## Sunday, May 20, 2018

### USL Scalability Modeling with Three Parameters

NOTE: Annoyingly, the remote mathjax server often takes it's sweet time rendering LaTex equations (like, maybe a minute!!!). I don't know if this is deliberate on the part of Google or a bug. It used to be faster. If anyone knows, I'd be interested to hear; especially if there is a way to speed it up. And no, I'm not planning to move to WordPress.

### The 2-parameter USL model

The original USL model, presented in my GCAP book and updated in the blog post How to Quantify Scalability, is defined in terms of two fitting parameters $\alpha$ (contention) and $\beta$ (coherency). $$X(N) = \frac{N \, X(1)}{1 + \alpha (N - 1) + \beta N (N - 1)} \label{eqn: usl2}$$

Fitting this nonlinear USL equational model to data requires several steps:

1. normalizing the throughput data, $X$, to determine relative capacity, $C(N)$.
2. equation (\ref{eqn: usl2}) is equivalent to $X(N) = C(N) \, X(1)$.
3. if the $X(1)$ measurement is missing or simply not available—as is often the case with data collected from production systems—the GCAP book describes an elaborate technique for interpolating the value.
The motivation for a 2-parameter model arose out of a desire to meet the twin goals of:
1. providing each term of the USL with a proper physical meaning, i.e., not treat the USL like a conventional multivariate statistical model (statistics is not math)
2. satisfying the von Neumann criterion: minimal number of modeling parameters
Last year, I realized the 2-paramater constraint is actually overly severe. Introducing a third parameter would make the statistical fitting process even more universal, as well as simplify the overall procedure. For the USL particularly, the von Neumann criterion should not be taken too literally. It's really more of a guideline: fewer is generally better. Additionally, Baron Schwarz told me that he'd had better luck fitting production RDBMS data in Excel by substituting a third parameter into the numerator of the USL. As ever, the question remained: How could this actually work?

### The 3-parameter USL model

Going back to equation (\ref{eqn: usl2}), let's just consider the simplest case where scaling is linear-rising, as would be the case for ideal parallelism. In the linear region, where $\alpha = \beta = 0$, equation (\ref{eqn: usl2}) simplifies to $$X(N) = N \, X(1) \label{eqn: usl1}$$

In other words, the overall throughput $X(N)$ increases in simple proportion to $N$. The "single-user" throughput, $X(1)$, doesn't change and therefore acts like a constant of proportionality.

But what happens when we don't know the value of $X(1)$? That means the $X(1)$ factor in equations (\ref{eqn: usl2}) and (\ref{eqn: usl1}) is undefined. We might denote this situation by writing

$$X(N) = N \, ? \label{eqn: uslx}$$

Of course, that makes no sense, mathematically speaking. As already mentioned, the conventional way out of this situation is to estimate the value of $X(1)$ using mathematical interpolation. But here's the epiphany.

Rather than using the more complicated interpolation procedure, we can simply appeal to statistical regression! Yes, that's right, we treat the USL equation as a conventional multivariate statistical model. After all, we're already using nonlinear statistical regression to determine the $\alpha$ and $\beta$ parameters. More importantly, since statistics is not math, we can replace equation ($\ref{eqn: uslx}$) with a statement about correlation, rather than strict equality. In statistical models, that's accomplished by introducing another parameter (I'll call it $\gamma$, since that's the third letter of the Greek alphabet) to replace the question mark in equation ($\ref{eqn: uslx}$), namely

$$X(N) = N \, \gamma \label{eqn: uslg}$$

The new parameter $\gamma$ is just a constant of proportionality that represents the slope of the line associated with ideal parallel scaling. See the plots below.

And here's a little piece of magic. If we choose $N = 1$ in equation ($\ref{eqn: uslg}$), it becomes $X(1) = \gamma$. So, when the $\gamma$ parameter is determined by statistical regression, it also tells us the estimated value of $X(1)$, whether it was measured or missing. In other words, we don't need to do any explicit interpolation because the nonlinear regression procedure does it automatically by fitting the third parameter.

Equation (\ref{eqn: usl2}) is now replaced by a 3-parameter version of the USL model: $$X(N) = \frac{N \, \gamma}{1 + \alpha (N - 1) + \beta N (N - 1)} \label{eqn: usl3}$$

Unlike the 2-parameter USL, equation (\ref{eqn: usl3}) can be fitted directly to your throughput measurements without the need to do any data normalization or interpolation. The following examples show the results of fitting the 3-parameter USL model.

These are load-test data and the "single-user" throughput was measured as $X(1) = 955.16$ per unit time. The 3-parameter USL fit is summarized in the following plot.

The fitted value of $\gamma = 995.65$, which is the estimated value of $X(1)$. It can also be regarded as the slope of the linear-rising throughput indicated by the sloping red line on the left of the plot.

### Production data

These data are from a continuously running production system and thus, no $X(1)$ was ever produced.

The fitted value of $\gamma = 3.22$ is also equivalent to the estimated value of $X(1)$. Similarly, it can be regarded as the slope of the linear-rising throughput on the left of the plot. Interestingly, in these data, $\alpha = 0$, while $beta$ is non-zero. That suggests there is no significant contention in the workload but there is some data exchange coherency at play.

One word of caution. Fitting the 3-parameter USL can be more sensitive to the actual data, especially with a large number of production data scatter points. I'll go into all this, and more, in the upcoming Guerrilla training classes.

## Sunday, April 22, 2018

### The Geometry of Latency

... AKA hyperbolae.

Here's a mnemonic tabulation based on dishes and bowls:

Hopefully this makes amends for the more complicated explanation I wrote for CMG back in 2009 entitled: "Mind Your Knees and Queues: Responding to Hyperbole with Hyperbolæ", which I'm pretty sure almost nobody understood.

## Saturday, April 21, 2018

### Virtual cloudXchange 2018 Conference

Our abstract has been accepted for presentation at the first CMG cloudXchange Virtual Conference to be held on June 19, 2018.

## Exposing the Cost of PerformanceHidden in the Cloud

Neil Gunther
Performance Dynamics, Castro Valley, California

Mohit Chawla
Independent Systems Engineer, Hamburg, Germany

Whilst offering versatile elastic capacity, the cloud also reintroduces an old mainframe concept—chargeback—which rejuvenates the need for performance analysis and capacity planning. Combining production JMX data with an appropriate performance model, we show how to assess fee-based EC2 configurations for a mobile-user application running on a Linux-hosted Tomcat cluster. The performance model also facilitates ongoing cost-benefit analysis of different AWS Auto Scaling policies.

## Wednesday, March 14, 2018

### WTF is Modeling, Anyway?

A conversation with performance and capacity management veteran Boris Zibitsker, on his BEZnext channel, about how to save multiple millions of dollars with a one-line performance model (at 21:50 minutes into the video) that has less than 5% error. I wish my PDQ models were that good. :/

The strength of the model turns out to be its explanatory power, rather than prediction, per se. However, with the correct explanation of the performance problem in hand (which also proved that all other guesses were wrong), this model correctly predicted a 300% reduction in application response time for essentially no cost. Modeling doesn't get much better than this.

### Footnotes

1. According to Computer World in 1999, a 32-node IBM SP2 cost \$2 million to lease over 3 years. This SP2 cluster was about 6 times bigger.
2. Because of my vain attempt to suppress details (in the interests of video length), Boris gets confused about the kind of files that are causing the performance problem (near 26:30 minutes). They're not regular data files and they're not executable files. The executable is already running but sometimes waits—for a long time. The question is, waits for what? They are, in fact, special font files that are requested by the X-windows application (the client, in X parlance). These remote files may also get cached, so it's complicated. In my GCAP class, I have more time to go into this level of detail. Despite all these potential complications, my 'log model' accurately predicts the mean application launch time.
3. Log_2 assumes a binary tree organization of font files whereas, Log_10 assumes a denary tree.
4. Question for the astute viewer. Since these geophysics applications were all developed in-house, how come the developers never saw the performance problems before they ever got into production? Here's a hint.
5. Some ppl have asked why there's no video of me. This was the first time Boris had recorded video of a Skype session and he pushed the wrong button (or something). It's prolly better this way. :P

## Wednesday, February 21, 2018

### CPU Idle Is Not Like White Space

This post seems like it ought to be too trite to write but, I see the following performance gotcha cropping up over and over again.

Under pressure to consolidate resources, usually driven by management and especially regarding processor capacity, there is often an urge to "use up" any idle processor cycles. Idle processor capacity tends to be viewed like it's whitespace on a written page—just begging to be filled up.

The logical equivalent of filling up the "whitespace" is absorbing idle processor capacity by migrating applications that are currently running on other servers and turning those excess servers off or using them for something else.