CAP Theorem. The CAP theorem states that it is impossible to completely provide Consistency, Availability and Partition tolerance to distributed application. The “local reads & global writes” method will assure availability and partition tolerance while providing for eventual consistency.

In this technique, updates are done both on local servers along with asynchronous writes to all data centers. The writes are hence global in nature. The updates will not wait for all writes to complete before moving along. However reads will be local ensuring that the latency is low. Data reads based on data proximity will ensure that latency is really low.

Since writes are asynchronous the data will tend to be “eventually consistent” rather than being “strongly consistent” but this is a tradeoff that can be taken into account. Ideally it will be essential to implement the quorum protocol along with the “local reads & global writes” technique to ensure that you read your writes.

The application could have a modified quorum protocol such that R+ W > N where R is the number of data reads and W is the number of writes to servers and N is the total number of servers in the memcached server list.

Similar technique has been used in Cassandra & CouchDB etc.

With the “local reads & global writes” technique it is possible keep the latencies within reasonable limits since data reads will be based on proximity. Also replication the data to all regions will also ensure that eventually all regions will have a consistent view of the data.

INWARDi Technologies