Firstly, we’re thrilled to be recognized by a blogger, who in this case, had previous hands on experience with Application Performance Management (APM) products like OpTier, CA Wily, HP and dynaTrace. Secondly, whilst it was obviously good (and slightly nerving at times) to read our features and capabilities compared with another vendor (and the APM market in general), it was actually nice to see our freemium and SaaS based go-to-market strategy being recognized as well. I guess these things were actually the main reason why a blogger could access, compare and contrast two next generation APM solutions in the first place. It’s not like IBM, CA or Compuware would make their APM solution available to the masses for evaluation, let alone welcome an independent opinion.

You can read Ben’s blog article in full here.

App Man.

Update: HP does in fact offer their solution (HP Diagnostics v9) via trial, but you’ve got to download and install 4GB of their software.  In the time it takes to do this you could already be up and running with AppDynamics Lite.

Why? There is little or no intelligence in how a monitoring solution determines what is normal or abnormal for application performance. Today, monitoring solutions are only as good as the users that configure them, which is bad news because humans make mistakes, configuration takes time, and time is something many of us have little of.

Its therefore no surprise to learn that behavioral learning and analytics are becoming key requirements for modern application performance monitoring (APM) solutions. In fact, Will Capelli from Gartner recently published a report on IT Operational Analytics and pattern based strategies in the data center. The report covered the role of Complex Event Processing (CEP), behavior learning engines (BLEs) and analytics as a means for monitoring solutions to deliver better intelligence and quality information to Dev and Ops. Rather than just collect, store and report data, monitoring solutions must now learn and make sense of the data they collect, thus enabling them to become smarter and deliver better intelligence back to their users.

Change is constant for applications and infrastructure thanks to agile cycles, therefore monitoring solutions must also change so they can adapt and stay relevant. For example, if the performance of a business transaction in an application is 2.5 secs one week, and that drops to 200ms the week after because of a development fix. 200ms should become the new performance baseline for that same transaction, otherwise the monitoring solution won’t learn or alert of any performance regression. If the end user experience of a business transaction goes from 2.5 secs to 200ms, then end user expectations change instantly, and users become used to an instant response. Monitoring solutions have to keep up with user expectations, otherwise IT will become blind to the one thing that impacts customer loyalty and experience the most.

So what does behavioral learning and analytics actually do, and how does it help someone in IT? Let’s look at some key Dev and Ops use cases that benefit from such technology.

#1 Problem Identification – Do I have a problem?

Alerts are only as good as the thresholds which trigger them. A key benefit of behavioral learning technology is the ability to automate the process of discovering and applying relevant performance thresholds to an application, its business transactions and infrastructure, all without human intervention. It does this by automatically learning the normal response time of an application, its business transactions and infrastructure, at different hours of the day, week and month, ensuring these references create an accurate and dynamic baseline of what normal application performance is over-time.

A performance baseline which is dynamic over-time is significantly more accurate than a baseline which is static. For example, having a static baseline threshold which assumes application performance is OK if all response times are less than 2 seconds is naive and simplistic. All user requests and business transactions are unique, they have distinct flows across the application infrastructure, which vary, depending on what data is requested, processed and packaged up as a response.

Take for example, a credit card payment business transaction – would these requests normally take less than 2 seconds for a typical web store application? not really, they can vary between 2 and 10 seconds. Why? There is often a delay whilst an application calls a remote 3rd party service to validate credit card details before it can be authorized and confirmed. In comparison, a product search business transaction is relatively simple and localized to an application, meaning it often returns sub-second response times 24/7 (e.g. like Google). Applying a 2 second static threshold to multiple business transactions like “credit card payment” and “search” will trigger alert storming (false and redundant alerts). To avoid this without behavioral learning, users must manually define individual performance thresholds for every business transaction in an application. This is bad, because as I said earlier, nobody in IT has the time to do this, so most users resort to applying thresholds which are static and global across an application. Don’t believe me? ask your Ops people whether they get enough alerts today, chances are they’ll smile or snarl.

The screenshot below shows the average response time of a production application over-time, with spikes representing peak load during weekend evening hours. You can see on weekdays normal performance is around 100ms, yet under peak load its normal to experience application performance of up to several seconds. Applying a static threshold in this scenario, of 1 or 2 seconds would basically cause alert storming at the weekend even though its normal to see such performance spikes. This application could therefore benefit from behavioral learning technology so the correct performance baseline is applied for the correct hour and day.

Another key limitation with alerts and traditional monitoring solutions is that they lack business context. They’re typically tied to infrastructure health rather the health of the business, making it impossible for anyone to understand the business impact of an alert or problem. It can be the difference between “Server CPU Utilization is above 90%” and “22% of Credit Card Payments are stalling”. You can probably guess the latter alert is more important to troubleshoot than pulling up a terminal console, logging onto a server and typing prstat to view processes and CPU usage. Behavioral learning combined with business context allows a monitoring solution to alert on the performance and activity of the business, rather than say, the performance and activity of its infrastructure. This ensures Dev and Ops have the correct context to understand and be aligned with the business services.

Analytics can also play a critical role in how monitoring data is presented to the user to help them troubleshoot. If a business transaction is slow or has breached its threshold, the user needs to understand the severity of the problem. For example, were a few or lot of user transactions impacted? how many returned errors or actually stalled and timed out? Everything is relative, Dev or Ops doesn’t have the time to investigate every user transaction breach, its therefore important to prioritize with business impact before jumping in to troubleshoot.

If we look at the below screenshot of AppDynamics Pro, you can see how behavioral learning and analytics can help a user identify a problem in production. We can see the checkout business transaction has breached its performance baseline (which was learnt automatically), we can also see the severity of the breach which shows no errors, 10 slow requests, 13 very slow and no stalls. 23 out of the 74 user requests (calls) were impacted meaning this is a critical problem for Dev and Ops to troubleshoot.

#2 Problem Isolation – Where is my problem?

Once a user has identified abnormal application performance, the next step for them is to isolate where that latency is spent in the application infrastructure. A key problem today is that most monitoring solutions collect and report data, but they don’t process or visualize it in a way that automates problem isolation for a user. Data exists, but its down to the individual users to drill down and piece together data, so they can find what they’re looking for. This is made difficult by the fact that performance data can be fragmented across multiple silo’s and monitoring toolsets, making it impossible for Dev or Ops to get a consistent end to end view of application performance and business activity. To solve this data fragmentation problem, many monitoring solutions use time-based correlation or Complex Event Processing (CEP) engines to piece together data/events from the multiple sources, so they can look for patterns or key trends which may help a user isolate where a problem or latency exists in an application.

For example, if a user credit card payment business transaction took 9 seconds to execute, where was that 9 seconds spent in the application infrastructure exactly? If you look at performance data from an OS, app server, database or network perspective you’ll end up with four different views of performance, none of which relate to that individual credit card payment business transaction which took 9 seconds. Using time-based correlation won’t’ help either, knowing the database was running at 90% cpu whilst the credit card payment transaction executed is about as helpful as a poke in the eye. Time-based correlation is effectively a guess, given the complexity and distribution of applications today, the last thing you want to be doing is guessing where a problem might be in your application infrastructure. Infrastructure metrics tell you how an application is consuming system resource, they don’t have the granularity to tell you where an individual user business transaction is slow in the infrastructure.

Behavioral learning can be used together to learn and track how business transactions flow across distributed application infrastructure. If a monitoring solution is able to learn the journey of a business transaction, then they can monitor the real flow execution of them across and inside distributed application infrastructure. By visualizing the entire journey and latency of a business transaction, at each hop in the infrastructure, monitoring solutions can make it simple for Dev and Ops to isolate problems in seconds. If you want to travel from San Francisco to LA by car, the easiest way to understand that journey, is to visualize it on Google Maps in seconds. In comparison, the easiest way for Dev or Ops to isolate a slow user business transaction, is to do the same thing and visualize its journey across the application infrastructure. For example, take the below screenshot which shows the distributed transaction flow of a “Checkout” business transaction which took 10 seconds across its application infrastructure. You can see that 99.8% of its response time is spent making a JDBC call to the Oracle database. Isolating problems this way is much faster and efficient than tailing log files or asking sys, network or DBA administrators whether their silos are performing correctly.

You can also apply dynamic base-lining and analytics to the performance and flow execution of a business transaction. This means a monitoring solution can effectively highlight to the user which application infrastructure tier is responsible for a performance breach and baseline deviation. Take for example, the below screenshot which visualizes the flow of a business transaction in a production environment, and highlights the breach for the application tier “Security Server” which has deviated from its normal performance baseline of 959ms.

Behavioral learning and analytics can therefore be a key enabler to automating problem isolation in large, complex, distributed applications.

#3 Problem Resolution – How do I fix my problem?

Once Dev or Ops has isolated where the problem is in the application infrastructure, the next step is to then identify the root cause. Many monitoring solutions today can collect diagnostic data which relate to the activity of components within an application tier such as a JVM, CLR or database. For example, a java profiler might show you thread activity, a database tool might show you top N SQL Statements. What these tools lack is the ability to tie diagnostic data to the execution of real user business transactions which are slow or breaching associated performance thresholds. When Ops picks up the phone to an angry user, users don’t complain about CPU utilization, thread synchronization or garbage collection. Users complain about specific business transactions they are trying to complete like login, search or purchase.

As I outlined above in the Problem Isolation section, monitoring solutions can leverage behavioral learning technology to monitor the flow execution of business transactions across distributed application infrastructure. This capability can also be extended inside an application tier, so monitoring solutions can learn, and monitor, the relevant code execution of a slow or breaching business transaction.

For example, here is a screenshot which shows the complete code execution (diagnostic data) of a distributed Checkout business transaction which took 10 seconds. We can see in the top dialogue the code execution from the initial struts action all the way through to the remote Web Service call which took 10 seconds. From this point we can drill inside the offending web service to its related application tier and see its code execution, before finally pinpointing the root cause of the problem which is a slow SQL statement as shown.

Without behavioral learning and analytics,  monitoring solutions lack intelligence on what diagnostic data to collect. Some solutions try to collect everything, whilst others limit what data they collect so that their agent overhead doesn’t become intrusive in production environments. The one thing you need when trying to identify root cause is complete visibility, otherwise you begin to make assumptions or guess what might be causing things to run slow. If you only have 10% visibility into the application code in production, then you’ve only got a 10% probability of finding the actual root cause of an issue. This is why users of most legacy application monitoring solutions struggle to find root cause – because they have to balance application code visibility with monitoring agent overhead.

Monitoring today isn’t about collecting everything, its about collecting what is relevant to business impact, so any business impact can be resolved as quickly as possible. You can have all the diagnostic data in the world, but if that data isn’t provided in the right context for the right problem to the right user, it becomes as about as useful as a chocolate teapot.

With applications becoming every increasingly complex, agile, virtual and distributed. Dev and Ops no longer have the time to monitor and analyze everything. Behavioral learning and analytics must help Dev and Ops monitor whats relevant in an application, so they can focus on managing real business impact instead of infrastructure noise. Monitoring solutions must become smarter so Dev and Ops can automate problem identification, isolation and resolution. The more monitoring solutions rely on human intervention to configure and analyze, the more monitoring solutions will continue fail.

If you want to experience how behavioral learning and analytics can automate the way you manage application performance, take a trial of AppDynamics Pro and see for yourself.

Embed this image on your site:
<a href="http://www.appdynamics.com/blog/2012/01/20/appdynamics-growth/" title="AppDynamics Growth - Not Bad for 3 Years"><br /> <img src="http://www.appdynamics.com/blog/wp-content/uploads/2012/01/AppDynamics_growth_infographic2.png" alt="AppDynamics Growth in 2011" /></a><br /> <br/>Source: <a href="http://www.appdynamics.com" title="AppDynamics Application Performance Management Tools">AppDynamics: Application Monitoring Company</a><br/>

Today, we just secured an additional $20 million funding led by Kleiner Perkins, have a brand new 13,500 sq ft “state of the art” office on 2nd and Folsom, 20 open head counts listed on our careers page, and we are re-defining the way customers manage application performance. Our Freemium and SaaS deployment models mean that prospects can evaluate our application monitoring technology in minutes without speaking to a single sales rep. In a competitive marketplace with over 35 vendors like CA Wily, Compuware DynaTrace, HP and IBM, our frictionless buying experience is a huge advantage and differentiator. Our ability to make application monitoring simple to deploy, and easy to use, for highly agile, distributed and virtual applications, makes us very difficult to compete with, and that’s before we get to our pricing – which is at least 50% less than all of the names mentioned above.

We’re making application monitoring accessible and affordable for everyone, because application performance doesn’t just impact large enterprises, it impacts organizations of all sizes. We have customers with 2, 20, 200 and 2000+ nodes in their applications. Rest assured, our mission is for prospects to understand the value of APM in less than 30 minutes by downloading AppDynamics Lite or by taking an online trial of AppDynamics Pro. We’re seeing enormous traction and positive feedback from prospects on this evaluation and buying experience; it’s validation that the days of traditional enterprise software is over. No prospect wants to spend two weeks filling in POC questionnaires, and trying to install complex, bloated application monitoring software while spending hours with technical consultants and account managers onsite. APM should be about what software can do, not what professional services can deliver.

At AppDynamics we’ve got big plans for 2012 with an aggressive and innovative product roadmap. Today’s additional funding will accelerate our growth plans and help us execute, so we can become a global force in application performance management (APM). We’ll be raising the bar for APM so customers can manage application performance all by themselves – the days of manual installations, burdensome configuration and unintuitive user interfaces will be over. There’s a new generation of applications which need managing and AppDynamics is leading the charge.

App Man.

According to Gartner’s report, APM is evolving into four key market requirements:

1. Complex and varied End Points

2. Cloud Services

3. Packaged Applications

4. Big Data

Mobile Devices

Mobile and smart phone devices are definitely causing an increase in business transaction throughput and concurrency for many applications today. We had a media customer recently launch a brand new service for mobile devices, traffic on this service was doubling every week and it wasn’t long before application performance deteriorated as a result. Within a month two major bottlenecks were found relating to user authentication and database access. As application volume and concurrency increases, its indeed vital as Gartner outlines, for customers to report how performance and scalability is being impacted.

APM as a Service

For the first time in our market an analyst is using the phrase “APMaaS” to describe the demands for monitoring application performance as a service. In much the same way that salesforce.com delivered CRM, only a few vendors are now delivering APM and monitoring. This is great validation for our product architecture at AppDynamics, which supports both on-premise and SaaS deployments. We made a decision back in 2008 to build a multi-tenant platform so we could offer APM as a service. Today, we see around a third of our customers choosing this deployment over our traditional on-premise deployment option. While APMaaS is innovative, its adoption does vary by industry and customer profiles. For example, we see financial services and large enterprises less likely to use a public cloud to send data outside of their corporate network for security and governance reasons, therefore our on-premise solution is more relevant. On the flip side, SMB’s are leveraging public clouds to become more agile without having to manage or own IT infrastructure, APMaaS for these organizations is therefore more attractive.

Packaged Applications

Packaged applications will always remain a key APM requirement due to the amount of legacy and 3rd party applications that exist. We saw a lot of packaged application in 2011, specifically Yantra supply chain for manufacturing customers, Blackboard in education, ATG/Sharepoint for content driven web applications and plenty of 3rd party blackbox applications like search, reporting and BI engines. The majority of these platforms have Java or .NET run-times which makes it relatively trivial for a vendor like AppDynamics to instrument, monitor and track business transaction execution through such platforms.

Big Data

Finally, Big Data as you’re probably aware is already a hot topic of conversation in IT. In APM we see this split into two different areas:

1. APM Analytics (for Big Monitoring Data)

2. Support of Big Data Technologies (e.g. NoSQL)

APM analytics will be key in 2012 to enable users to monitor and troubleshoot performance faster than ever before. The ability for a solution to auto-discover and analyze behavioral patterns in application performance, and business transaction execution, will help users automate their incident management and MTTR. Central to the AppDynamics product architecture is a behavioral learning and analytics engine, which is used to dynamically baseline application performance and trigger diagnostic data collection when needed. This ensures low monitoring overhead in production and also provides Dev and Ops teams with complete diagnostic data to determine the root cause of real performance issues in minutes.

In addition, Cloud is requiring more elastic and scalable data stores like Cassandra and MongoDB which are becoming major players in the NoSQL landscape. As data access for applications extends to these technologies, its vital APM solutions are able to monitor their performance and scalability.

Gartner on AppDynamics

Finally here is a brief summary of how Gartner saw AppDynamics:

AppDynamics’ freemium platform is delivered on-premises and as software as a service (SaaS). The offering includes four dimensions of APM, but lacks functionality in the end-user experience monitoring dimension. The product combines deep-dive monitoring functionality, transaction tracing and a pattern discovery engine to deliver educated autodiscovery in the offering. When the engine recognizes a macrolevel event pattern previously associated with a problem, the platform automatically initiates microlevel, deep-dive monitoring. Using this approach, AppDynamics is able to avoid the resource consumption problems associated with the continuous running of deep-dive monitoring. This and the ability to do basic autoscaling of Amazon Elastic Compute Cloud (Amazon EC2) workloads have made them well-suited to, and able to deliver in, large dynamic cloud deployments.

Its a pretty fair assessment really, we obviously partner with BMC Coradiant at the moment for End User Monitoring so can’t claim kudos on that point. We do feel our support and innovation for Cloud, Packaged Apps and Big Data is second to none in the market and we’ll be working really hard in 2012 to extend our support in those respective areas. Feel free to download AppDynamics Lite or trial AppDynamics Pro, you can access and evaluate our innovation in minutes.

We look forward to a great 2012 and will be working hard with analysts such as Gartner to keep you posted on our developments and progress. You can read the full Gartner report here.

App Man.

Interest in the Application Performance Management (APM) category is very high right now.   To stay one step ahead of their clients, the Industry Analysts who cover the category and write research to advise their clients have been very busy.  In December alone, there were six different analyst reports being researched by the major analyst firms.

Forrester published the results of their research in the 2nd week of December with the report: Market Overview: Application Performance Management, Q4 2011.  Forrester clients can access the report at www.forrester.com. In this report, Forrester provides very sound advice on why APM exists and what it should do for clients. Forrester has created their own “Reference Model” for APM and evaluated the vendor landscape against those criteria.

Raison d’etre for APM

Forrester VP and Principal Analyst, JP Garbani, gives readers very pragmatic advice on the raison d’etre for APM.  Simply put, APM’s job is to:

1) Alert IT to application performance and availability issues before a full-scale outage occurs

2) Isolate or pinpoint the problem source

3) Provide deep-diagnostics to enable IT to determine the root cause

For several years now, JP Garbani has been on the forefront of proclaiming that modern APM solutions should enable IT organizations to manage apps not by gauging the heath of their servers or servlets, but instead by assessing what the customer or end-user cares about most – whether their Business Transaction completes quickly and doesn’t make them wait.  He states that this has become even more critical as applications have gotten more distributed and complex.

Business Transactions

For most organizations, the Business Transaction will be something like “Login,” “Checkout,”  or “Pay Bill” – which is how the end-user thinks about the transaction they are attempting to execute.  Mr. Garbani goes further to point out why managing apps by Business Transaction is so important:  Only when the APM solution can map and trace each Business Transaction across the app infrastructure tiers being used can the APM solution be truly effective at step two and three above–isolating the problem source and determining root cause.

JP Garbani also states three conclusions that we wholeheartedly agree with.

1) Application performance is critical for workforce productivity (and customer productivity too)

2) IT favors new projects with short-term ROI and APM delivers exactly that.

3) Without tools & automation like APM, managing business service complexity is unmanageable

AppDynamics receives top scores

AppDynamics was pleased to be covered in this report and to receive the top score in key dimensions of Forrester’s Reference Model:

• Application Monitoring
• Transaction Tracing & Mapping
• Messaging Technology Monitoring
• Monitoring of Applications in Public Clouds
• Dashboard Reporting & Analytics

Our clients care passionately about having the best possible visibility into their application health and being able to quickly troublehoot and resolve problems.  This report gives them solid advice on what to expect from your APM solution and how to justify it.

ROI becoming critical for APM

We also appreciate that Forrester calls out ROI as a critical concern in the current economy.  When comparing APM solutions, it is important to assess all the R’s and I’s.  Here are a few suggestions for how to do this:

R – which APM solution provides your organization the the most visibility into the health of your application?

R – which APM solution will make your IT organization fastest at determining root cause and resolving issues?

R – which APM solution has the fastest-time-to-value.  Which one would  allow you to be deployed today?

R – which APM solution is so easy to use that more of your Application/IT Operations folks will adopt it?

I – what is the total cost of license and maintenance over 3-5 years?

I – what are the total costs of hardware and system software required to host the APM software?

I – what is the cost of the initial deployment in terms of personnel, consultant and training costs?

I – what are the ongoing costs to maintain the APM systems configuration and instrumentation?

For those new to AppDynamics, the Lite edition is 100% free…so you’ll really like the ROI of free.  60,000 users have already seen this for themselves.  Feel free to download here or request a 30 day trial of our Pro edition.

We feel APM should be for everyone. It should be affordable, it should be easy to deploy, and easy to use. APM should not be a luxury that only an elite group of enterprises can afford. Today, we have customers who monitor applications with 5 nodes, 50 nodes, 500 nodes and 5,000 nodes. Application performance impacts organizations of all sizes; that’s why we wanted our APM solution to be accessible to the masses over the web via our free download and SaaS trial. We wanted to be transparent with our buyers and demonstrate that they can evaluate and use our solution all by themselves with no account manager or technical consultant by their side. We really wanted prospects to see for themselves that APM can be simple to deploy and easy to use.

A major validation of this market disruption was when a customer called Karavel in France was looking for an APM solution and evaluated CA Wily, Compuware dynaTrace and AppDynamics. Karavel requested a trial, downloaded our software and we sent them a trial license key for 30 days. The whole AppDynamics install, deployment and evaluation was solely conducted by the customer on their own. This might not sound that impressive, but this is what the software buying experience should be all about: the customer and the solution. If the customer can’t install, deploy and evaluate an APM solution on their own, how will they manage this process when it comes to a production deployment? Software should sell itself these days–if it requires an army of people to sell it, it probably requires an army of people to implement it as well.

You can read the full Karavel press release here:
http://www.appdynamics.com/press/press-release-01-03-12.php

Full case study is available here also:
http://www.appdynamics.com/documents/case_studies/AppDynamics_CS_Karavel.pdf

Remember, software like APM doesn’t have to be complex and expensive. With the internet these days, there is no excuse why a prospect can’t download or evaluate solutions online in just a few hours.

App Man.

Embed this image on your site:
<a href="http://www.appdynamics.com/blog/2011/12/20/the-12-days-of-devops" title="AppDynamics Application Performance Management"><br /> <img src="http://www.appdynamics.com/blog/wp-content/uploads/2011/12/12_days_of_devops1.png" alt="12 Days of DevOps" /></a><br /> <br/>Source: <a href="http://www.appdynamics.com" title="AppDynamics Application Performance Management Tools">AppDynamics: Application Monitoring Company</a><br/>

Hey Santa Claus, what’s happening man?

Well, it’s that time of year again where unfortunately I have to work. Gift orders are up 50% and my elves are working their socks off right now stocking my warehouses for the big day. I honestly don’t know how I would have coped without my new ElfOps team–they’ve been elastic and fantastic thanks to our new AWS hosted applications. The bad news is that my applications have processed record orders in 2011 so I’ll have to work harder. Frankly, this sucks.

Come again? Santa is using the cloud?

Isn’t everyone these days? I mean what else can I possibly do when my orders and subscriptions double each year? If Santa can’t scale, I spend my vacation reading angry letters from parents about how I made their kids cry and upset on Christmas Day. Forget that. I’d rather sit on a beach in Hawaii drinking Mojito’s catching a nice tan. In fact, I should be able to upgrade myself to the Four Seasons in Hawaii in 2012 due to the money I’ve saved by migrating to the cloud. You simply wouldn’t believe how much my own data center was costing me, not to mention those expensive IT Elf consultants I had to bring in. I’m now a lean mean parcel delivering machine and it’s all thanks to the cloud.

Hey Santa, forgot to ask–what you drinking?

I’ll take a sherry, straight, shaken, but not stirred.

So why did your ElfOps team go with AWS?

Two reasons, really. The first has a lot to do with agility: not many cloud providers can spin up 5,000 nodes on the fly in a short period of time. Given the fluctuation of order volumes for Christmas, we simply can’t predict when orders will be received. Therefore, we need a cloud that is truly elastic and agile. For example, when Call of Duty Modern Warefare 3 was released on 10th November we almost brought down AWS due to the overwhelming demand. I think we needed almost 7,000 nodes to process the millions of orders we received over a weekend. The second reason is our applications are heavily distributed across multiple services, with several of those services connected to the retail arm of Amazon. It seemed like a natural fit to leverage Amazon EC2 given many of our applications were already leveraging Amazon web services API to keep our supply chain and order management ticking. Cost savings were an afterthought, really. In fact, I cleared my large data center out and used it as another warehouse to keep my stock onsite. Nothing worse than having to travel backwards and forwards to different stock depots on Christmas eve.

You’ll have to excuse me but I’ve never heard of the term ElfOps. Can you explain?

Sure. In the past we had Develfment who used to build applications, and Operations who used to deploy and support them. It got to the point where our agile release cycles were causing application outages, arguments, and constant fighting between the two teams. I therefore decided to merge them both into a single team which I called ElfOps. I believe IT should work together as a team, because if my applications go down or they don’t perform, I’ll end up kicking both their sorry asses anyway. I now have team that collaborates, communicates and is together accountable for making my business agile. It also means I do less work because pretty much everything is automated…apart from feeding the, reindeers that is. I had hoped this deployment tool we bought called Chef might help with that, but sadly it didn’t.

How does ElfOps manage application performance in the cloud?

Is that a rhetorical question, App Man? You know we use AppDynamics for this. Our applications these days are heavily distributed, complex and agile. Previously we had Wily Coyote to manage our application performance when it was just a a bunch of tomcat nodes. After we moved to a SOA and Cloud architecture, we were unable to get the same visibility we had due to monitoring limitations–not to mention dealing with a big 5 vendor. Can you believe that coyote tried to crank my support and maintenance up after being a loyal customer for five years?! With AppDynamics, my ElfOps team gets a complete end-to-end picture of application performance in the cloud, and we only pay for what we use thanks to your flexible subscription based pricing and SaaS platform.

What’s next for Santa?

Well, I’ve got a few research projects on the go. Every year I receive several billion gift orders for kids between the ages of 1 and 18. To improve my supply chain, inventory, and delivery schedules I’m looking at Big Data technology like Elfdoop Map/Reduce to try and discover patterns in my business so I can become more efficient, which basically means I don’t have to work as hard. For example, knowing that most kids in the UK want snooker tables as gifts means I can distribute snooker tables to my secret stock depot in the UK before Christmas Eve, whereas knowing kids in the US want baseball bats means I don’t have to ship snooker tables across the Atlantic. You wouldn’t believe how many reindeers it takes to ship a snooker table several thousand miles! Big Data is therefore a massive opportunity for me to better understand my customers, orders, and supply chain.

Maybe you could use Big Data to figure out which kids have been good at school over the year? Strike a deal with the schools to give you each student’s report, and then only deliver gifts to kids who’ve been good! You’ll save millions.

App Man, you’re a lot smarter than you look.

Lastly, who is your favorite superhero and why?

Hmm that’s a tough one. I’d have to say Postman Pat because like me, he has a red company car and always delivers on time.

We’ll release the full report soon, but here’s an infographic that summarizes some of the main findings:

Embed this image on your site:
<a href="http://www.appdynamics.com/blog/2011/12/14/storm-clouds-in-2012-summary-of-appdynamics-apm-customer-survey" title="AppDynamics Application Performance Management"><br /> <img src="http://www.appdynamics.com/blog/wp-content/uploads/2011/12/AppDynamics_infographic_2011_survey.jpg" alt="AppDynamics Application Performance Management" /></a><br /> <br/>Source: <a href="http://www.appdynamics.com" title="AppDynamics Application Performance Management Tools">AppDynamics: Application Monitoring Company</a><br/>

What I found personally surprising was the heavy reliance on log files. When you’re troubleshooting distributed architectures, time is of the essence–and there’s no way to cut your MTTR down when you’re relying on log files to identify root cause.

In fact, there’s only one guy who ever made using a log file look cool:

And I think we can all agree that’s a pretty unique use case.

We’ll have the full survey results available soon.