|By Michael Kopp||
|December 12, 2012 07:30 AM EST||
Anyone who ever monitored or analyzed an application uses or has used averages. They are simple to understand and calculate. We tend to ignore just how wrong the picture is that averages paint of the world. To emphasis the point let me give you a real-world example outside of the performance space that I read recently in a newspaper.
The article was explaining that the average salary in a certain region in Europe was 1900 Euro's (to be clear this would be quite good in that region!). However when looking closer they found out that the majority, namely 9 out of 10 people, only earned around 1000 Euros and one would earn 10.000 (I over simplified this of course, but you get the idea). If you do the math you will see that the average of this is indeed 1900, but we can all agree that this does not represent the "average" salary as we would use the word in day to day live. So now let's apply this thinking to application performance.
The Average Response Time
The average response time is by far the most commonly used metric in application performance management. We assume that this represents a "normal" transaction, however this would only be true if the response time is always the same (all transaction run at equal speed) or the response time distribution is roughly bell curved.
A Bell curve represents the "normal" distribution of response times in which the average and the median are the same. It rarely ever occurs in real applications
In a Bell Curve the average (mean) and median are the same. In other words observed performance would represent the majority (half or more than half) of the transactions.
In reality most applications have few very heavy outliers; a statistician would say that the curve has a long tail. A long tail does not imply many slow transactions, but few that are magnitudes slower than the norm.
This is a typical Response Time Distribution with few but heavy outliers - it has a long tail. The average here is dragged to the right by the long tail.
We recognize that the average no longer represents the bulk of the transactions but can be a lot higher than the median.
You can now argue that this is not a problem as long as the average doesn't look better than the median. I would disagree, but let's look at another real-world scenario experienced by many of our customers:
This is another typical Response Time Distribution. Here we have quite a few very fast transactions that drag the average to the left of the actual median
In this case a considerable percentage of transactions are very, very fast (10-20 percent), while the bulk of transactions are several times slower. The median would still tell us the true story, but the average all of a sudden looks a lot faster than most of our transactions actually are. This is very typical in search engines or when caches are involved - some transactions are very fast, but the bulk are normal. Another reason for this scenario are failed transactions, more specifically transactions that failed fast. Many real-world applications have a failure rate of 1-10 percent (due to user errors or validation errors). These failed transactions are often magnitudes faster than the real ones and consequently distorted an average.
Of course performance analysts are not stupid and regularly try to compensate with higher frequency charts (compensating by looking at smaller aggregates visually) and by taking in minimum and maximum observed response times. However we can often only do this if we know the application very well, those unfamiliar with the application might easily misinterpret the charts. Because of the depth and type of knowledge required for this, it's difficult to communicate your analysis to other people - think how many arguments between IT teams have been caused by this. And that's before we even begin to think about communicating with business stakeholders!
A better metric by far are percentiles, because they allow us to understand the distribution. But before we look at percentiles, let's take a look a key feature in every production monitoring solution: Automatic Baselining and Alerting.
Automatic Baselining and Alerting
In real-world environments, performance gets attention when it is poor and has a negative impact on the business and users. But how can we identify performance issues quickly to prevent negative effects? We cannot alert on every slow transaction, since there are always some. In addition, most operations teams have to maintain a large number of applications and are not familiar with all of them, so manually setting thresholds can be inaccurate, quite painful and time-consuming.
The industry has come up with a solution called Automatic Baselining. Baselining calculates out the "normal" performance and only alerts us when an application slows down or produces more errors than usual. Most approaches rely on averages and standard deviations.
Without going into statistical details, this approach again assumes that the response times are distributed over a bell curve:
The Standard Deviation represents 33% of all transactions with the mean as the middle. 2xStandard Deviation represents 66% and thus the majority, everything outside could be considered an outlier. However most real world scenarios are not bell curved...
Typically, transactions that are outside two times standard deviation are treated as slow and captured for analysis. An alert is raised if the average moves significantly. In a bell curve this would account for the slowest 16.5 percent (and you can of course adjust that); however; if the response time distribution does not represent a bell curve, it becomes inaccurate. We either end up with a lot of false positives (transactions that are a lot slower than the average but when looking at the curve lie within the norm) or we miss a lot of problems (false negatives). In addition if the curve is not a bell curve, then the average can differ a lot from the median; applying a standard deviation to such an average can lead to quite a different result than you would expect. To work around this problem these algorithms have many tunable variables and a lot of "hacks" for specific use cases.
Why I Love Percentiles
A percentile tells me which part of the curve I am looking at and how many transactions are represented by that metric. To visualize this look at the following chart:
This chart shows the 50th and 90th percentile along with the average of the same transaction. It shows that the average is influenced far mor heavily by the 90th, thus by outliers and not by the bulk of the transactions
The green line represents the average. As you can see it is very volatile. The other two lines represent the 50th and 90th percentile. As we can see the 50th percentile (or median) is rather stable but has a couple of jumps. These jumps represent real performance degradation for the majority (50%) of the transactions. The 90th percentile (this is the start of the "tail") is a lot more volatile, which means that the outliers slowness depends on data or user behavior. What's important here is that the average is heavily influenced (dragged) by the 90th percentile, the tail, rather than the bulk of the transactions.
If the 50th percentile (median) of a response time is 500ms that means that 50% of my transactions are either as fast or faster than 500ms. If the 90th percentile of the same transaction is at 1000ms it means that 90% are as fast or faster and only 10% are slower. The average in this case could either be lower than 500ms (on a heavy front curve), a lot higher (long tail) or somewhere in between. A percentile gives me a much better sense of my real world performance, because it shows me a slice of my response time curve.
For exactly that reason percentiles are perfect for automatic baselining. If the 50th percentile moves from 500ms to 600ms I know that 50% of my transactions suffered a 20% performance degradation. You need to react to that.
In many cases we see that the 75th or 90th percentile does not change at all in such a scenario. This means the slow transactions didn't get any slower, only the normal ones did. Depending on how long your tail is the average might not have moved at all in such a scenario.!
In other cases we see the 98th percentile degrading from 1s to 1.5 seconds while the 95th is stable at 900ms. This means that your application as a whole is stable, but a few outliers got worse, nothing to worry about immediately. Percentile-based alerts do not suffer from false positives, are a lot less volatile and don't miss any important performance degradations! Consequently a baselining approach that uses percentiles does not require a lot of tuning variables to work effectively.
The screenshot below shows the Median (50th Percentile) for a particular transaction jumping from about 50ms to about 500ms and triggering an alert as it is significantly above the calculated baseline (green line). The chart labeled "Slow Response Time" on the other hand shows the 90th percentile for the same transaction. These "outliers" also show an increase in response time but not significant enough to trigger an alert.
Here we see an automatic baselining dashboard with a violation at the 50th percentile. The violation is quite clear, at the same time the 90th percentile (right upper chart) does not violate. Because the outliers are so much slower than the bulk of the transaction an average would have been influenced by them and would not have have reacted quite as dramatically as the 50th percentile. We might have missed this clear violation!
How Can We Use Percentiles for Tuning?
Percentiles are also great for tuning, and giving your optimizations a particular goal. Let's say that something within my application is too slow in general and I need to make it faster. In this case I want to focus on bringing down the 90th percentile. This would ensure sure that the overall response time of the application goes down. In other cases I have unacceptably long outliers I want to focus on bringing down response time for transactions beyond the 98th or 99th percentile (only outliers). We see a lot of applications that have perfectly acceptable performance for the 90th percentile, with the 98th percentile being magnitudes worse.
In throughput oriented applications on the other hand I would want to make the majority of my transactions very fast, while accepting that an optimization makes a few outliers slower. I might therefore make sure that the 75th percentile goes down while trying to keep the 90th percentile stable or not getting a lot worse.
I could not make the same kind of observations with averages, minimum and maximum, but with percentiles they are very easy indeed.
Averages are ineffective because they are too simplistic and one-dimensional. Percentiles are a really great and easy way of understanding the real performance characteristics of your application. They also provide a great basis for automatic baselining, behavioral learning and optimizing your application with a proper focus. In short, percentiles are great!
|rtalexander 11/21/12 12:58:00 AM EST|
Hey, could you post a reference or two that covers the theory and/or practicalities of the approach you describe?
There will be new vendors providing applications, middleware, and connected devices to support the thriving IoT ecosystem. This essentially means that electronic device manufacturers will also be in the software business. Many will be new to building embedded software or robust software. This creates an increased importance on software quality, particularly within the Industrial Internet of Things where business-critical applications are becoming dependent on products controlled by software. Qua...
Jul. 26, 2016 06:15 AM EDT Reads: 1,406
SYS-CON Events has announced today that Roger Strukhoff has been named conference chair of Cloud Expo and @ThingsExpo 2016 Silicon Valley. The 19th Cloud Expo and 6th @ThingsExpo will take place on November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. "The Internet of Things brings trillions of dollars of opportunity to developers and enterprise IT, no matter how you measure it," stated Roger Strukhoff. "More importantly, it leverages the power of devices and the Interne...
Jul. 26, 2016 05:15 AM EDT Reads: 2,058
Large scale deployments present unique planning challenges, system commissioning hurdles between IT and OT and demand careful system hand-off orchestration. In his session at @ThingsExpo, Jeff Smith, Senior Director and a founding member of Incenergy, will discuss some of the key tactics to ensure delivery success based on his experience of the last two years deploying Industrial IoT systems across four continents.
Jul. 26, 2016 05:00 AM EDT Reads: 1,525
CenturyLink has announced that application server solutions from GENBAND are now available as part of CenturyLink’s Networx contracts. The General Services Administration (GSA)’s Networx program includes the largest telecommunications contract vehicles ever awarded by the federal government. CenturyLink recently secured an extension through spring 2020 of its offerings available to federal government agencies via GSA’s Networx Universal and Enterprise contracts. GENBAND’s EXPERiUS™ Application...
Jul. 26, 2016 03:45 AM EDT Reads: 1,830
The Internet of Things will challenge the status quo of how IT and development organizations operate. Or will it? Certainly the fog layer of IoT requires special insights about data ontology, security and transactional integrity. But the developmental challenges are the same: People, Process and Platform. In his session at @ThingsExpo, Craig Sproule, CEO of Metavine, demonstrated how to move beyond today's coding paradigm and shared the must-have mindsets for removing complexity from the develo...
Jul. 26, 2016 02:00 AM EDT Reads: 1,335
SYS-CON Events announced today that MangoApps will exhibit at the 19th International Cloud Expo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. MangoApps provides modern company intranets and team collaboration software, allowing workers to stay connected and productive from anywhere in the world and from any device.
Jul. 26, 2016 01:45 AM EDT Reads: 1,313
The IETF draft standard for M2M certificates is a security solution specifically designed for the demanding needs of IoT/M2M applications. In his session at @ThingsExpo, Brian Romansky, VP of Strategic Technology at TrustPoint Innovation, explained how M2M certificates can efficiently enable confidentiality, integrity, and authenticity on highly constrained devices.
Jul. 26, 2016 01:30 AM EDT Reads: 1,001
The 19th International Cloud Expo has announced that its Call for Papers is open. Cloud Expo, to be held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA, brings together Cloud Computing, Big Data, Internet of Things, DevOps, Digital Transformation, Microservices and WebRTC to one location. With cloud computing driving a higher percentage of enterprise IT budgets every year, it becomes increasingly important to plant your flag in this fast-expanding business opportuni...
Jul. 26, 2016 01:15 AM EDT Reads: 2,535
In today's uber-connected, consumer-centric, cloud-enabled, insights-driven, multi-device, global world, the focus of solutions has shifted from the product that is sold to the person who is buying the product or service. Enterprises have rebranded their business around the consumers of their products. The buyer is the person and the focus is not on the offering. The person is connected through multiple devices, wearables, at home, on the road, and in multiple locations, sometimes simultaneously...
Jul. 26, 2016 12:45 AM EDT Reads: 649
“delaPlex Software provides software outsourcing services. We have a hybrid model where we have onshore developers and project managers that we can place anywhere in the U.S. or in Europe,” explained Manish Sachdeva, CEO at delaPlex Software, in this SYS-CON.tv interview at @ThingsExpo, held June 7-9, 2016, at the Javits Center in New York City, NY.
Jul. 26, 2016 12:00 AM EDT Reads: 1,545
From wearable activity trackers to fantasy e-sports, data and technology are transforming the way athletes train for the game and fans engage with their teams. In his session at @ThingsExpo, will present key data findings from leading sports organizations San Francisco 49ers, Orlando Magic NBA team. By utilizing data analytics these sports orgs have recognized new revenue streams, doubled its fan base and streamlined costs at its stadiums. John Paul is the CEO and Founder of VenueNext. Prior ...
Jul. 25, 2016 11:15 PM EDT Reads: 2,020
"We've discovered that after shows 80% if leads that people get, 80% of the conversations end up on the show floor, meaning people forget about it, people forget who they talk to, people forget that there are actual business opportunities to be had here so we try to help out and keep the conversations going," explained Jeff Mesnik, Founder and President of ContentMX, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
Jul. 25, 2016 11:15 PM EDT Reads: 1,317
Internet of @ThingsExpo, taking place November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with the 19th International Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world and ThingsExpo Silicon Valley Call for Papers is now open.
Jul. 25, 2016 10:00 PM EDT Reads: 2,521
The IoT is changing the way enterprises conduct business. In his session at @ThingsExpo, Eric Hoffman, Vice President at EastBanc Technologies, discussed how businesses can gain an edge over competitors by empowering consumers to take control through IoT. He cited examples such as a Washington, D.C.-based sports club that leveraged IoT and the cloud to develop a comprehensive booking system. He also highlighted how IoT can revitalize and restore outdated business models, making them profitable ...
Jul. 25, 2016 08:30 PM EDT Reads: 1,946
With 15% of enterprises adopting a hybrid IT strategy, you need to set a plan to integrate hybrid cloud throughout your infrastructure. In his session at 18th Cloud Expo, Steven Dreher, Director of Solutions Architecture at Green House Data, discussed how to plan for shifting resource requirements, overcome challenges, and implement hybrid IT alongside your existing data center assets. Highlights included anticipating workload, cost and resource calculations, integrating services on both sides...
Jul. 25, 2016 08:00 PM EDT Reads: 1,988
Big Data engines are powering a lot of service businesses right now. Data is collected from users from wearable technologies, web behaviors, purchase behavior as well as several arbitrary data points we’d never think of. The demand for faster and bigger engines to crunch and serve up the data to services is growing exponentially. You see a LOT of correlation between “Cloud” and “Big Data” but on Big Data and “Hybrid,” where hybrid hosting is the sanest approach to the Big Data Infrastructure pro...
Jul. 25, 2016 07:30 PM EDT Reads: 1,897
"We are a well-established player in the application life cycle management market and we also have a very strong version control product," stated Flint Brenton, CEO of CollabNet,, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
Jul. 25, 2016 07:15 PM EDT Reads: 1,807
We all know the latest numbers: Gartner, Inc. forecasts that 6.4 billion connected things will be in use worldwide in 2016, up 30 percent from last year, and will reach 20.8 billion by 2020. We're rapidly approaching a data production of 40 zettabytes a day – more than we can every physically store, and exabytes and yottabytes are just around the corner. For many that’s a good sign, as data has been proven to equal money – IF it’s ingested, integrated, and analyzed fast enough. Without real-ti...
Jul. 25, 2016 07:15 PM EDT Reads: 1,034
I wanted to gather all of my Internet of Things (IOT) blogs into a single blog (that I could later use with my University of San Francisco (USF) Big Data “MBA” course). However as I started to pull these blogs together, I realized that my IOT discussion lacked a vision; it lacked an end point towards which an organization could drive their IOT envisioning, proof of value, app dev, data engineering and data science efforts. And I think that the IOT end point is really quite simple…
Jul. 25, 2016 06:30 PM EDT Reads: 1,033
A critical component of any IoT project is what to do with all the data being generated. This data needs to be captured, processed, structured, and stored in a way to facilitate different kinds of queries. Traditional data warehouse and analytical systems are mature technologies that can be used to handle certain kinds of queries, but they are not always well suited to many problems, particularly when there is a need for real-time insights.
Jul. 25, 2016 06:15 PM EDT Reads: 1,784