By Diego Lomanto

For most companies, the challenge with big data lies in making sense of the data acquired in order to apply it to real world problems when decisions matter most.  Big data is hot right now because we recognize that we are generating more data than ever before and that we might be able to do something with it.  However, much the execution of big data has been around storage of the data (think Hadoop) and search (think Splunk).  That’s a great start, but do they really solve any problems in a new way on their own?

Start a big data project and you will soon realize that the data itself is limited because it is partial (takes whatever is available), difficult to consume for analysis (because it’s unstructured) and often offers limited value use cases.  It’s complicated.

I think the evolution towards better value from the data is still in progress.  I think we’ll not only see continued progress in storage but I believe that technology will emerge to make working with big data feel a wee bit smaller.  What I mean by that is we’ll still collect the data at massive scales, but there will be technology that simplifies the big data into a model that is consumable by analytic applications.  In other words, it will transform the data to actually represent something that can be analyzed.

Big Transaction Data

Big Transaction Data (BTD) is a great example of this.   It is complete, comprehensive and correlated.  But it’s also usable.  Let’s have a quick primer on BTD.

What it is, effectively, is the data generated by transactional systems in raw form modeled to represent the unique end-to-end transaction that drove the data generation in the first place, and stored alongside millions, billions, trillions (insert your own “illion” here) of other transactions.  This is done by technology – typically business transaction management software that observes and reports on transaction performance at each tier.

This is REAL big data in action.  And that’s where business transaction data comes into play.  BTD takes the data and stores it in a consumable form for analytics.  The transaction becomes the anchor for the analytics process.

The Problem with Fragmented Data

For example, say you wanted to analyze the end to end process performance of a financial trade system.  The systems that execute financial trades are ridiculously complex.  Think of the most complex system you can think of  and then multiply it by 3.  Why?  Because they are using a mix of new and old technologies and it’s distributed across multiple tiers and managed by many different stakeholders.  So what you get his this hodgepodge of tiers to execute trades that is incredibly difficult to rationalize into a singular data set.  The unfortunate by-product of this is that your view of the trade transaction is really just fragmented data.  You can see pieces of the transaction performance but not really ALL of the transaction.

But, you still need to analyze trades across the tiers and processes as a single input into your trade effectiveness analysis.  So you do the best you can.  You go deep into the tier data and try to correlate it on your own within your own analytic model.  For example, you try to monitor cross-process fallout with a cool looking dashboard that gives you data on each process, but you don’t really do it well and miss a lot of cross-process issues.

Or you try to do a cost analysis.  Or a segmentation analysis.  Or a performance analysis.  But the work to create a singular data set is so complicated that you never really have full confidence in the results.

Big Transaction Data in Action

Here is a great opportunity to employ big transaction data.  Instead of working with billions of manually correlated data points, let’s simplify and work with millions of well-defined transactions instead.  End-to-end transactions that represent each trade across each process in full.  Now you have a data set that you can inject it into your BI platform or use simply use BI tools within the big transaction data solution itself for analysis.

So back to those 3 Cs.  The data is complete – that means all information is generated by BTM end to end one view.  It’s comprehensive – capturing ALL interactions. And, it’s correlated – it knows everything about vital meta-data such as user, tiers, etc. The result is easy to consume meaningful analytics leading to business outcomes.

So, big data is hot.  But it’s not quite there yet.  We’re waking up with more data but we’re still working to rationalize it.  Fortunately, the technology is on its way to simplify and gain more (true) value from big data.

]]> http://blog.optier.com/2012/05/09/overcoming-the-complexity-of-big-data-with-big-transaction-data/feed/ 0 diegolomanto @diego_lomanto on twitter http://blog.optier.com/2012/05/01/big-transaction-data-the-corporations-new-friend/ http://blog.optier.com/2012/05/01/big-transaction-data-the-corporations-new-friend/#comments Tue, 01 May 2012 18:46:18 +0000 Linh C. Ho http://blog.optier.com/?p=1102 ]]> By Linh C. Ho (@linh_ho_nyc)

2012 will undoubtedly see big data remain high on the agenda of big businesses. In fact, recently the Obama administration shared plans to spend $200 Million on ‘Big Data’ initiative. According to Deloitte, by the end of 2012 more than 90 percent of the Fortune 500 will likely have at least some Big Data initiatives under way. Ovum research reports 50% of global enterprise IT investment decision makers plan to spend on Big Data analytics in the years to come. Why this hype? Organizations believe that with the skyrocketing data volumes, big data holds the key to getting ahead of the competition, and most importantly could help to improve the experience of the life-blood of their organization, their customers.  

In today’s competitive and “always-on” business environment, it’s critical that organizations are not sinking with the volume of data but have the relevant information at their fingertips when needed. Take a CIO for example, he needs to understand how his enterprise IT department enables and affects business outcomes. Intelligence about revenue-generating transactions, customer interactions (are they using tablets, mobile devices, browsers? etc), usage consumption patterns (cost per business transaction, per application or per user)—all become handy information for technology executives. Today, customers expect not just availability but immediacy, and are unforgiving for slowness and failure to complete their transactions with the business (e.g. querying about the terms of service before asking about its price). Businesses that can’t guarantee an always-on, speedy service and cannot respond to issues quickly enough could be rapidly held accountable.

Clearly, in order to generate this level of real-time Big Transaction Data analysis a business will need to access and analyze huge amounts of data throughout the customer journey. This is considered daunting by many not just because of the need to analyze such vast data and put it into relevant context but also collecting it from the multiple different touch points at which the business interacts with customers is complex. This is where Big Transaction Data initiatives come into play.

Customers are executing transactions every second through applications they’re interacting with. Whether the customers are using an online billing portal, a website login, a tablet, mobiles or putting their card into a chip and pin terminal—they’re executing a business transaction. Whenever they use these applications, a transaction is created that travels through a business’ IT infrastructure at lightening speeds.

Capturing and ongoing management of all business transactions 24 hours a day, seven days a week, and create a real-time map of what is occurring in their infrastructure is what big transaction data is all about.  Turning your IT management into a big transaction data initiative gives you the innovative edge to immediately get business context to your unstructured data—that is revenue generating transactions! This way, the business wins, IT wins.

Big Transaction Data is enabled by Business Transaction Management to automatically discover, collect and store data, dynamically map into business context (revenue-generating transactions), continuously monitor every single transaction taking place across their business. This allows for agile response when and how a transaction has failed in real time and further, making use of such information to proactively keep user experience failures or even slowness from occurring in the first place.

In short, big data can be an incredibly useful asset to organizations looking to improve the business outcomes. However, for that to happen, you must find the big data that provides the business insight to enable you to monitor, analyze and act instantly at all times. Big Transaction Data provides a powerful, business-relevant and dynamic solution.

By Diego Lomanto and Iddo Avneri 

I recently heard a customer that manages applications for a Fortune 50 bank say in a meeting, “Before OpTier we needed almost 20 people to isolate the problem location, now I can do it in 5 minutes by myself”.  He attributed the breakthrough to our dynamic topology mapping functionality that is a core capability of Always-on APM.   I have heard comments like this about dynamic topologies quite often, so I thought it would be neat to document how and why this happens.  I went to our Director of Presales, Iddo Avneri – the expert on how our product works in the field and he provided some amazing examples of dynamic topologies in action (thanks Iddo!).  That research led to this blog post.

Before I get into what I found, let me quickly summarize what dynamic topology maps (or “living topologies” or “business transaction maps” as some people like to call it) are and how they are used in a BTM-driven APM solution. Since OpTier’s Always-on APM tracks transactions in real time, across distributed application tiers, it discovers the flow topology for each transaction as a byproduct of the tracking and can display it visually. This way, it is possible to easily see valuable information such as the different paths transactions of the same type take, whether the transaction followed its expected path and did not deviate to unexpected databases, applications, or legacy servers, where transaction flow breaks and so on.

Let’s imagine you were to draw a diagram of your application architecture at any point in time.  It could look something like this:

One key problem with the drawing above is that it’s static.  That’s a moment in time.  There are tools out there which can do this for you, but because they are not using transactions as the method for tracing the topology, they do not update in real-time or give you good information around the relationship between the tiers.

That’s where a dynamic topology map comes into play. The image below shows an example of the flow topology of one type of a business transaction as discovered and visualized by a transaction-driven approach to APM.

As you can see, this mapping looks a lot like the hand-drawn version.  However, it’s dynamic –it’s been automatically been derived from the actual transactions that flowed thru the app in this period.  This means that the next time you look at it, if the infrastructure or app evolves and changes – this picture will evolve and change as well, and you don’t need to do anything.  Or it could simply be that transactions types not used during the first period will be invoked.   The system automatically detects that.

Another great advantage of this approach is that it provides details on the health and performance of tiers from a transaction perspective – i.e. how did the tier impact the transaction tracked as it went through, and also provides insight into the relationship between tiers in the transaction context – i.e. which transactions use the LDAP connection between tier A and the SiteMinder server.  In the picture above you can see some tiers are green and some are red – these colors are indications of service level performance.  And the lines between tiers show how much transaction volume flows between the tiers. So how are these maps being used today?

The reason that we can do this is because of the transaction data we generate.  This data has many uses, but one of the keys is how simple it makes application performance management.  As the transaction passes each tier, data is sent to a centralized server for analysis.  The end result is this map.  You can only do this if you take a transaction approach.

Users of Dynamic Topologies

Let’s start with who is using it.  It starts with the folks in the service operations center.  A dynamic topology gives them view of the overall health of the infrastructure and how it relates to the application ervices being delivered by it.  And if SLAs are in danger of being breached, and alerts start to go off indicating the degradation, they know exactly where in the topology to go look deeper and who to involve.  In addition, performance optimization analysts and applications support use dynamic topologies to find ways to improve performance. QA Teams can compare different load tests and see if any tier is performing badly, or even – are all the tiers and the entire transaction path is being tested in a load test?  And perhaps once of the most interesting uses of dynamic topologies is by the application support team to eliminate all-hands calls. To me, that was one of the neatest benefits that I saw.  Instead of getting 30 people on one call and going through a painful roll call to diagnose the problem (“no problem here!”) you can just go directly to the managers of the tiers that are in the red.

Deployment of Dynamic Topologies

The key to deploying this technology is that dynamic topologies represent automatically discovered relationships.  All you need to is instrument the tiers (a simple agent install on the key tiers – not all of them either) but you don’t need to tell the technology anything about the relationship between tiers.  That’s the magic.  The agents automatically detect those relationships by tracking the transactions that flow across the tiers end to end – the way Fedex tracks packages.  Here for example is a screenshot of a UAT environment in which an application makes calls to a production database as discovered by transactions making those calls.

Reading Dynamic Topologies

Dynamic topologies provide a few crucial pieces of information just by looking at them.

• Coupling between tiers – the thickness between tiers representing how many transactions go through relative to other flows
• Chattiness between tiers – indicated by color – with red indication chatty (as in potentially bad ) behavior

In the screenshot below you will see that customer portals 1 and 2 are very chatty with the authentication server. OpTier’s Always-on APM identified single executions on the application servers that made hundreds of authentication calls because of a misconfigured Single-Sign-On mechanism. As a result, users suffered very bad response times for specific types of transactions.

Another common example is transactions that make many connections to the database. In the below screen shot, we display a single invocation of a transaction (an instance), every arrow between the portal application server and the reporting server displays a connection from the shared connection pool. Obviously when a single transaction abuses a shared connection, the rest have to wait for it to become free:

What you will also notice in the screenshot is observed tiers (they are marked by the open door).  You don’t need to instrument (put agents on) your entire environment. Even without installing on all tiers, a dynamic topology will automatically include calls to non-instrumented tiers and show valuable information such as the amount of time spent on those tiers, error information and more.  For specific tiers such as database you can even see more, for example you can show connection pool utilization and long running SQLs.

Here is an example where the observed tier capabilities helped detect a major application configuration error. Once installed in the UAT environment at this customer site the customer noticed the application server making calls to the production database. While trying to reproduce a problem from production in UAT the application server configuration was copied over from production, including the data source target:

Drilling Down from Dynamic Topologies

What’s neat about dynamic topologies is that they immediately indicate SLA breaches by color and you can isolate the problems for immediate resolution by drilling down using your deep dive tools (already included in the OpTier solution if you don’t have one).  In the screenshot below we see that the compliance web service is breaching its SLA because it is red.  This information tells us:

• what application is not working
• the transaction types affected
• the transaction instances affected (there could be many instances of a specific type of transaction)
• who is being affected (users , locations…)

Another neat example is usage for load balancing.  In the screenshot below we see that there are 3 Jbosses in a cluster and one of the instances handles only an uneven distribution of 12% of the traffic.  Another common use case is for configuration issues. For example, if you have 4 web logic servers that receive an even load, but 3 have an average response time of 5 milliseconds but one of them has a response of 35 milliseconds.  There’s a configuration issue.  That information is all right there in the dynamic topology!

So, if you are looking for a solution that can help you get a better sense of the dynamic infrastructure that is your application environment, it’s very helpful to take the dynamic topology approach that is facilitated by transaction analysis.  By following transactions through the entire backend architecture, dynamic topologies can give you high level performance overviews, detect hotspots in testing that need to be dealth with before they impact you in production, as well as give you the detailed information that helps you avoid or resolve any problems that arise in production.  What about your experiences?  We’d love to hear some more stories from your environments.

Picking a game wrong in your bracket can greatly impact your ability to win your bet or office pool. A similar situation plays out daily in IT in regards to business critical applications. Hundreds of alerts indicate a service degradation and looming failure. But in the case of IT, where does one look to assess the impact? From a Application Performance Management (APM) perspective, the answer is the service dependency map. This article will compare the NCAA Men’s Basketball Tournament with APMchallenges that are met by Business Transaction Management (BTM).

The annual NCAA Men’s Basketball Tournament is currently underway. It is better known as “March Madness”. In 1981, CBS bought the rights to the air the tournament for an average of $16M per year. According to an ESPN Outside the Lines report, in 1999, CBS inked a new contract for an average of $545M per year, which is an increase of ~3300%. According to a TNS Media Intelligence article, the ad revenue for the NCAA Tournament in 2007 was $520M, second only to pro football for post-season ad revenue for US sports. Bottom-line, this is big business. But why does the NCAA Tournament create so much interest and passion? In large part it is due to the way people can analyze, bet on and track the tourney using the bracket.

The tournament is composed of 64 teams broken down into 4 geographic regions of 16 teams each. This year’s starting bracket was as follows:

There are 63 games with two outcomes each (team A or team B wins), which yields 263 combinations of potential outcomes, which is more than 9 quintillion (9,000,000,000,000,000,000). (See more on this calculation at The Math Forum @ Drexel, “NCAA Tournament Possibilities”).  This doesn’t even include the play-in.

With so many combinations, you never know what can happen, hence the madness. However, there is a map to guide you through the madness – the bracket itself. You can track the outcome of each game and your status (winning/losing) at any point in the tournament. The bracket is living entity for the duration of the tournament and contains only the most important high-level information – who won the game and perhaps the score. (Noticeably absent are lower level metrics such as Possessions, Field Goal %, Fouls, Turnovers, etc.)

In the word of information technology, there are many potential paths a transaction can take in even a (grossly) simplified application topology such as this:

In this example, if you assume all transactions start at the first logical tier, there are 32 (2*4*2*2) possible transaction paths. That doesn’t seem so bad at first, but then consider that each transaction must be considered individually as well. If an application handles 100,000 transaction per day (many applications handle that many per minute) then there are now 3.2M combinations of transaction and path. Even a simple example represents a daunting challenge. This challenge arises precisely by adding the transaction dimension to the application topology. BTM answers this challenge.

BTM adds the transactional dimension to APM (which is absent from configuration management databases (CMDBs)). It also provides high level metrics (transaction response time, transaction chattiness and transaction specific resource consumption) that combine with the living topology to provide the map view akin to the bracket for the NCAA tournament. This transaction map can be used in real-time to make sense of the madness – which makes it the backbone of outage avoidance and problem isolation capabilities. Additionally, BTM enables proactive capacity planning and performance optimization efforts. Together with the more detailed, domain-specific data and statistics captured as part of APM, BTM enables better service level management, change and configuration management and more.

When investigating and evaluating BTM solutions customers often focus their investigation on how transactions are tracked (and thus how the living topology is generated). While the methods of transaction tracking are of paramount importance, of equal importance is how the data and complexity are processed, analyzed and managed server-side. The combination of capturing the right data in a transaction context and being able to analyze that data across multiple dimensions defines BTM as an evolutionary step in APM- one at the center of APMitself.

Millions will enjoy “March Madness” for the few weeks it dominates the American sports landscape. For IT professionals, every day year round brings new and dynamic challenges. BTM provides the map and analysis to bring sanity to the madness.

Uncontrollable changes are happening fast; cloud, mobility, social business, big data to name a few make enterprise IT turn to drugs – gravol and aspirin specifically.

With the disruptive trends surrounding us come with a new wave of complexities for IT operations. Modern businesses  can no longer afford to have any technology glitches or application performance slowdown. No excuses for not being always-on…

To address these challenges – OpTier today released two big announcements:

1)      OpTier rebrands the company and the product to reflect the industry need and commitment for new approach to APM. The new company tagline “Always-on Business” reflects the pervasiveness of computing with cloud, mobility and social that is happening around us forcing businesses to be always-on. The new product name “Always-on APM™” is the technology enabler for an always-on business.

2)      Jointly with the rebranding announcement is the Always-on APM version 4.5 product release. OpTier continues to innovate and give Global, Fortune 1000 enterprises a robust and proven many times over, a business transaction-driven APM technology that is designed for enterprise production and is always-on. 

Let’s look into what we mean by Always-on Business.

Delivering always-on business in today’s world means every user interaction count: wherever users are located geographically, however users interact via web, mobile, tablets, over any IT architecture. It’s about helping enterprises assure immediate user response with faster business transactions and  resistant to the impact of changes while being agile enough to adopt new business opportunities.

Let’s see what the analysts have to say about this:

“IT is currently facing dramatic challenges from three arenas – new technologies such as cloud and mobile; process issues as IT is being forced to evolve beyond its silos towards a more service-centric way of working; and relevance issues, as IT is being asked to demonstrate business value in response to dramatically increasing global competition,” said Dennis Drogseth, a vice president with Enterprise Management Associates.  “OpTier’s always-on business approach can directly address all three challenges given its dynamic currency, its insights into business impacts and business processes, its transaction-centric end-to-end cohesiveness, and its proven ability to work effectively in broader service management ecosystems.”

“OpTier’s rebranding to focus on the always-on nature of its approach to APM aligns well with customer needs to constantly manage and optimize end to end application performance and end user experience inside and outside company firewalls,” stated Mary Johnston Turner, IDC Research Vice President Enterprise Systems Management Software.  She adds, “As business applications become more complex and dynamic, enterprise IT teams need accurate analytics and insight 7×24 in order to keep the business operating effectively.”

Hear it from OpTier:
View OpTier President Mark Thompson’s message on Always-on Business>>

Now, let’s take a peek on what’s new with Always-on APM 4.5. Don’t be fooled by a dot release, OpTier has new game changing capabilities such as bringing the first APM product to market with new business process flow dimension in the same product! As analytics and intelligence is becoming more critical, OpTier continues to lead the market with innovative actionable analytics. And that’s only one key value area of version 4.5! Always-on APM 4.5 provides five key value areas for customers. The diagram below gives you a brief summary of 4.5; business process flow dimension, browser-based user activity tracking, deeper transaction diagnostics, broader platform support–and to wrap it up, cloud capabilities are now part of the product out-of-the-box for application planning, migration and operations.

Overview of Always-on APM 4.5

Let’s see what customers are saying:

“OpTier continues to innovate in the area of application performance management with its differentiated business transaction-driven technology,” said Spencer Holland, strategic architect at Capgemini. “OpTier has provided us with actionable intelligence to help us avoid costly service level breaches and ensure our business is always-on.”

According to Tim Travaille, Chief Information Officer of Frontier Communications, the nation’s largest communications company focused on rural America, “We chose OpTier because Frontier puts the customer first. OpTier will help ensure a great experience for our customers by allowing us to monitor application performance 24/7.”  Frontier’s range of residential and business products include voice, high-speed internet, video, and security solutions in addition to MetroE, VoIP and ROADM networks. “The performance of our offerings is critical to the success of our customers,” Travaille added.  “They rely on us, and we are relying on OpTier to help meet their expectations.”

For a short demo of Always-on APM 4.5 >>

Now, excuse us for a few days – we’re off to our sales kickoff in Las Vegas!

Welcome to the concluding post in our series on the Five Keys to Success with APM in Production Environments.  In this series we have been discussing how the Gartner Magic Quadrant provides a great start to implementing an APM solution.  However, maximizing your APM investment in production hinges on critical capabilities that can make or break an implementation.   To refresh your memory, so far we’ve covered:

• Continuous monitoring, NOT exception-based monitoring
• APM analytics that enable you to become more proactive with application/transaction data
• Smart alerting and real-time analytics impact
• Broad platform support to eliminate blind spots in your monitoring strategy

Now, let’s talk about enterprise scale and readiness

Enterprise Scale and Readiness

If you can check off everything above – you’re in good shape.  But there’s one more factor to success with APM in production.  When you roll out your investment, will it be able to handle the scale of your business today and when it grows?  If you are investing in APM, then you probably have a high-volume of critical transactions to analyze.  Your solution must be able to handle hundreds of millions of transactions per day.  It cannot fail just as transactions sharply rise.  These are precisely the times you need APM!  It goes back to the whole continuous monitoring vs exception-based monitoring argument and ensuring that the solution does not fail at crunch time, or work around limitations by limiting scale of deployment.

You must also be sure that the solution itself can scale easily as your environment grows.  IT is fluid, and APM must be as well.  It’s not just about number of transactions either.  Determine if it’s easy to add tracking on new tiers or is that a whole project unto itself?  Make sure your vendor provides a path to application monitoring expansion that is manageable.

Speaking of manageability, some other key requirements are that the solution should not have a single point of failure, should be able to be remotely configured and should have a high availability.  In addition, whatever method of tracking employed should have low overhead so you that it doesn’t crash once you put it into production.  This is typically a problem for exception-based solutions so keep your eyes on that.

Now, You are Prepared

Ok, now we you have a better handle on the ins and outs of APM in production.  It’s important that you pick a product that not only includes the five dimensions of APM, but also can run effectively in production and provide the right information to you at the right time.  If you look for these requirements during the research phase, you’ll have a much better experience during roll out.  And your solution will provide much more value, both for IT and the enterprise.

Thanks for tuning in during this series.  It’s been great walking through these critical success factors and judging by the traffic it’s helped a few people.  If you have any comments, I would love to hear them in the comment box below, or on twitter @diego_lomanto.

diego_lomanto

This is the fourth of a five part series where we explore the critical factors of implementing APM in production environments successfully.  You can find partsone , two and three here.  Please check back next for part five. 

In this series we are discussing how the Gartner Magic Quadrant provides a great start to implementing with APM solution.  However, maximizing your APM investment in production hinges on critical capabilities that can make or break an implementation.  Capabilities that don’t get as much coverage in the media. They are:

• Continuous monitoring, NOT exception-based monitoring
• APM analytics that enable you to become more proactive with application/transaction data
• Smart alerting and real-time analytics impact
• Broad platform support to eliminate blind spots in your monitoring strategy
• Enterprise readiness for growth and scalability

Part 4 – Broad Platform Support to Eliminate Blind Spots in your Monitoring Strategy

Your APM solution must be adaptable to support everything you have within your environment.  This includes support not just for application processing tiers, but also databases and middleware, as they are often the cause of performance problems.  A complete APM solution should support a diverse mix of SOA, private and public cloud, middleware, databases, homegrown, legacy and proprietary technology stacks. A solution that will thrive in production should also support applications of unknown design/no access to code.  This provides IT with visibility into 3rd-party applications’ and components’ performance and reduces the time to identify performance issues associated with 3rd party applications and components.

At the heart of broad platform support is the capability to  track each and every transaction instance through its entire life cycle. To enable this, there are multiple techniques to track transactions across virtually any application and environment. Some of these innovative technologies include

Active Context Tracking - ACT technology uses lightweight agents to track cross-tier context and the resource utilization of each transaction across the entire datacenter.

APIs – C, C++, C#, or Java APIs for monitoring transaction flow through proprietary and homegrown components.

Network packet capture – Non-intrusive agents capture all network traffic to identify and measure transactions from the network perspective.

Real-time log parsing – For platforms that are not optimal for instrumentation, real-time log file analysis is used to identify information about transaction flow. The log analysis is seamlessly integrated into the transaction model with no overhead

Passive Context Tracking - When a transaction crosses a component that cannot be monitored (for example, a security appliance that cannot be instrumented and does not have logs), passive context tracking is used to stitch together the flow using an ID common to the different parts of the transaction.

When a transaction enters the system, it is identified by one of the agents and then undergoes classification and analytical processing.  An effective production-ready APM solution continues to track the transaction as it traverses web, application, middleware, and database tiers, while collecting performance and resource consumption metrics at each tier. Even when a transaction makes a call to a tier that isn’t monitored, metrics such as the number of calls and the time spent on that tier are captured.

As we discussed in parts one-three, success in production is contingent upon continuously monitoring as much as possible to derive intelligence right when it’s needed.  The capabilities listed above, out of the box, are important part of the always-on discovery and tracking approach to APM. It is important for the speed of deployment and value provided by the APM solution.  If you have wide gaps in coverage, you simply miss too much and have too many blind spots in your monitoring capabilities.

So, that’s the fourth critical requirement.  Coming up next, and closing out the series, is enterprise scalability.

This is the third of a five part series where we explore the critical factors of implementing APM in production environments successfully.  You can find parts one and two here.  Please check back next week for part four. 

In this series we are discussing how the Gartner Magic Quadrant provides a great start to implementing with APM solution.  However, maximizing your APM investment in production hinges on critical capabilities that can make or break an implementation.  Capabilities that don’t get as much coverage in the media. They are:

• Continuous monitoring, NOT exception-based monitoring
• APM analytics that enable you to become more proactive with application/transaction data
• Smart alerting and real-time analytics impact
• Broad platform support eliminating all blind spots in your monitoring strategy
• Enterprise readiness for growth and scalability

Part 3 – Real-time Application Performance Monitoring Analytics

In part two of our series, we explored the value of APM analytics to conduct historical analysis of application performance in the short and long-term.  Now let’s look at how critical it is assess application performance in real-time production environments to ensure success.

Real-time APM analytics allows you to make quick decisions about applications to improve performance, while understanding and quantify the business impact of your actions.  A real-time analysis is typically triggered by a real-time alert from the APM solution, or real-time dashboard within the solution then followed up by a deep-dive into the data using real-time OLAP tools.

Real-time Alerts

APM Alerts can come via e-mail, text or even through a dedicated smartphone application like the one pictured below.

APM real-time alerts sent to a smartphone

Most APM solutions provide alerts.  However, look for one that integrates a “Complex Events Processing” (CEP) engine into the alerting algorithm in production.  Without CEP your alerts from production environments often are without significance.   CEP engines combine multiple events to generate an alert, giving you a thinking engine that can make correlations between different events and tell you something is gone wrong – even if it’s not apparent at first glance. Most application monitoring tools only generate alerts based on IT events, not business events

Real-time Dashboards

Dashboards monitor SLAs or other relevant key metrics that assess the health of your systems.  Here is an examples of an APM real-time dashboards in action:

APM real-time dashboards

 Common Real-Time Triggers

Here is an example of how an alert can be constructed in an APM solution with a complex events processing engine.  In this scenario, all the transactions are being analyzed and the APM solution triggers an alert when business process SLA’s are breached.  This is important because transaction SLA’s are set at the technical system level, not at business level.  So while the transactions may not have breached their SLA, the business process is suffering because of poor performance of many transactions.

Other common triggers:

• Database CPU spikes caused by users with older browsers
• Search responding slowly to one specific query that was not optimized
• Chatty transactions inhibiting cloud-based transaction performance
• Imbalanced cluster of middleware tiers

Real-Time OLAP Engines

Once an alert is triggered or a dashboard reveals a problem in real-time, the next step is to slice and dice and look at the data from different points of view to isolate where the problem is coming from in real time. With real-time OLAP engines, you can view the transaction path, tier performance, end-user perspective, in order to isolate issues.  just to name a few.  .  This is the true power of real-time analytics in production environments. And, it’s the difference between IT saying all KPIs are green and business saying orders falling off.

Some common real-time OLAP findings:

• Business process completed/failed/not completed
• Users were impacted by the current outage
• Locations were impacted by the current slowdown
• Unauthorized users accessing the application
• Topology changes
• Load balancing
• Slow databases
• High CPU resource consumption on specific tiers
• Web services provider underperforming

Tying it all Together

Armed with this knowledge, you can now go solve the problem.  You can even assign business impact to the real-time analysis to prioritize your actions.  For example, through real-time OLAP you can discover that two transactions are beginning to fail.  One of the transactions is responsible for $1M a minute in revenue, and the other is worth $10k.  It’s now easy to figure out what to solve first.

This type of analysis is crucial to operating an APM solution in a production environment effectively.  With so much data available to analysts, it is imperative that not only can they make sense of it, but that they have access to the information as problems arise.  Through a combination of alerts, dashboards and OLAP engines users can effectively monitor their infrastructure proactively.

Next week we’ll discuss broad platform support in part four of the series.

Another year ending with the Annual Gartner Data Center show in Las Vegas! This show has grown quite a bit since I first attended almost a decade ago. I believe over 2500 attendees this year roamed around the Caesars Palace with name badges hanging on the belly—some camouflaged with the fans of the National Finals Rodeo.

This year, the hot topic was not only cloud computing but seems there were a lot of discussions around DevOps and analytics. Other topics of interest included Application Performance Monitoring (APM), End-user Experience (EUE), Business Transaction Management (BTM), Big Data and many more around IT operations.

Take a gander (something like that) at the few bits and bites I picked up, particularly found the polling questions interesting to share.

Poll: what is the biggest reason ITOps group isn’t doing more innovation?

• 41% too busy with day to day operations
• 27% politics
• 11% cultural
• 9% feel they are very innovative
• 7% not a priority
• None or others

Not surprising. When IT is too busy with day to day operations like keeping the lights on, and expensive resources are stuck on a conference bridge figuring who is accountable to fix the problem; of course there is no time and resources put into innovation. Only when IT is proactive and preventive to issues that innovation has a spot on the agenda. Though, 9% feel very innovative (innuh-vaaa-div as rodeos would say) :-)  – I’d be interested in hearing from this 9% to understand what they are working on! Innovation clearly brings new ideas that drive change and create value that can only enable better business outcomes. If you’ve had a positive change in business outcome due to innovation; please do share!

Poll: what is your top priority for availability and performance tool investment for the coming budget year?

• 26% APM
• 21% ECA
• 14% SLA
• 7% Virtualization
• 7% Server Monitoring
• 5% Network Monitoring
• 2% BSM
• 2% Cloud

Indeed APM is hot again, though this is referring to ‘new APM’ which is above and beyond the traditional deep-dive tools. One of the analyst cautioned about using last millennium’s tools to solve today’s problems! Deep-dive is only a slice of five dimensions of APM according to Gartner. Gartner did publish a 2011 APM Magic Quadrant which seven vendors were positioned in the leader’s quadrant (and meeting all five dimensions): OpTier, CA, Compuware, HP, IBM, Opnet and Quest. If you’re in the 26%, here’s a complimentary copy of the APM report.

APM inquiries according to the analysts seem to have increased by over 50% compared to last year. Within APM, end-user experience and business transaction profiling are touted the two hottest topics.

Event Correlation Analysis (ECA) is second to APM, beyond ECA but somewhat related, we see customers looking to apply analytics for both IT and business operations. Approaches such as multi-dimensional OLAP (online analytical processing), CEP (complex event processing) and log analysis are commonly seen. Log parsing and analysis comes up for those looking to parse log files to assist with trouble-shooting primarily. Bringing intelligence through the likes of CEP helps IT elevate its awareness of business impact, prioritization and prevent abnormal behaviors in both IT and business operations. Multi-dimensional OLAP helps bring different perspectives easily and quickly for problem isolation, impact assessment, resolution, optimization and more. For example one can view service levels by business transactions, by users, by applications or flip it around to get resource consumption by applications, users, and transactions—think of a rubik’s cube for IT management.

These all borrow business intelligence concepts for the world of IT management – which is not a bad thing when the ultimate goal is aligning IT and the business.

I am surprised to see only 2% cloud for the next coming year, perhaps the audience weren’t sure what Gartner meant by ‘cloud’ as it could be different extremes of initiatives. Or simply, the audience still isn’t quite ready. Lastly, I can’t say I am surprised to see 2% BSM (Business Service Management); this term has been nebulous for quite some time and since the last pure-play BSM vendor was picked up by Novell — we’re not hearing much from that corner.

DevOps surfaced quite a bit, here’s a couple of interesting polls.

Poll: Is your organization leveraging DevOps?

• 62% have not heard of DevOps before
• 11% aware of DevOps but not planning to use it
• 9% experimenting – not in production
• 8% using for both critical and non-critical apps
• 7% considering using DevOps in next 12-14 months
• 3% using it for non critical apps

This was a surprise – 62% have not heard of DevOps? To defend it, bridging the gap between dev and ops; we’re just not there yet. The reality is more “OpsDev” – how to help IT operation guys bring factual data to the Dev guys to fix issues that are causing pain in production. IT operation needs to be proactive at crossing over that wall. This can only improve productivity, communication and eliminate the traditional siloed approach. There is still some work to do here to break the great wall.

Poll: what process is most in need of being addressed via Devops?

• 36% Release management
• 35% Change management
• 12% performance management
• 12% capacity management
• Others

Not shocking. Change and release management are key processes to address via DevOps – how often do changes cause performance problems? Do you understand the change impact on your end-user experience, critical business transactions, application performance? Effective change and release processes can only be achieved with solid collaboration between dev and ops to minimize application rollbacks, improve quality releases and reduce risks of impacting performance.

Finally, kudos to the Gartner analysts for the informative sessions, one-on-ones, dinners and drinks – they’ve gone the extra mile for attendees and vendors! Usually Gartner will have a write up on the Data Center poll results the following spring; buckle up! it will be interesting to see what they make out of all this!

Until then, have a safe and happy holiday season!