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”

Find me on Google+

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.


Find me on Google+

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.

Find me on Google+

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 architectures. 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.

OpenFlow Specification

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”

Find me on Google+

The Case for a 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.

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+

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.

Find me on Google+

Eliminating the Performance Drag

Nothing is more important to designers and architects of web applications than performance.   Everybody dreams of applications that can roar and spit fire! Attributes that are most sought after in large scale web applications are low latency and high throughput.  Performance is money. So it is really important to work the design and churn the best possible design.

Squeezing performance from your application is truly an art. The challenges and excitement are somewhat similar to what a race car designer or an aeronautical engineer would experience in reducing the drag on the machine while increasing the thrust of the engine. Understanding the physics of program execution is truly a rare art.

This post will attempt to touch upon some key aspects that have to be looked at a little more closely to wring the maximum performance from your application.

The Store Clerk Pattern: This is an oft quoted analogy to describe the relation between latency and throughput. In this example the application can be likened to retail store with store clerks checking out customers who queue with their purchases. Assume that there are 5 store clerks and each take 1sec to process a customer. If there 5 customers the response time for each customer will be 1 sec and a total throughput of 5 customers/sec can be achieved. However if a 6th customer enters then he/she will have to wait 1 sec in the queue while 5 customers are being handled by the store clerks. For this customer the response time will be 1 sec (waiting) + 1 sec (processing) or a total response time of 2 secs. It can be readily seen that as more customers queue up the wait times will increase and the latency will keep increasing. Also it has to be noted that the throughput will plateau at 5 customers/sec and cannot go above that.

The first point of attack in improving performance is to identify the store clerk pattern in your application. Identify where you application has a queue of incoming requests and a thread pool to address these requests as they get processed.  The latency and throughput are governed by the number of parallel thread (store clerks) who process and the wait times in the queue. One naïve technique is to increase the number of threads in the pool or increase the number of pools. However this may be limited by the CPU and system limitations. What is extremely important is to identify what factors contribute to the processing of each request. While processing, do threads need to access and retrieve data? Do they have to make API or SQL calls?  Identify what is the worst case performance of the thread and determine if this worst case can be improved by a different algorithm. So the key in the store clerk pattern is a) to optimize the threads in the pool and b) to improve the worst case performance of processing the request.

Resource Contention: This is another area of the application that needs to be looked at very closely. It is quite likely that data is being shared by many threads. Access to shared data is going to involve locks and waits. Identify and determine the worst case wait for threads. Is your application read-heavy and write-light or write-heavy and read-light? In the former situation it may be worthwhile to use a Reader-Writer locking algorithm in which many number of readers can simultaneously read data by updating a semaphore. However a write, which happens occasionally, will result in locking the resource and cause the  wait of all reader threads. However if the application is write-heavy then other alternatives like message based locking could be used. Clearly thread waits can be a drain on performance.

Algorithmic changes: If there are modules that perform enormous number of insertions, updates or deletions on data in memory then this has to be looked at closely. Determine the type of data structures or STLs being used.  The solution is to be able to re-organize data so that the operation happens much more efficiently ideally reaching towards   O (1). Maybe the data may need to be organized as hash map of lists or a hash map pointing to n-ary trees instead of a list of lists. This will really require deep thought and careful analysis to identify the best possible approach that provides the least possible times for the most common operation.

From Relational to NoSQL : Though the transition from a RDBMS to a NoSQL databases like Cassandra, CouchDB  etc would really be based on scalability, the ability to partition data horizontally and hash the key for accesses, updations and deletions will be really fast and is an avenue that is worth looking into.

Caching : This is a widely used technique to reduce frequent SQL queries to the database. Data that is commonly used can be cached in-memory. One such technique is to use memcached.  Memcached caches data across several servers. Access to data is through hashing and is of the order of O (1). If there is a miss of data in the memcached server’s then data is accessed through a SQL query. Access to data is through simple get, put methods in which the key is hashed to identify the server in which the data is stored.

Profiling : The judicious use of profiling tools  is extremely important in  optimizing performance. Tools like valgrind truly help in identifying bottlenecks. Other tools also help in monitoring thread pools and identifying where resource contention is taking place. It may also be worthwhile to timestamp different modules and collect data over several thousand runs, average them and pin-point trouble spots.

These are some technique that can be used for optimizing performance. However improving performance beyond a point will really depend on being able to visualize the application in execution and divining problem hot spots.

Find me on Google+

To Hadoop, or not to Hadoop

Published in Telecom Asia, Jul 23, 2012 – To Hadoop or not to Hadoop

To Hadoop, or not to Hadoop: that is the question.  In many  of my discussions I find that Hadoop with its implementation of Map-Reduce crops  ups time and time and again.  To many Map-Reduce is the panacea for all kinds of performance evils. It appears that somehow using the Map-Reduce in your application will magically transform your application into a high performing, screaming application.

The fact is the Map-Reduce algorithm is applicable to only certain class of problems.  Ideally it is suited to what is commonly referred to as “embarrassingly parallel” class of problems.  These are problems that are inherently parallel for e.g. the creation of inverted indices from web crawled documents.

Map-Reduce is an algorithm that has popularized by Google. The term map –reduce actually originates from Lisp in which the “map” function takes a list of arguments and performs the same operation on all of its argument. The” reduce” then applies a common criterion to pick a reduced set of values from this list. Google uses the Map-Reduce to create an inverted index. An inverted index basically provides a mapping of a word with the list of documents in which it occurs. This typically happens in two stages. A set of parallel “map” tasks take as input documents, parse them and emits a sequence of (word, document id) pairs. In other words, the map takes as input a key value pair (k1, v1) and maps it into an intermediate (k2, v2) pair. The reduce tasks take the pair of (word, document id), reduce them, and emit a (word, list {document id}). Clearly applications, like the inverted index, make sense for the Map-Reduce algorithm as several mapping tasks can work in parallel on separate documents. Another typical application is counting the occurrence of words in documents or the number of times a web URL has been hit from a traffic log.

The key point in all these typical class of problems is that the problem can be handled in parallel. Tasks that can execute independently besides being inherently parallel are eminently suitable for Hadoop processing. These tasks work on extraordinarily large data sets.  This is also another criterion for Hadoop worthy applications.

Hadoop uses a large number of commodity servers to execute the algorithm. A complementary technology along with Hadoop is the Hadoop Distributed File System (HDFS). The HDFS is a storage system in which the input data is partitioned across several servers. Google uses the Google File System (GFS) for its inverted index and page ranking algorithm.

Typical applications that are prime candidates for Hadoop are those applications that have to operate on terabytes of data. Also the additional requirement is that the application can run some sort of transformation or “map” algorithm on the data independently and produce an intermediate result for the “reduce” part of the algorithm. The “reduce” essentially applies some criteria on the intermediate sets to produce a zero or 1 output.

However several real world applications do not fall into this category where we can parallelize the execution of the application. For example an e-retail application which allows users to search for book, electronic products, add to shopping card and finally make the purchase, in my opinion,  is not really suited for Hadoop as each individual transaction is separate  and typically has its own unique sequential flow. An Ad serving application also is not ideally suited for Hadoop. Each individual transaction has its own individual flow in time.

However on closer look we can see that there are certain aspects of the application that are conducive to Hadoop based Map-Reduce algorithm. For e.g. if the application needs to search through large data sets for example the e-retail application will have tens of thousands of electronic products and books from different vendors with their own product id.  We could use Hadoop to pre-process these large amounts of data, classify and create smaller data sets which the e-retail or other application can use. Hadoop is a clear winner when large data sets have to searched, sorted or some subset selected from.

In these kinds of applications Hadoop has a clear edge over other types as it can really crunch data. Hadoop is also resilient to failures and is based on the principle of “data locality” which allows the “map” or “reduce” to use data stored locally on its sever or in a neighboring machine.

Hence while Hadoop is no silver bullet for all types of applications if due diligence is performed we can identify aspects of the application which can be crunched by Hadoop.

Find me on Google+

The Future is C-cubed: Computing, Communication and the Cloud

We are the on the verge of the next great stage of technological evolution. The trickle of different trends clearly point to what I would like to term as C-cubed (C3) representing the merger of computing technologies, communication advances and the cloud.

There are no surprises in this assessment. Clearly it does not fall into the category of Chaos theories’ “butterfly effect” where a seemingly unrelated cause has a far-reaching effect, typically the fluttering of a butterfly in Puerto Rico is enough to cause an earthquake in China.

The C-cubed future that seems very probable is based on the advances in mobile broadband, advances in communication and the emergence of cloud computing.  A couple of years back Scott McNealy of Sun Microsystems believed that the “network is the computer”. Now with the introduction of Google’s Chrome book this trend will soon catch on. In fact I can easily visualize a ubiquitous device which I would like to call as the “cloudbook”.

The cloudbook would be a device that would resemble a tablet like the iPad, Playbook etc but would carry little or no hard disk.  Local storage will be through USB devices or SD-Cards which these days come with large storage in the range of 80GB and above. The Cloud book would have no operating system. It would simply have a bootstrap program which will allow the user to choose from several different Operating Systems (OS) namely Window’s, Linux, Solaris and Mac etc which will execute on the cloud. All applications will be executed directly on the cloud. The user will also store all his programs and data on the cloud.  Some amount of offline storage will be possible in portable storage devices like the memory stick, SD card etc.

The cloudbook will be a ubiquitous device.  It will access the internet through mobile broadband.  The access could be through a GPRS, WCDMA or a LTE connection. With the blazing speeds of 56 Mbps promised by LTE the ability to access the public cloud for executing programs and for storing of data is extremely feasible. Access should be almost instantaneous. Using the mobile broadband for access and the cloud for computing and storage will be the trend in the future.

Besides its use for computing, the cloudbook will also be used for making voice or video calls. This is the promise of IP Multimedia Systems (IMS) technology. IMS is a technology that has been in the wings for quite some time. IMS technology envisages an all-IP Core Network that will be used for transporting voice, data and video. As the speeds of the IP pipes become faster and the algorithms to iron out QOS issues are worked out the complete magnificence of the vision of IMS will become a reality and high speed video applications will become common place.

The cloudbook will use the WCDMA, 3G, network to make voice and video calls to others. The 3G RNC or the 4G eNodeB’s will enable the transmission and reception of voice, data or video to and from the Core Network. LTE networks will either user Circuit Switched Fall Back (CSFB) or VOLTE (Voice over LTE) to transfer voice and video over either the 3G network or over the Evolved Packet Core (EPC).  In the future high speed video based calls and applications will be extremely prevalent and a device like the cloudbook will increase the user experience manifold.

Besides IMS also envisions Applications Server (AS) spread across the network providing other services like Video-on-Demand, Real-time multi player gaming. It is clear that these AS may actually be instances sitting off the public cloud.

Hence the future clearly points to a marriage of computing, communication and the cloud where each will have a symbiotic relationship with the other resulting in each other. The network can be visualized as one large ambient network of IMS Call Session Control Function Servers (CSCFs) , Virtualized Servers on the Cloud and Application servers (AS).

Mobile broadband will become commonplace and all computing and communication will be through 3G or 4G networks.

The future is almost here and the future is C-cubed (C3)!!!

Published in Telecom Asia, Jul 8 2011 – The Future is C-cubed

Find me on Google+

Managing Multi-Region Deployments

If there is one lesson from this year’s major Amazon’s EC2 outage it is “don’t deploy all your application instances in a single region”. The outage has clearly demonstrated that entire regions are not immune to disasters. Thus, it has become imperative for designers and architects to deploy applications spanning major regions. Currently there are 4 major regions – US-West, US-East, Europe and APAC.

Both fundamentally and from a strategic point of view it makes sense to deploy web applications in different regions for e.g. both in US-East and US-West. This will build into the application a certain amount of geographical resiliency . In this way you are protected from major debacles like the Amazon’s EC2 outage in April 2011 or a possible meteor crashing and burning in one of the data centers.

Deploying instances in different regions is almost like minimizing risk by diversifying your portfolio. The design of application besides including other methods of fault tolerance should also incorporate geographical resilience.

Currently Amazon’s ELB does not support load balancing across regions. The ELB can only distribute traffic among instances in different availability zones of a region. The solution is to go for other DNS services like UltraDNS, DNSMadeEasy or DynDNS.

These DNS services provide geoIP based load balancer that can distribute traffic based on the region from which it originated. Currently there are 4 major regions in the world – US-East, US-West, Europe and APAC. GeoIP based traffic distribution besides balancing the load based on origination also has the added benefit of getting to the application closest to the origination thus reducing latencies.

The GeoIP based traffic distributor can distribute traffic to the closest region. An Amazon’s ELB can then internally distribute the traffic among the instances within that region. For a look at some typical problems in multi-region cloud deployments do look at my post “Cache-22”

INWARDi Technologies

Deploying across regions

Find me on Google+