Guerrilla 2018 Classes Now Open

All Guerrilla training classes are now open for registration.

1. GCAP: Guerrilla Capacity and Performance — From Counters to Containers and Clouds
2. GDAT: Guerrilla Data Analytics — Everything from Linear Regression to Machine Learning
3. PDQW: Pretty Damn Quick Workshop — Personal tuition for performance and capacity mgmt

The following highlights indicate the kind of thing you'll learn. Most especially, how to make better use of all that monitoring and load-testing data you keep collecting.

• How to save millions of dollars with a one-line performance model (video)
• How to minimize chargeback after you lift and shift to the cloud (video)
• How to correctly emulate web traffic on a load-testing rig (PDF)

See what Guerrilla grads are saying about these classes. And how many instructors do you know that are available for you from 9am to 9pm (or later) each day of your class?

Who should attend?

• IT architects
• Application developers
• Performance engineers
• Sysadmins (Linux, Unix, Windows)
• System engineers
• Test engineers
• Mainframe sysops (IBM. Hitachi, Fujitsu, Unisys)
• Database admins
• Devops practitioners
• SRE engineers
• Anyone interested in getting beyond performance monitoring

As usual, Sheraton Four Points has bedrooms available at the Performance Dynamics discounted rate. The room-booking link is on the registration page.

Tell a colleague and see you in September!

Chargeback in the Cloud - The Movie

If you were unable to attend the live presentation on cost-effective defenses against chargeback in the cloud, or simply can't get enough performance and capacity analysis for the AWS cloud (which is completely understandable), here's a direct link to the video recording on CMG's YouTube channel.

The details concerning how you can do this kind of cost-benefit analysis for your cloud applications will be discussed in the upcoming GCAP class and the PDQW workshop. Check the corresponding class registration pages for dates and pricing.

Guerrilla Training September 2017

This year offers a new 3-day class on applying PDQ in your workplace. The classic Guerrilla classes, GCAP and GDAT, are also being presented.

Who should attend?

• architects
• application developers
• performance engineers
• sys admins
• test engineers
• mainframe sysops
• database admins
• webops
• anyone interested in getting beyond ad hoc performance analysis and capacity planning skills

Some course highlights:

• There are only 3 performance metrics you need to know
• How to quantify scalability with the Universal Scalability Law
• Hadoop performance and capacity management
• Virtualization Spectrum from hyper-threads to cloud services
• How to detect bad data
• Statistical forecasting techniques
• Machine learning algorithms applied to performance data

Register online. Early-bird discounts run through the end of August.

As usual, Sheraton Four Points has bedrooms available at the Performance Dynamics discounted rate. Link is on the registration page.

Also see what graduates are saying about these classes.

Tell a colleague and see you in September!

PDQ as a Performance Periscope

This is a guest post by performance modeling enthusiast, Mohit Chawla, who brought the following very interesting example to my attention. In contrast to many of the examples in my Perl::PDQ book, these performance data come from a production web server, not a test rig. —NJG

Performance analysts usually build performance models based on their understanding of the software application's behavior. However, this post describes how a PDQ model acted like a periscope and also turned out to be pedagogical by uncovering otherwise hidden details about the application's inner workings and performance characteristics.

Some Background

2016 Guerrilla Training Schedule 2016

After a six month hiatus working on a major consulting gig, Guerrilla training classes are back in business with the three classic courses: Guerrilla Bootcamp (GBOOT), Guerrilla Capacity Planning (GCAP) and Guerrilla Data Analysis Techniques (GDAT).

See what graduates are saying about these courses.

Some course highlights:

• There are only 3 performance metrics you need to know
• How to quantify scalability with the Universal Scalability Law
• Hadoop performance and capacity management
• Virtualization Spectrum from hyper-threads to cloud services
• How to detect bad data
• Statistical forecasting techniques
• Machine learning algorithms applied to performance data

Register online. Early-bird discounts run through the end of July.

As usual, Sheraton Four Points has bedrooms available at the Performance Dynamics discounted rate.

Tell a friend and see you in September!

Guerrilla Training: New Location

Finally! We have a new location for our Guerrilla training classes in Pleasanton, California: Sheraton Four Points.

We had some complaints last year about noise from the car parks of surrounding restaurants during the night at the previous location. Four Points is much more secluded. It also has its, own restaurant, which some of you will recognize if you've attended previous Guerrilla classes (more than likely, we did lunch and/or dinner there).

The current 2015 schedule and registration page is now posted. The classroom is intimate and only holds about 10-12 people, so book early, book often.

A Little Triplet

Little's law appears in various guises in performance analysis. It was known to Agner Erlang (the father of queueing theory) in 1909 to be intuitively correct but was not proven mathematically until 1961 by John Little. Even though you experience it all the time, queueing is not such a trivial phenomenon as it may seem. In the subsequent discussion, I'll show you that there is actually a triplet of such laws, where each version refers to a slightly different aspect of queueing. Although they have a common general form, the less than obvious interpretation of each version is handy to know for solving almost any problem in performance analysis.

To see the Little's law triplet, consider the line of customers at the grocery store checkout lane shown in Figure 1. Following the usual queueing theory convention, the queue includes not only the customers waiting but also the customer currently in service.

Figure 1. Checkout lane decomposed into its space and time components

As an aside, it is useful to keep in mind that there are only three types of performance metric:

1. Time $T$ (the fundamental performance metric), e.g., minutes
2. Count or a number $N$ (no formal dimensions), e.g., transactions
3. Rate $N/T$ (inverse time dimension), e.g., transactions per minute

How to Remember the Poisson Distribution

The Poisson cumulative distribution function (CDF) \begin{equation} F(α,n) = \sum_{k=0}^n \dfrac{α^k}{k!} \; e^{-α} \label{eqn:pcdf} \end{equation} is the probability of at most $n$ events occurring when the average number of events is α, i.e., $\Pr(X \le n)$. Since \eqref{eqn:pcdf} is a probability function, it cannot have a value greater than 1. In R, the CDF is given by the function ppois(). For example, with α = 4 the first 16 values are

> ppois(0:15,4)
 [1] 0.01831564 0.09157819 0.23810331 0.43347012 0.62883694 0.78513039 0.88932602 0.94886638
 [9] 0.97863657 0.99186776 0.99716023 0.99908477 0.99972628 0.99992367 0.99998007 0.99999511

As the number of events increases from 0 to 15 the CDF approaches 1. See Figure.

Importing an Excel Workbook into R

The usual route for importing data from spreadsheet applications like Excel or OpenOffice into R involves first exporting the data in CSV format. A newer and more efficient CRAN package, called XLConnect (c. 2011), facilitates reading an entire Excel workbook and manipulating worksheets and cells programmatically from within R.

XLConnect doesn't require a running installation of Microsoft Excel or any other special drivers to be able to read and write Excel files. The only requirement is a recent version of a Java Runtime Environment (JRE). Moreover, XLConnect can handle older .xls (BIFF) as well as the newer .xlsx (Office XML) file formats. Internally, XLConnect uses Apache POI (Poor Obfuscation Implementation) to manipulate Microsoft Office documents.

As a simple demonstration, the following worksheet, from a Guerrilla Capacity Planning workbook, will be displayed in R.

First, the Excel workbook is loaded as an R object:

Guerrilla Data Analysis Class, May 19th

This is your fast track to enterprise performance and capacity management with an emphasis on applying R statistical tools to your performance data.

Discounts are available for 3 or more people from the same company. Enquire for details.

All classes are held at our lovely Larkspur Landing location in Pleasanton. Larkspur Landing also provides free Wi-Fi Internet access in their residence-style rooms, as well as our classroom.

Some of the topics covered will include:

• Introduction to using R
• How to Detect Bad Data
• Measurement Errors and Analysis
• Multivariate Linear and Nonlinear Regression
• Quantitative Scalability Analysis
• Statistical forecasting techniques for CaP
• Machine learning algorithms applied to CaP Data
• Wild Not Mild Data: SQL access, web traffic, data recovery
• Taming the Data Torrent with PCA and PerfViz techniques

Attendees should bring their laptops to the class as course materials are provided on a flash drive and calculational tools like OpenOffice, Excel and R will be useful.

Before registering online, take a look at what former students have said about their Guerrilla training experience.

Melbourne's Weather and Cross Correlations

During a lunchtime discussion among recent GCaP class attendees, the topic of weather came up and I casually mentioned that the weather in Melbourne, Australia, can be very changeable because the continent is so old that there is very little geographical relief to moderate the prevailing winds coming from the west.

In general, Melbourne is said to have a mediterranean climate, but it can also be subject to cold blasts of air coming up from Antarctic regions at any time, but especially during the winter. Fortunately, the island state of Tasmania acts as something of a geographical barrier against those winds. Understanding possible relationships between these effects presents an interesting exercise in correlation analysis.

Facebook Meets Florence Nightingale and Enrico Fermi

Highlighting Facebook's mistakes and weaknesses is a popular sport. When you're the 800 lb gorilla of social networking, it's inevitable. The most recent rendition of FB bashing appeared in a serious study entitled, Epidemiological Modeling of Online Social Network Dynamics, authored by a couple of academics in the Department of Mechanical and Aerospace Engineering (???) at Princeton University.

They use epidemiological models to explain adoption and abandonment of social networks, where user adoption is analogous to infection and user abandonment is analogous to recovery from disease, e.g., the precipitous attrition witnessed by MySpace. To this end, they employ variants of an SIR (Susceptible Infected Removed) model to predict a precipitous decline in Facebook activity in the next few years.

Channeling Mark Twain^†, FB engineers lampooned this conclusion by pointing out that Princeton would suffer a similar demise under the same assumptions.

Irrespective of the merits of the Princeton paper, I was impressed that they used an SIR model. It's the same one I used, in R, last year to reinterpret Florence Nightingale's zymotic disease data during the Crimean War as resulting from epidemic spreading.

Another way in which FB was inadvertently dinged by incorrect interpretation of information—this time it was the math—occurred in the 2010 movie, "The Social Network" that tells the story of how FB (then called Facemash) came into being. While watching the movie, I noticed that the ranking metric that gets written on a dorm window (only in Hollywood) is wrong! The correct ranking formula is analogous to the Fermi-Dirac distribution, which is key to understanding how electrons "rank" themselves in atoms and semiconductors.

---

^†"The reports of my death have been greatly exaggerated."

Guerrilla Classes in March 2014

The upcoming GBoot and GCaP training classes are your fast track to enterprise performance and capacity management. You can now register entirely online using either your corporate or personal credit card.

New topics include:

• The effect of think-time settings in load tests
• Closed vs. open workloads in load testing

Classic topics include:

• There are only 3 performance metrics
• How performance metrics are related to each another
• How to quantify scalability with the Universal Scalability Law (USL)
• IT Infrastructure Library (ITIL) for Guerrillas
• The Virtualization Spectrum from hyperthreads to hyperservices

As usual, all classes are held at our lovely Larkspur Landing Pleasanton location in California. Attendees should bring their laptops to the class as course materials are provided on a flash drive. Larkspur Landing also provides free wi-fi Internet in their residence-style rooms as well as the training room.

Response Time Percentiles for Multi-server Applications

In a previous post, I applied my rules-of-thumb for response time (RT) percentiles (or more accurately, residence time in queueing theory parlance), viz., 80th percentile: $R_{80}$, 90th percentile: $R_{90}$ and 95th percentile: $R_{95}$ to a cellphone application and found that the performance measurements were not completely consistent. Since the relevant data only appeared in a journal blog, I didn't have enough information to resolve the discrepancy; which is ok. The first job of the performance analyst is to flag performance anomalies but most probably let others resolve them—after all, I didn't build the system or collect the measurements.

More importantly, that analysis was for a single server application (viz., time-to-first-fix latency). At the end of my post, I hinted at adding percentiles to PDQ for multi-server applications. Here, I present the corresponding rules-of-thumb for the more ubiquitous multi-server or multi-core case.

Single-server Percentiles

First, let's summarize the Guerrilla rules-of-thumb for single-server percentiles (M/M/1 in queueing parlance): \begin{align} R_{1,80} &\simeq \dfrac{5}{3} \, R_{1} \label{eqn:mm1r80}\\ R_{1,90} &\simeq \dfrac{7}{3} \, R_{1}\\ R_{1,95} &\simeq \dfrac{9}{3} \, R_{1} \label{eqn:mm1r95} \end{align} where $R_{1}$ is the statistical mean of the measured or calculated RT and $\simeq$ denotes approximately equal. A useful mnemonic device is to notice the numerical pattern for the fractions. All denominators are 3 and the numerators are successive odd numbers starting with 5.

Laplace the Bayesianista and the Mass of Saturn

I'm reviewing Bayes' theorem and related topics for the upcoming GDAT class. In its simplest form, Bayes' theorem is a statement about conditional probabilities. The probability of A, given that B has occurred, is expressed as: \begin{equation} \Pr(A|B) = \dfrac{\Pr(B|A)\times\Pr(A)}{\Pr(B)} \label{eqn:bayes} \end{equation} In Bayesian language, $\Pr(A|B)$ is called the posterior probability, $\Pr(A)$ the prior probability, and $\Pr(B|A)$ the likelihood (essentially a normalization factor).

Source: Wikipedia

GDAT Class October 14-18, 2013

This is your fast track to enterprise performance and capacity management with an emphasis on applying R statistical tools to your performance data.

Early-bird discounts are available for the Level III Guerrilla Data Analysis Techniques class Oct 14—18.

As usual, all classes are held at our lovely Larkspur Landing location in Pleasanton. Attendees should bring their laptops to the class as course materials are provided on a flash drive. Larkspur Landing also provides free wi-fi Internet in their residence-style rooms as well as the training room.

Adding Percentiles to PDQ

Pretty Damn Quick (PDQ) performs a mean value analysis of queueing network models: mean values in; mean values out. By mean, I mean statistical mean or average. Mean input values include such queueing metrics as service times and arrival rates. These could be sample means. Mean output values include such queueing metrics as waiting time and queue length. These are computed means based on a known distribution. I'll say more about exactly what distribution, shortly. Sometimes you might also want to report measures of dispersion about those mean values, e.g., the 90th or 95th percentiles.

Percentile Rules of Thumb

In The Practical Performance Analyst (1998, 2000) and Analyzing Computer System Performance with Perl::PDQ (2011), I offer the following Guerrilla rules of thumb for percentiles, based on a mean residence time R:

• 80th percentile: p80 ≃ 5R/3
• 90th percentile: p90 ≃ 7R/3
• 95th percentile: p95 ≃ 9R/3

I could also add the 50th percentile or median: p50 ≃ 2R/3, which I hadn't thought of until I was putting this blog post together.

Upcoming GDAT Class May 6-10, 2013

Enrollments are still open for the Level III Guerrilla Data Analysis Techniques class to be held during the week May 6—10. Early-bird discounts are still available. Enquire when you register.

As usual, all classes are held at our lovely Larkspur Landing location. Before registering online, take a look at what former students have said about the Guerrilla courses.

Attendees should bring their laptops, as course materials are provided on CD or flash drive. Larkspur Landing also provides free Internet wi-fi in all rooms.

The Most Important Scatterplot Since Hubble?

In 1929, the astronomer Edwin Hubble published the following scatterplot based on his most recent astronomical measurements.

Figure 1. Edwin Hubble's original scatterplot

It shows the recession velocity of the "stars" (in km/s) on the y-axis and their corresponding distance (in Megaparsecs) on the x-axis. A Megaparsec is about 3.25 million light-years. This scatterplot is important for several reasons:

Harmonic Averaging of Monitored Rate Data

The following slides constitute evolving notes made in response to remarks that arose during the Monitorama Conference in Boston MA, March 28-29, 2013. Since they are evolving, the content will be updated continuously in place. So, get on RSS or Twitter or check back often to read the latest version.

During the Graphite workshop session at Monitorama, the topic of aggregating monitored rate data came up. This caused me to interject the cautionary comment: