My travels through the realms of Data Science, Machine Learning, Deep Learning and (AI)

Then felt I like some watcher of the skies

When a new planet swims into his ken;

Or like stout Cortez when with eagle eyes

He star’d at the Pacific—and all his men

Look’d at each other with a wild surmise—

Silent, upon a peak in Darien.

On First Looking into Chapman’s Homer by John Keats

The above excerpt from John Keat’s poem captures the the exhilaration that one experiences, when discovering something for the first time. This also summarizes to some extent my own as enjoyment while pursuing Data Science, Machine Learning and the like.

I decided to write this post, as occasionally youngsters approach me and ask me where they should start their adventure in Data Science & Machine Learning. There are other times, when the ‘not-so-youngsters’ want to know what their next step should be after having done some courses. This post includes my travels through the domains of Data Science, Machine Learning, Deep Learning and (soon to be done AI).

By no means, am I an authority in this field, which is ever-widening and almost bottomless, yet I would like to share some of my experiences in this fascinating field. I include a short review of the courses I have done below. I also include alternative routes through courses which I did not do, but are probably equally good as well. Feel free to pick and choose any course or set of courses. Alternatively, you may prefer to read books or attend bricks-n-mortar classes, In any case, I hope the list below will provide you with some overall direction.

All my learning in the above domains have come from MOOCs and I restrict myself to the top 3 MOOCs, or in my opinion, ‘the original MOOCs’, namely Coursera, edX or Udacity, but may throw in some courses from other online sites if they are only available there. I would recommend these 3 MOOCs over the other numerous online courses and also over face-to-face classroom courses for the following reasons. These MOOCs

Are taken by world class colleges and the lectures are delivered by top class Professors who have a great depth of knowledge and a wealth of experience
The Professors, besides delivering quality content, also point out to important tips, tricks and traps
You can revisit lectures in online courses anytime to refresh your memory
Lectures are usually short between 8 -15 mins (Personally, my attention span is around 15-20 mins at a time!)

Here is a fair warning and something quite obvious. No amount of courses, lectures or books will help if you don’t put it to use through some language like Octave, R or Python.

The journey
My trip through Data Science, Machine Learning started with an off-chance remark,about 3 years ago, from an old friend of mine who spoke to me about having done a few courses at Coursera, and really liked it. He further suggested that I should try. This was the final push which set me sailing into this vast domain.

I have included the list of the courses I have done over the past 5 years (37+ certifications completed and another 9 audited-listened only without doing the assignments). For each of the courses I have included a short review of the course, whether I think the course is mandatory, the language in which the course is based on, and finally whether I have done the course myself etc. I have also included alternative courses, which I may have not done, but which I think are equally good. Finally, I suggest some courses which I have heard of and which are very good and worth taking.

1. Machine Learning, Stanford, Prof Andrew Ng, Coursera
(Requirement: Mandatory, Language:Octave,Status:Completed)
This course provides an excellent foundation to build your Machine Learning citadel on. The course covers the mathematical details of linear, logistic and multivariate regression. There is also a good coverage of topics like Neural Networks, SVMs, Anamoly Detection, underfitting, overfitting, regularization etc. Prof Andrew Ng presents the material in a very lucid manner. It is a great course to start with. It would be a good idea to brush up some basics of linear algebra, matrices and a little bit of calculus, specifically computing the local maxima/minima. You should be able to take this course even if you don’t know Octave as the Prof goes over the key aspects of the language.

2. Statistical Learning, Prof Trevor Hastie & Prof Robert Tibesherani, Online Stanford– (Requirement:Mandatory, Language:R, Status;Completed) –
The course includes linear and polynomial regression, logistic regression. Details also include cross-validation and the bootstrap methods, how to do model selection and regularization (ridge and lasso). It also touches on non-linear models, generalized additive models, boosting and SVMs. Some unsupervised learning methods are also discussed. The 2 Professors take turns in delivering lectures with a slight touch of humor.

3a. Data Science Specialization: Prof Roger Peng, Prof Brian Caffo & Prof Jeff Leek, John Hopkins University (Requirement: Option A, Language: R Status: Completed)
This is a comprehensive 10 module specialization based on R. This Specialization gives a very broad overview of Data Science and Machine Learning. The modules cover R programming, Statistical Inference, Practical Machine Learning, how to build R products and R packages and finally has a very good Capstone project on NLP

3b. Applied Data Science with Python Specialization: University of Michigan (Requirement: Option B, Language: Python, Status: Not done)
In this specialization I only did the Applied Machine Learning in Python (Prof Kevyn-Collin Thomson). This is a very good course that covers a lot of Machine Learning algorithms(linear, logistic, ridge, lasso regression, knn, SVMs etc. Also included are confusion matrices, ROC curves etc. This is based on Python’s Scikit Learn

3c. Machine Learning Specialization, University Of Washington (Requirement:Option C, Language:Python, Status : Not completed). This appears to be a very good Specialization in Python

4. Statistics with R Specialization, Duke University (Requirement: Useful and a must know, Language R, Status:Not Completed)
I audited (listened only) to the following 2 modules from this Specialization.
a.Inferential Statistics
b.Linear Regression and Modeling
Both these courses are taught by Prof Mine Cetikya-Rundel who delivers her lessons with extraordinary clarity. Her lectures are filled with many examples which she walks you through in great detail

5.Bayesian Statistics: From Concept to Data Analysis: Univ of California, Santa Cruz (Requirement: Optional, Language : R, Status:Completed)
This is an interesting course and provides an alternative point of view to frequentist approach

6. Data Science and Engineering with Spark, University of California, Berkeley, Prof Antony Joseph, Prof Ameet Talwalkar, Prof Jon Bates
(Required: Mandatory for Big Data, Status:Completed, Language; pySpark)
This specialization contains 3 modules
a.Introduction to Apache Spark
b.Distributed Machine Learning with Apache Spark
c.Big Data Analysis with Apache Spark

This is an excellent course for those who want to make an entry into Distributed Machine Learning. The exercises are fairly challenging and your code will predominantly be made of map/reduce and lambda operations as you process data that is distributed across Spark RDDs. I really liked the part where the Prof shows how a matrix multiplication on a single machine is of the order of O(nd^2+d^3) (which is the basis of Machine Learning) is reduced to O(nd^2) by taking outer products on data which is distributed.

7. Deep Learning Prof Andrew Ng, Younes Bensouda Mourri, Kian Katanforoosh : Requirement:Mandatory,Language:Python, Tensorflow Status:Completed)

This course had 5 Modules which start from the fundamentals of Neural Networks, their derivation and vectorized Python implementation. The specialization also covers regularization, optimization techniques, mini batch normalization, Convolutional Neural Networks, Recurrent Neural Networks, LSTMs applied to a wide variety of real world problems

The modules are
a. Neural Networks and Deep Learning
In this course Prof Andrew Ng explains differential calculus, linear algebra and vectorized Python implementations of Deep Learning algorithms. The derivation for back-propagation is done and then the Prof shows how to compute a multi-layered DL network
b.Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Deep Neural Networks can be very flexible, and come with a lots of knobs (hyper-parameters) to tune with. In this module, Prof Andrew Ng shows a systematic way to tune hyperparameters and by how much should one tune. The course also covers regularization(L1,L2,dropout), gradient descent optimization and batch normalization methods. The visualizations used to explain the momentum method, RMSprop, Adam,LR decay and batch normalization are really powerful and serve to clarify the concepts. As an added bonus,the module also includes a great introduction to Tensorflow.
c.Structuring Machine Learning Projects
A very good module with useful tips, tricks and traps that need to be considered while working on Machine Learning and Deep Learning projects
d. Convolutional Neural Networks
This domain has a lot of really cool ideas, where images represented as 3D volumes, are compressed and stretched longitudinally before applying a multi-layered deep learning neural network to this thin slice for performing classification,detection etc. The Prof provides a glimpse into this fascinating world of image classification, detection andl neural art transfer with frameworks like Keras and Tensorflow.
e. Sequence Models
In this module covers in good detail concepts like RNNs, GRUs, LSTMs, word embeddings, beam search and attention model.

8. Neural Networks for Machine Learning, Prof Geoffrey Hinton,University of Toronto
(Requirement: Mandatory, Language;Octave, Status:Completed)
This is a broad course which starts from the basic of Perceptrons, all the way to Boltzman Machines, RNNs, CNNS, LSTMs etc The course also covers regularisation, learning rate decay, momentum method etc

9.Probabilistic Graphical Models, Stanford Prof Daphne Koller(Language:Octave, Status: Partially completed)
This has 3 courses
a.Probabilistic Graphical Models 1: Representation – Done
b.Probabilistic Graphical Models 2: Inference – To do
c.Probabilistic Graphical Models 3: Learning – To do
This course discusses how a system, which can be represented as a complex interaction
of probability distributions, will behave. This is probably the toughest course I did. I did manage to get through the 1st module, While I felt that grasped a few things, I did not wholly understand the import of this. However I feel this is an important domain and I will definitely revisit this in future

10. Reinforcement Specialization : University of Alberta, Prof Adam White and Prof Martha White
(Requirement: Very important, Language;Python, Status: Partially Completed)
This is a set of 4 courses. I did the first 2 of the 4. Reinforcement Learning appears deceptively simple, but it is anything but simple. Definitely a very critical area to learn.

a.Fundamentals of Reinforcement Learning: This course discusses Markov models, value functions and Bellman equations and dynamic programming.
b.Sample based learning Learning methods: This course touches on Monte Carlo methods, Temporal Difference methods, Q Learning etc.

Reinforcement Learning is a must-have in your AI arsenal.

11. Tensorflow in Practice Specialization – Prof Laurence Moroney – Deep Learning.AI
(Requirement: Important, Language;Python, Status: Completed)
This is a good course but definitely do the Deep Learning Specialization by Prof Andrew Ng
There are 4 courses in this Specialization. I completed all 4 courses. They are fairly straight forward
a. Introduction to TensorFlow – This course introduces you to Tensorflow, image recognition with brute-force method
b. Convolutional Neural Networks in Tensorflow – This course touches on how to build a CNN, image augmentation, transfer learning and multi-class classification
c. Natural Language Processing in Tensorflow – Word embeddings, sentiment analysis, LSTMs, RNNs are discussed.
d. Sequences, time series and prediction – This course discusses using RNNs for time series, auto correlation

12. Natural Language Processing Specialization – Prof Younes Bensouda, Lukasz Kaiser from DeepLearning.AI
(Requirement: Very Important, Language;Python, Status: Partially Completed)
This is the latest specialization from Deep Learning.AI. I have completed the first 2 courses
a.Natural Language Processing with Classification and Vector Spaces -The first course deals with sentiment analysis with Naive Bayes, vector space models, capturing dependencies using PCA etc
b. Natural Language Processing with Probabilistic Models – In this course techniques for auto correction, Markov models and Viterbi algorithm for Parts of Speech tagging, auto completion and word embedding are discussed.

13. Mining Massive Data Sets Prof Jure Leskovec, Prof Anand Rajaraman and ProfJeff Ullman. Online Stanford, Status Partially done.,
I did quickly audit this course, a year back, when it used to be in Coursera. It now seems to have moved to Stanford online. But this is a very good course that discusses key concepts of Mining Big Data of the order a few Petabytes

14. Introduction to Artificial Intelligence, Prof Sebastian Thrun & Prof Peter Norvig, Udacity
This is a really good course. I have started on this course a couple of times and somehow gave up. Will revisit to complete in future. Quite extensive in its coverage.Touches BFS,DFS, A-Star, PGM, Machine Learning etc.

15.Deep Learning (with TensorFlow), Vincent Vanhoucke, Principal Scientist at Google Brain.
Got started on this one and abandoned some time back. In my to do list though

My learning journey is based on Lao Tzu’s dictum of ‘A good traveler has no fixed plans and is not intent on arriving’. You could have a goal and try to plan your courses accordingly.
And so my journey continues…

I hope you find this list useful.
Have a great journey ahead!!!

Into the Telecom vortex

“Ten little Indian boys went out to dine,
One choked his little self and then there were nine
Nine little Indian boys sat up very late;
One overslept himself and then there were eight…”

From the poem “Ten Little Indians”

You don’t need to be particularly observant to notice that the telecom landscape over the last decade and a half is full of dead organizations, bloodshed and gore. Organizations have been slain by ruthless times and bigger ones have devoured the weaker, fallen ones. Telecom titans have vanished, giants have been reduced to dwarfs.

Some telecom companies have merged in a deadly embrace trying to beat the market forces only to capitulate to its inexorable death march.

The period from the early 1980s to the late 1990’s were the glorious periods for telecommunication. Digital switches (1972-1982), ISDN (1988), international calling, trunk protocols, mobile (~1991), 2G, 2.5G, and 3G moved in succession, one after another.

Advancement came after advancement. The future had never looked so bright for telecom companies.

The late 1990’s were heady years, not just for telecom companies, but to all technology companies. Stock prices soared. Many stocks were over-valued. This was mainly due to what was described as the ‘irrational exuberance’ of the stock market.

Lucent, Alcatel, Ericsson, Nortel Networks, Nokia, Siemens, Telecordia all ruled supreme.

1997-2000. then the inevitable happened. There was the infamous dot-com bust of the 2000 which sent reduced many technology stocks to penny stocks. Telecom company stocks went into a major tail spin. Stock prices of telecom organizations plummeted. This situation, many felt, was further exacerbated by the fact that nothing important or earth shattering was forth-coming from the telecom. In other words, there was no ‘killer app’ from the telecommunication domain.

From 2000 onwards 3G, HSDPA, LTE etc. have all come and gone by. But the markets were largely unimpressed. This was also the period of the downward slide for telecom. The last decade and a half has been extra-ordinarily violent. Technology units of dying organizations have been cannibalized by the more successful ones.

Stellar organizations collapsed, others transformed into ‘white dwarfs’, still others shattered with the ferocity of a super nova.

Here is a short recap of the major events.

2006 – After a couple of unsuccessful attempts Alcatel and Lucent finally decide to merge
2006 – Nokia marries Siemens in a 20 billion Euro deal. N
2009-10 – Ericsson purchases Nortel’s CDMA and LTE business for $1.13 billion
2009-10 – Nortel implodes
2010 – Motorola sells networking unit to Nokia for $1.2 Billion
2011 – Internet giant Google mops up Motorola’s handset division for $12.5 billion, largely for the patents
2012 – Ericsson closes a deal with Telcordia for $1.15 billion
2013 – Nokia sells its handset division to Microsoft after facing a serious beating from smartphones
2015 – Nokia agrees to a $16.6 billion takeover of Alcatel Lucent

And so the story continues like the rhyme in Agatha Christie’s mystery novel

“And then there were none”

“Ten little Indian boys went out to dine,
One choked his little self and then there were nine…”

The Telecom companies continue their search for the elusive ‘killer app’ as progress comes in small increments – 3G, 3.5G, 3.75G, 4G, and 5G etc.

Personally I think the future of Telecom companies, lies in its ability to embrace the latest technologies of Cloud Computing, Big Data, Software Defined Networks, and Software Defined Datacenters and re-invent themselves. Rather than looking for some elusive ‘killer app’ they have to re-enter the technology scene with a Big Bang

As I referred to in one of my earlier posts “Architecting a cloud Based IP Multimedia System” the proverbial pot at the end of the rainbow may be in

Virtualizing IP Multimedia Switches (IMS) namely the CSCFs (P-CSCF, S-CSCF, I-CSCF etc.),
Using the features of the cloud like Software Defined Storage (SDS) , Load balancers and auto-scaling to elastically scale-up or scale down the CSCF instances to handle varying ‘call traffic’
Having equipment manufacturers (Nokia, Ericsson, and Huawei) will have to use innovating pricing models with the carriers like AT&T, MCI, Airtel or Vodafone. Instead of a one-time cost for hardware and software, the equipment manufacturers will need to charge based on usage or call traffic (utility charging). This will be a win-win for both the equipment manufacturer and carrier
Using SDN to provide the necessary virtualized pipes between users with the necessary policies for advanced services like video-chat, white-boarding, real-time gaming etc.
Using Big Data and Hadoop to analyze Call Detail Records (CDRs) and provide advanced services to customers like differential rates for calls etc

Clearly there will be challenges in this virtualized view of things. Telecom equipment is renowned for its 5 9’s availability. The challenge will be achieving this resiliency, high availability and fault-tolerance with cloud servers. How can WAN latencies be mitigated? How to can SDN provide the QoS required for voice, video and data traffic in IMS?

IMS has many interesting services where video calls from laptops can be transferred as data calls to mobile phones and vice versa, from mobile networks to WiFi and so on.

Many hurdles will have to be crossed. But this is, in my opinion, will be the path forward.

While the last decade and a half have been bad for the telecom industry, I personally feel we are on the verge on the next big breakthrough in telecom in the next year or two. Telecom will rise like the phoenix from its ashes in the next couple of years

Also see
1. A crime map of India in R: Crimes against women
2. What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
3. Bend it like Bluemix, MongoDB with autoscaling – Part 2
4. Informed choices through Machine Learning : Analyzing Kohli, Tendulkar and Dravid
5. Thinking Web Scale (TWS-3): Map-Reduce – Bring compute to data
6. Deblurring with OpenCV:Weiner filter reloaded

Presentation on Wireless Technologies – Part 2

Here is a continuation of my earlier presentation on Wireless Technologies – Part 1. These presentations trace the evolution of telecom from basic telephony all the way to the advances in LTE.

Wireless technologies – Part 2 from Tinniam Ganesh

Find me on Google+

Architecting a cloud based IP Multimedia System (IMS)

Here is an idea of mine that has been slow cooking in my head for more than 1 and a 1/2 year. Finally managed to work its way to IP.com. See link below

Architecting a cloud based IP Multimedia System (IMS)

The full article is included below

Abstract

This article describes an innovative technique of “cloudifying” the network elements of the IP Multimedia (IMS) framework in order to take advantage of keys benefits of the cloud like elasticity and the utility style pricing. This approach will provide numerous advantages to the Service Provider like better Return-on-Investment(ROI), reduction in capital expenditure and quicker deployment times, besides offering the end customer benefits like the availability of high speed and imaginative IP multimedia services

Introduction

IP Multimedia Systems (IMS) is the architectural framework proposed by 3GPP body to establish and maintain multimedia sessions using an all IP network. IMS is a grand vision that is access network agnostic, uses an all IP backbone to begin, manage and release multimedia sessions. This is done through network elements called Call Session Control Function (CSCFs), Home Subscriber Systems (HSS) and Application Servers (AS). The CSCFs use SDP over SIP protocol to communicate with other CSCFs and the Application Servers (AS’es). The CSCFs also use DIAMETER to talk to the Home Subscriber System (HSS’es).

Session Initiation Protocol (SIP) is used for signaling between the CSCFs to begin, control and release multi-media sessions and Session Description Protocol (SDP) is used to describe the type of media (voice, video or data). DIAMETER is used by the CSCFs to access the HSS. All these protocols work over IP. The use of an all IP core network for both signaling and transmitting bearer media makes the IMS a very prospective candidate for the cloud system.

This article proposes a novel technique of “cloudifying” the network elements of the IMS framework (CSCFs) in order to take advantage of the cloud technology for an all IP network. Essentially this idea proposes deploying the CSCFs (P-CSCF, I-CSCF, S-CSCF, BGCF) over a public cloud. The HSS and AS’es can be deployed over a private cloud for security reasons. The above network elements either use SIP/SDP over IP or DIAMETER over IP. Hence these network elements can be deployed as instances on the servers in the cloud with NIC cards. Note: This does not include the Media Gateway Control Function (MGCF) and the Media Gate Way (MGW) as they require SS7 interfaces. Since IP is used between the servers in the cloud the network elements can setup, maintain and release SIP calls over the servers of the cloud. Hence the IMS framework can be effectively “cloudified” by adopting a hybrid solution of public cloud for the CSCF entities and the private cloud for the HSS’es and AS’es.

This idea enables the deployment of IMS and the ability for the Operator, Equipment Manufacturer and the customer to quickly reap the benefits of the IMS vision while minimizing the risk of such a deployment.

Summary

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 of multi-media call sessions. The multi-media sessions can be any combination of voice, data and video.

In the 3GPP Release 5 Architecture IMS draws an architecture of Proxy CSCF (P-CSCF), Serving CSCF(S-CSCF), Interrogating CSCF(I-CSCF), and Breakout CSCF(BGCF), Media Gateway Control Function (MGCF), 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 to the HSS.

The technology has not 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.

The IMS framework uses CSCFs which work in concert to setup, manage and release multi media sessions. This is done by using SDP over SIP for signaling and media description. Another very prevalent protocol used in IMS is DIAMETER. DIAMETER is the protocol that is used for authorizing, authenticating and accounting of subscribers which are maintained in the Home Subscriber System (HSS). All the above protocols namely SDP/SIP and DIAMETER protocols work over IP which makes the entire IMS framework an excellent candidate for deploying on the cloud.

Benefits

There are 6 key benefits that will accrue directly from the above cloud deployment for the IMS. Such a cloud deployment will

i. Obviate the need for upfront costs for the Operator

ii. The elasticity and utility style pricing of the cloud will have multiple benefits for the Service Provider and customer

iii. Provider quicker ROI for the Service Provider by utilizing a innovative business model of revenue-sharing for the Operator and the equipment manufacturer

iv. Make headway in IP Multimedia Systems

v. Enable users of the IMS to avail of high speed and imaginative new services combining voice, data, video and mobility.

vi. The Service Provider can start with a small deployment and grow as the subscriber base and traffic grows in his network

Also, a cloud deployment of the IMS solution has multiple advantages to all the parties involved namely

a) The Equipment manufacturer

b) The Service Provider

c) The customer

A cloud deployment of IMS will serve to break the inertia that Operators have for deploying new architectures in the network.

a) The Equipment manufactures for e.g. the telecommunication organizations that create the software for the CSCFs can license the applications to the Operators based on innovative business model of revenue sharing with the Operator based on usage

b) The Service Provider or the Operator does away with the Capital Expenditure (CAPEX) involved in buying CSCFs along with the hardware. 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. A cloud based IMS will also greatly reduce the complexity of dimensioning a core network. Inaccurate dimensioning can result in either over-provisioning or under-provisioning of the network. Utilizing a cloud for deploying the CSCFs, HSS and AS can obviate the need upfront infrastructure expenses for the Operator. As mentioned above the Service Provider can pay the equipment manufactured based on the number of calls or traffic through the system

c) Lastly the customer stands to gain as the IMS vision truly allows for high speed multimedia sessions with complex interactions like multi-party video conferencing, handoffs from mobile to laptop or vice versa. Besides IMS also allows for whiteboarding and multi-player gaming sessions.

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.

Description

This article describes a method in which the Call Session Control Function (CSCFs) namely the P-CSCF, S-CSCF,I-CSCF and BGCF can be deployed on a public cloud. This is possible because there are no security risks associated with deploying the CSCFs on the public cloud. Moreover the elasticity and the pay per use of the public cloud are excellent attributes for such a cloudifying process. Similarly the HSS’es and AS’es can be deployed on a private cloud. This is required because the HSS and the AS do have security considerations as they hold important subscriber data like the IMS Public User Identity (IMPU) and the IMS Private User Identity (IMPI). However, the Media Gateway Control Function (MGCF) and Media Gateway (MGW) are not included this architecture as these 2 elements require SS7 interfaces

Using the cloud for deployment can bring in the benefits of zero upfront costs, utility style charging based on usage and the ability to grow or shrink elastically as the call traffic expands or shrinks.

This is shown diagrammatically below where all the IMS network elements are deployed on a cloud.

In Fig 1., all the network elements are shown as being part of a cloud.

Fig 1. Cloudifying the IMS architecture.

Detailed description

This idea requires that the IMS solution be “cloudified “i.e. the P-CSCF, I-CSCF, S-CSCF and the BGCF should be deployed on a public cloud. These CSCFs are used to setup, manage and release calls and the information that is used for the call does not pose any security risk. These network elements use SIP for signaling and SDP over SIP for describing the media sessions. The media sessions can be voice, video or data.

However the HSS and AS which contain the Public User Identity (IMPU) and Private User Identity (IMPI) and other important data can be deployed in a private cloud. Hence the IMS solution needs a hybrid solution that uses both the public and private cloud. Besides the proxy SIP servers, Registrars and redirect SIP servers also can be deployed on the public cloud.

The figure Fig 2. below shows how a hybrid cloud solution can be employed for deploying the IMS framework

Fig 2: Utilizing a hybrid cloud solution for deploying the IMS architecture

The call from a user typically originated from a SIP phone and will initially reach the P-CSCF. After passing through several SIP servers it will reach a I-CSCF. The I-CSCF will use DIAMETER to query the HSS for the correct S-CSCF to handle the call. Once the S-CSCF is identified the I-CSCF then signals the S-CSCF to reach a terminating a P-CSCF and finally the end user on his SIP phone. Since the call uses SDP over SIP we can imagine that the call is handled by P-CSCF, I-CSCF, S-CSCF and BGCF instances on the cloud. Each of the CSCFs will have the necessary stacks for communicating to the next CSCF. The CSCF typically use SIP/SDP over TCP or UDP and finally over IP. Moreover query from the I-CSCF or S-CSCF to the HSS will use DIAMTER over UDP/IP. Since IP is the prevalent technology between servers in the cloud communication between CSCFs is possible.

Methodology

The Call Session Control Functions (CSCFs P-CSCF, I-CSCF, S-CSCF, BGCF) typically handle the setup, maintenance and release of SIP sessions. These CSCFs use either SIP/SDP to communicate to other CSCFs, AS’es or SIP proxies or they use DIAMETER to talk to the HSS. SIP/SDP is used over either the TCP or the UDP protocol.

We can view each of the CSCF, HSS or AS as an application capable of managing SIP or DIAMETER sessions. For this these CSCFs need to maintain different protocol stacks towards other network elements. Since these CSCFs are primarily applications which communicate over IP using protocols over it, it makes eminent sense for deploying these CSCFs over the cloud.

The public cloud contains servers in which instances of applications can run in virtual machines (VMs). These instances can communicate to other instances on other servers using IP. In essence the entire IMS framework can be viewed as CSCF instances which communicate to other CSCF instances, HSS or AS over IP. Hence to setup, maintain and release SIP sessions we can view that instances of P-CSCF, I-CSCF, S-CSCF and B-CSCF executed as separate instances on the servers of a public cloud and communicated using the protocol stacks required for the next network element. The protocol stacks for the different network elements is shown below

The CSCF’s namely the P-CSCF, I-CSCF, S-CSCF & the BGCF all have protocol interfaces that use IP. The detailed protocol stacks for each of these network elements are shown below. Since they communicate over IP the servers need to support 100 Base T Network Interface Cards (NIC) and can typically use RJ-45 connector cables, Hence it is obvious that high performance servers which have 100 Base T NIC cards can be used for hosting the instances of the CSCFs (P-CSCF, I-CSCF, S-CSCF and BGCF). Similarly the private cloud can host the HSS which uses DIAMETER/TCP-SCTP/IP and AS uses SDP/SIP/UDP/IP. Hence these can be deployed on the private cloud.

Network Elements on the Public Cloud

The following network elements will be on the public cloud

a) P-CSCF b) I-CSCF c) S-CSCF d) BGCF

The interfaces of each of the above CSCFs are shown below

a) Proxy CSCF (P-CSCF) interface

As can be seen from above all the interfaces (Gm, Gq, Go and Mw) of the P-CSCF are either UDP/IP or SCTP/TCP/IP.

b) Interrogating CSCF(I- CSCF) interface

As can be seen from above all the interfaces (Cx, Mm and Mw) of the I-CSCF are either UDP/IP or SCTP/TCP/IP.

c) Serving CSCF (S-CSCF) interfaces

The interfaces of the S-CSCF (Mw, Mg, Mi, Mm, ISC and Cx) are all either UDP/IP or SCTP/TCP/IP

d) Breakout CSCF (BGCF) interface

The interfaces of the BGCF (Mi, Mj, Mk) are all UDP/IP.

Network elements on the private cloud

The following network elements will be on the private cloud

a) HSS b) AS

a) Home Subscriber Service (HSS) interface

The HSS interface (Cx) is DIAMETER/SCTP/TCP over IP.

b) Application Server (AS) Interface

The AS interface ISC is SDP/SIP/UDP over IP.

As can be seen the interfaces the different network elements have towards other elements are over either UDP/IP or TCP/IP.

Hence we can readily see that a cloud deployment of the IMS framework is feasible.

Conclusion

claimtoken-515bdc0894cdb

Thus it can be seen that a cloud based IMS deployment is feasible given the IP interface of the CSCFs, HSS and AS. Key features of the cloud like elasticity and utility style charging will be make the service attractive to the Service providers. A cloud based IMS deployment is truly a great combination for all parties involved namely the subscriber, the Operator and the equipment manufactures. A cloud based deployment will allow the Operator to start with a small customer base and grow as the service becomes popular. Besides the irresistibility of IMS’ high speed data and video applications are bound to capture the subscribers imagination while proving a lot cheaper.

Also see my post on “Envisioning a Software Defined Ip Multimedia System (SD-IMS)”

Find me on Google+

The Case for a Cloud Based IMS Solution

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.

Please see my post for a more detailed view of the above post in “Architecting a cloud based IP Multimedia System (IMS)”

A related post of relevance is “Adding the OpenFlow variable to the IMS equation“.

Find me on Google+