Thinking Clearly about Performance

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Performance

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September 1, 2010 Volume 8, issue 9

Thinking Clearly about Performance

Improving the performance of complex software is difficult, but understanding some fundamental principles can make it easier.

Cary Millsap, Method R Corporation

When I joined Oracle Corporation in 1989, performance—what everyone called "Oracle tuning"—was difficult. Only a few people claimed they could do it very well, and those people commanded high consulting rates. When circumstances thrust me into the "Oracle tuning" arena, I was quite unprepared. Recently, I've been introduced to the world of "MySQL tuning," and the situation seems very similar to what I saw in Oracle more than 20 years ago.

It reminds me a lot of how difficult beginning algebra seemed when I was about 13 years old. At that age, I had to appeal heavily to trial and error to get through. I can remember looking at an equation such as 3x + 4 = 13 and basically stumbling upon the answer, x = 3.

The trial-and-error method worked—albeit slowly and uncomfortably—for easy equations, but it didn't scale as the problems got tougher—for example, 3x + 4 = 14. Now what? My problem was that I wasn't thinking clearly yet about algebra. My introduction at age 15 to teacher James R. Harkey put me on the road to solving that problem.

In high school Mr. Harkey taught us what he called an axiomatic approach to solving algebraic equations. He showed us a set of steps that worked every time (and he gave us plenty of homework to practice on). In addition, by executing those steps, we necessarily documented our thinking as we worked. Not only were we thinking clearly, using a reliable and repeatable sequence of steps, but we were also proving to anyone who read our work that we were thinking clearly.

Our work for Mr. Harkey looked like this:

This was Mr. Harkey's axiomatic approach to algebra, geometry, trigonometry, and calculus: one small, logical, provable, and auditable step at a time. It's the first time I ever really got mathematics.

Naturally, I didn't realize it at the time, but of course proving was a skill that would be vital for my success in the world after school. In life I've found that, of course, knowing things matters, but proving those things to other people matters more. Without good proving skills, it's difficult to be a good consultant, a good leader, or even a good employee.

My goal since the mid-1990s has been to create a similarly rigorous approach to Oracle performance optimization. Lately, I have been expanding the scope of that goal beyond Oracle to: "Create an axiomatic approach to computer software performance optimization." I've found that not many people like it when I talk like that, so let's say it like this: "My goal is to help you think clearly about how to optimize the performance of your computer software."

What is Performance?

Googling the word performance results in more than a half-billion hits on concepts ranging from bicycle racing to the dreaded employee review process that many companies these days are learning to avoid. Most of the top hits relate to the subject of this article: the time it takes for computer software to perform whatever task you ask it to do.

And that's a great place to begin: the task, a business-oriented unit of work. Tasks can nest: "print invoices" is a task; "print one invoice"—a subtask—is also a task. For a computer user, performance usually means the time it takes for the system to execute some task. Response time is the execution duration of a task, measured in time per task, such as "seconds per click." For example, my Google search for the word performance had a response time of 0.24 seconds. The Google Web page rendered that measurement right in my browser. That is evidence to me that Google values my perception of Google performance.

Some people are interested in another performance measure: throughput, the count of task executions that complete within a specified time interval, such as "clicks per second." In general, people who are responsible for the performance of groups of people worry more about throughput than does the person who works in a solo contributor role. For example, an individual accountant is usually more concerned about whether the response time of a daily report will require that accountant to stay late after work. The manager of a group of accounts is also concerned about whether the system is capable of processing all the data that all of the accountants in that group will be processing.

Response Time versus Throughput

Throughput and response time have a generally reciprocal type of relationship, but not exactly. The real relationship is subtly complex.

Example 1. Imagine that you have measured your throughput at 1,000 tasks per second for some benchmark. What, then, is your users' average response time? It's tempting to say that the average response time is 1/1,000 = .001 seconds per task, but it's not necessarily so.

Imagine that the system processing this throughput had 1,000 parallel, independent, homogeneous service channels (that is, it's a system with 1,000 independent, equally competent service providers, each awaiting your request for service). In this case, it is possible that each request consumed exactly 1 second.

Now, you can know that average response time was somewhere between 0 and 1 second per task. You cannot derive response time exclusively from a throughput measurement, however; you have to measure it separately (I carefully include the word exclusively in this statement, because there are mathematical models that can compute response time for a given throughput, but the models require more input than just throughput).

The subtlety works in the other direction, too. You can certainly flip this example around and prove it. A scarier example, however, will be more fun.

Example 2. Your client requires a new task that you're programming to deliver a throughput of 100 tasks per second on a single-CPU computer. Imagine that the new task you've written executes in just .001 seconds on the client's system. Will your program yield the throughput that the client requires?

It's tempting to say that if you can run the task once in just a thousandth of a second, then surely you'll be able to run that task at least 100 times in the span of a full second. And you're right, if the task requests are nicely serialized, for example, so that your program can process all 100 of the client's required task executions inside a loop, one after the other.

But what if the 100 tasks per second come at your system at random, from 100 different users logged into your client's single-CPU computer? Then the gruesome realities of CPU schedulers and serialized resources (such as Oracle latches and locks and writable access to buffers in memory) may restrict your throughput to quantities much less than the required 100 tasks per second. It might work; it might not. You cannot derive throughput exclusively from a response time measurement. You have to measure it separately.

Response time and throughput are not necessarily reciprocals. To know them both, you need to measure them both. Which is more important? For a given situation, you might answer legitimately in either direction. In many circumstances, the answer is that both are vital measurements requiring management. For example, a system owner may have a business requirement not only that response time must be 1.0 second or less for a given task in 99 percent or more of executions but also that the system must support a sustained throughput of 1,000 executions of the task within a 10-minute interval.

Percentile Specifications

In the prior section, I used the phrase "in 99 percent or more of executions" to qualify a response time expectation. Many people are more accustomed to such statements as "average response time must be r seconds or less." The percentile way of stating requirements maps better, though, to the human experience.

Example 3. Imagine that your response time tolerance is 1 second for some task that you execute on your computer every day. Imagine further that the lists of numbers shown in table 1 represent the measured response times of 10 executions of that task. The average response time for each list is 1.000 second. Which one do you think you would like better?

Although the two lists in table 1 have the same average response time, the lists are quite different in character. In list A, 90 percent of response times were one second or less. In list B, only 60 percent of response times were one second or less. Stated in the opposite way, list B represents a set of user experiences of which 40 percent were dissatisfactory, but list A (having the same average response time as list B) represents only a 10 percent dissatisfaction rate.

In list A, the 90th percentile response time is .987 seconds; in list B, it is 1.273 seconds. These statements about percentiles are more informative than merely saying that each list represents an average response time of 1.000 second.

As GE says, "Our customers feel the variance, not the mean."⁴ Expressing response-time goals as percentiles makes for much more compelling requirement specifications that match with end-user expectations: for example, the "Track Shipment" task must complete in less than .5 second in at least 99.9 percent of executions.

Problem Diagnosis

In nearly every performance problem I've been invited to repair, the stated problem has been about response time: "It used to take less than a second to do X; now it sometimes takes 20+." Of course, a specific statement like that is often buried under veneers of other problems such as: "Our whole [adjectives deleted] system is so slow we can't use it."⁸

Just because something has happened a lot for me doesn't mean that it will happen for you. The most important thing for you to do first is state the problem clearly, so that you can then think about it clearly.

A good way to begin is to ask, what is the goal state that you want to achieve? Find some specifics that you can measure to express this: for example, "Response time of X is more than 20 seconds in many cases. We'll be happy when response time is one second or less in at least 95 percent of executions." That sounds good in theory, but what if your user doesn't have such a specific quantitative goal? This particular goal has two quantities (1 and 95); what if your user doesn't know either one of them? Worse yet, what if your user does have specific ideas, but those expectations are impossible to meet? How would you know what "possible" or "impossible" even is?

Let's work our way up to those questions.

The Sequence Diagram

A sequence diagram is a type of graph specified in UML (Unified Modeling Language), used to show the interactions between objects in the sequential order that those interactions occur. The sequence diagram is an exceptionally useful tool for visualizing response time. Figure 1 shows a standard UML sequence diagram for a simple application system composed of a browser, application server, and a database.

Imagine now drawing the sequence diagram to scale, so that the distance between each "request" arrow coming in and its corresponding "response" arrow going out is proportional to the duration spent servicing the request. I've shown such a diagram in Figure 2. This is a good graphical representation of how the components represented in your diagram are spending your user's time. You can "feel" the relative contribution to response time by looking at the picture.

Sequence diagrams are just right for helping people conceptualize how their responses are consumed on a given system, as one tier hands control of the task to the next. Sequence diagrams also work well to show how simultaneous processing threads work in parallel, and they are good tools for analyzing performance outside of the information technology business.¹⁰

The sequence diagram is a good conceptual tool for talking about performance, but to think clearly about performance, you need something else. Here's the problem. Imagine that the task you're supposed to fix has a response time of 2,468 seconds (41 minutes, 8 seconds). In that period of time, running that task causes your application server to execute 322,968 database calls. Figure 3 shows what the sequence diagram for that task would look like.

There are so many request and response arrows between the application and database tiers that you can't see any of the detail. Printing the sequence diagram on a very long scroll isn't a useful solution, because it would take weeks of visual inspection before you would be able to derive useful information from the details you would see.

The sequence diagram is a good tool for conceptualizing flow of control and the corresponding flow of time. To think clearly about response time, however, you need something else.

The Profile

The sequence diagram doesn't scale well. To deal with tasks that have huge call counts, you need a convenient aggregation of the sequence diagram so that you understand the most important patterns in how your time has been spent. Table 2 shows an example of a profile, which does the trick. A profile is a tabular decomposition of response time, typically listed in descending order of component response time contribution.

Example 4. The profile in table 2 is rudimentary, but it shows exactly where your slow task has spent your user's 2,468 seconds. With the data shown here, for example, you can derive the percentage of response time contribution for each of the function calls identified in the profile. You can also derive the average response time for each type of function call during your task.

A profile shows where your code has spent your time and—sometimes even more importantly—where it has not. There is tremendous value in not having to guess about these things.

From the data shown in table 2, you know that 70.8 percent of your user's response time is consumed by DB:fetch() calls. Furthermore, if you can drill down into the individual calls whose durations were aggregated to create this profile, you can know how many of those App:await_db_netIO() calls corresponded to DB:fetch() calls, and you can know how much response time each of those consumed. With a profile, you can begin to formulate the answer to the question, "How long should this task run?" which, by now, you know is an important question in the first step of any good problem diagnosis.

Amdahl's Law

Profiling helps you think clearly about performance. Even if Gene Amdahl hadn't given us Amdahl's law back in 1967, you would probably have come up with it yourself after the first few profiles you looked at.

Amdahl's law states: Performance improvement is proportional to how much a program uses the thing you improved. If the thing you're trying to improve contributes only 5 percent to your task's total response time, then the maximum impact you'll be able to make is 5 percent of your total response time. This means that the closer to the top of a profile that you work (assuming that the profile is sorted in descending response-time order), the bigger the benefit potential for your overall response time.

This doesn't mean that you always work through a profile in top-down order, though, because you also need to consider the cost of the remedies you'll be executing.⁹

Example 5. Consider the profile in Table 3. It's the same profile as in Table 2, except here you can see how much time you think you can save by implementing the best remedy for each row in the profile, and you can see how much you think each remedy will cost to implement.

Which remedy would you implement first? Amdahl's law says that implementing the repair on line 1 has the greatest potential benefit of saving about 851 seconds (34.5 percent of 2,468 seconds). If it is truly "super expensive," however, then the remedy on line 2 may yield better net benefit—and that's the constraint to which you really need to optimize—even though the potential for response time savings is only about 305 seconds.

A tremendous value of the profile is that you can learn exactly how much improvement you should expect for a proposed investment. It opens the door to making much better decisions about what remedies to implement first. Your predictions give you a yardstick for measuring your own performance as an analyst. Finally, it gives you a chance to showcase your cleverness and intimacy with your technology as you find more efficient remedies for reducing response time more than expected, at lower-than-expected costs.

What remedy action you implement first really boils down to how much you trust your cost estimates. Does "dirt cheap" really take into account the risks that the proposed improvement may inflict upon the system? For example, it may seem dirt cheap to change that parameter or drop that index, but does that change potentially disrupt the good performance behavior of something out there that you're not even thinking about right now? Reliable cost estimation is another area in which your technological skills pay off.

Another factor worth considering is the political capital that you can earn by creating small victories. Maybe cheap, low-risk improvements won't amount to much overall response-time improvement, but there's value in establishing a track record of small improvements that exactly fulfill your predictions about how much response time you'll save for the slow task. A track record of prediction and fulfillment ultimately—especially in the area of software performance, where myth and superstition have reigned at many locations for decades—gives you the credibility you need to influence your colleagues (your peers, your managers, your customers...) to let you perform increasingly expensive remedies that may produce bigger payoffs for the business.

A word of caution, however: don't get careless as you rack up successes and propose ever-bigger, costlier, riskier remedies. Credibility is fragile. It takes a lot of work to build it up but only one careless mistake to bring it down.

Skew

When you work with profiles, you repeatedly encounter sub-problems such as this:

Example 6. The profile in table 2 revealed that 322,968 DB: fetch() calls had consumed 1,748.229 seconds of response time. How much unwanted response time would be eliminated if you could eliminate half of those calls? The answer is almost never, "Half of the response time." Consider this far simpler example for a moment:

Example 7. Four calls to a subroutine consumed four seconds. How much unwanted response time would be eliminated if you could eliminate half of those calls? The answer depends upon the response times of the individual calls that we could eliminate. You might have assumed that each of the call durations was the average 4/4 = 1 second, but nowhere did the statement tell you that the call durations were uniform.

Example 8. Imagine the following two possibilities, where each list represents the response times of the four subroutine calls:

A = {1, 1, 1, 1}

B = {3.7, .1, .1, .1}

In list A, the response times are uniform, so no matter which half (two) of the calls you eliminate, you will reduce total response time to two seconds. In list B, however, it makes a tremendous difference which two calls are eliminated. If you eliminate the first two calls, then the total response time will drop to .2 seconds (a 95 percent reduction). If you eliminate the final two calls, then the total response time will drop to 3.8 seconds (only a 5 percent reduction).

Skew is a nonuniformity in a list of values. The possibility of skew is what prohibits you from providing a precise answer to the question I asked at the beginning of this section. Let's look again:

Example 9. The profile in table 2 revealed that 322,968 DB: fetch() calls had consumed 1,748.229 seconds of response time. How much unwanted response time would you eliminate by eliminating half of those calls? Without knowing anything about skew, the most precise answer you can provide is, "Somewhere between 0 and 1,748.229 seconds."

Imagine, however, that you had the additional information available in table 4. Then you could formulate much more precise best-case and worst-case estimates. Specifically, if you had information like this, you would be smart to try to figure out how to specifically eliminate the 47,444 calls with response times in the .01- to .1-second range.

Minimizing Risk

A couple of sections back I mentioned the risk that repairing the performance of one task can damage the performance of another. This reminds me of something that happened to me once in Denmark. It's a quick story:

Scene: The kitchen table in Måløv, Denmark; the oak table, in fact, of Oak Table Network fame, a network of Oracle practitioners who believe in using scientific methods to improve the development and administration of Oracle-based systems.¹² Roughly 10 people sit around the table, working on their laptops and conducting various conversations.

Cary: Guys, I'm burning up. Would you mind if I opened the window for a little bit to let some cold air in?

Carel-Jan: Why don't you just take off your heavy sweater?

The End.

There's a general principle at work here that humans who optimize know: when everyone is happy except for you, make sure your local stuff is in order before you go messing around with the global stuff that affects everyone else, too.

This principle is why I flinch whenever someone proposes to change a system's Oracle SQL*Net packet size when the problem is really a couple of badly written Java programs that make unnecessarily many database calls (and, hence, unnecessarily many network I/O calls as well). If everybody is getting along fine except for the user of one or two programs, then the safest solution to the problem is a change whose scope is localized to just those one or two programs.

Efficiency

Even if everyone on the entire system is suffering, you should still focus first on the program that the business needs fixed first. The way to begin is to ensure that the program is working as efficiently as it can. Efficiency is the inverse of how much of a task execution's total service time can be eliminated without adding capacity and without sacrificing required business function.

In other words, efficiency is an inverse measure of waste. Here are some examples of waste that commonly occur in the database application world:

• A middle-tier program creates a distinct SQL statement for every row it inserts into the database. It executes 10,000 database prepare calls (and thus 10,000 network I/O calls) when it could have accomplished the job with one prepare call (and thus 9,999 fewer network I/O calls).

• A middle-tier program makes 100 database fetch calls (and thus 100 network I/O calls) to fetch 994 rows. It could have fetched 994 rows in 10 fetch calls (and thus 90 fewer network I/O calls).

• A SQL statement (my choice of wording here is a dead giveaway that I was introduced to SQL within the Oracle community) touches the database buffer cache 7,428,322 times to return a 698-row result set. An extra filter predicate could have returned the seven rows that the end user really wanted to see, with only 52 touches upon the database buffer cache.

Certainly, if a system has some global problem that creates inefficiency for broad groups of tasks across the system (e.g., ill-conceived index, badly set parameter, poorly configured hardware), then you should fix it. Don't tune a system to accommodate programs that are inefficient, however. (Admittedly, sometimes you need a tourniquet to keep from bleeding to death, but don't use a stopgap measure as a permanent solution. Address the inefficiency.) There is a lot more leverage in curing the program inefficiencies themselves. Even if the programs are commercial off-the-shelf applications, it will benefit you more in the long run to work with your software vendor to make your programs efficient than it will to try to optimize your system to be as efficient as it can with an inherently inefficient workload.

Improvements that make your program more efficient can produce tremendous benefits for everyone on the system. It's easy to see how top-line reduction of waste helps the response time of the task being repaired. What many people don't understand as well is that making one program more efficient creates a side effect of performance improvement for other programs on the system that have no apparent relation to the program being repaired. It happens because of the influence of load upon the system.

Load

Load is competition for a resource induced by concurrent task executions. It is the reason that the performance testing done by software developers doesn't catch all the performance problems that show up later in production.

One measure of load is utilization, which is resource usage divided by resource capacity for a specified time interval. As utilization for a resource goes up, so does the response time a user will experience when requesting service from that resource. Anyone who has ridden in an automobile in a big city during rush hour has experienced this phenomenon. When the traffic is heavily congested, you have to wait longer at the tollbooth.

The software you use doesn't actually "go slower" as your car does when you're going 30 mph in heavy traffic instead of 60 mph on the open road. Computer software always goes the same speed no matter what (a constant number of instructions per clock cycle), but certainly response time degrades as resources on your system get busier.

There are two reasons that systems get slower as load increases: queuing delay and coherency delay.

Queuing Delay

The mathematical relationship between load and response time is well known. One mathematical model, called M/M/m, relates response time to load in systems that meet one particularly useful set of specific requirements.¹¹ One of the assumptions of M/M/m is that the system you are modeling has "theoretically perfect scalability." This is akin to having a physical system with "no friction," an assumption that so many problems in introductory physics courses invoke.

Regardless of some overreaching assumptions such as the one about perfect scalability, M/M/m has a lot to teach us about performance. Figure 4 shows the relationship between response time and load using M/M/m.

In figure 4 you can see mathematically what you feel when you use a system under different load conditions. At low load, your response time is essentially the same as your response time at no load. As load ramps up, you sense a slight, gradual degradation in response time. That gradual degradation doesn't really do much harm, but as load continues to ramp up, response time begins to degrade in a manner that's neither slight nor gradual. Rather, the degradation becomes quite unpleasant and, in fact, hyperbolic.

Response time (R), in the perfect scalability M/M/m model, consists of two components: service time (S) and queuing delay (Q), or R = S + Q. Service time is the duration that a task spends consuming a given resource, measured in time per task execution, as in seconds per click. Queuing delay is the time that a task spends enqueued at a given resource, awaiting its opportunity to consume that resource. Queuing delay is also measured in time per task execution (e.g., seconds per click).

In other words, when you order lunch at Taco Tico, your response time (R) for getting your order is the queuing delay time (Q) that you spend in front of the counter waiting for someone to take your order, plus the service time (S) you spend waiting for your order to hit your hands once you begin talking to the order clerk. Queuing delay is the difference between your response time for a given task and the response time for that same task on an otherwise unloaded system (don't forget our perfect scalability assumption).

The Knee

When it comes to performance, you want two things from a system:

• The best response time you can get: you don't want to have to wait too long for tasks to get done.

• The best throughput you can get: you want to be able to cram as much load as you possibly can onto the system so that as many people as possible can run their tasks at the same time.

Unfortunately, these two goals are contradictory. Optimizing to the first goal requires you to minimize the load on your system; optimizing to the second goal requires you to maximize it. You can't do both simultaneously. Somewhere in between—at some load level (that is, at some utilization value)—is the optimal load for the system.

The utilization value at which this optimal balance occurs is called the knee. This is the point at which throughput is maximized with minimal negative impact to response times. (I am engaged in an ongoing debate about whether it is appropriate to use the term knee in this context. For the time being, I shall continue to use it. See sidebar below for details.) Mathematically, the knee is the utilization value at which response time divided by utilization is at its minimum. One nice property of the knee is that it occurs at the utilization value where a line through the origin is tangent to the response-time curve. On a carefully produced M/M/m graph, you can locate the knee quite nicely with just a straightedge, as shown in figure 5.

Another nice property of the M/M/m knee is that you need to know the value of only one parameter to compute it. That parameter is the number of parallel, homogeneous, independent service channels. A service channel is a resource that shares a single queue with other identical resources, such as a booth in a toll plaza or a CPU in an SMP (symmetric multiprocessing) computer.

The italicized lowercase m in the term M/M/m is the number of service channels in the system being modeled. The M/M/m knee value for an arbitrary system is difficult to calculate, but I've done it for you in table 5, which shows the knee values for some common service channel counts. (By this point, you may be wondering what the other two Ms stand for in the M/M/m queuing model name. They relate to assumptions about the randomness of the timing of your incoming requests and the randomness of your service times. See Kendall's Notation for more information, or Optimizing Oracle Performance¹¹ for even more.)

Why is the knee value so important? For systems with randomly timed service requests, allowing sustained resource loads in excess of the knee value results in response times and throughputs that will fluctuate severely with microscopic changes in load. Hence, on systems with random request arrivals, it is vital to manage load so that it will not exceed the knee value.

Relevance of the Knee

How important can this knee concept be, really? After all, as I've told you, the M/M/m model assumes this ridiculously utopian idea that the system you're thinking about scales perfectly. I know what you're thinking: it doesn't.

What M/M/m does give us is the knowledge that even if your system did scale perfectly, you would still be stricken with massive performance problems once your average load exceeded the knee values in table 1. Your system isn't as good as the theoretical systems that M/M/m models. Therefore, the utilization values at which your system's knees occur will be more constraining than the values in table 1. (I use the plural of values and knees, because you can model your CPUs with one model, your disks with another, your I/O controllers with another, and so on.)

To recap:

• Each of the resources in your system has a knee.

• That knee for each of your resources is less than or equal to the knee value you can look up in table 1. The more imperfectly your system scales, the smaller (worse) your knee value will be.

• On a system with random request arrivals, if you allow your sustained utilization for any resource on your system to exceed your knee value for that resource, then you'll have performance problems.

• Therefore, it is vital that you manage your load so that your resource utilizations will not exceed your knee values.

Capacity Planning

Understanding the knee can collapse a lot of complexity out of your capacity planning. It works like this:

• Your goal capacity for a given resource is the amount at which you can comfortably run your tasks at peak times without driving utilizations beyond your knees.

• If you keep your utilizations less than your knees, your system behaves roughly linearly—no big hyperbolic surprises.

• If you're letting your system run any of its resources beyond their knee utilizations, however, then you have performance problems (whether you're aware of them or not).

• If you have performance problems, then you don't need to be spending your time with mathematical models; you need to be spending your time fixing those problems by rescheduling load, eliminating load, or increasing capacity.

That's how capacity planning fits into the performance management process.

Random Arrivals

You might have noticed that I used the term random arrivals several times. Why is that important?

Some systems have something that you probably don't have right now: completely deterministic job scheduling. Some systems—though rare these days—are configured to allow service requests to enter the system in absolute robotic fashion, say, at a pace of one task per second. And by this, I don't mean at an average rate of one task per second (for example, two tasks in one second and zero tasks in the next); I mean one task per second, as a robot might feed car parts into a bin on an assembly line.

If arrivals into your system behave completely deterministically—meaning that you know exactly when the next service request is coming—then you can run resource utilizations beyond their knee utilizations without necessarily creating a performance problem. On a system with deterministic arrivals, your goal is to run resource utilizations up to 100 percent without cramming so much workload into the system that requests begin to queue.

The reason the knee value is so important on a system with random arrivals is that these tend to cluster and cause temporary spikes in utilization. These spikes need enough spare capacity to consume so that users don't have to endure noticeable queuing delays (which cause noticeable fluctuations in response times) every time a spike occurs.

Temporary spikes in utilization beyond your knee value for a given resource are OK as long as they don't exceed a few seconds in duration. How many seconds are too many? I believe (but have not yet tried to prove) that you should at least ensure that your spike durations do not exceed eight seconds. (You'll recognize this number if you've heard of the "eight-second rule."²) The answer is certainly that if you're unable to meet your percentile-based response time promises or your throughput promises to your users, then your spikes are too long.

Coherency Delay

Your system doesn't have theoretically perfect scalability. Even if I've never studied your system specifically, it's a pretty good bet that no matter what computer application system you're thinking of right now, it does not meet the M/M/m "theoretically perfect scalability" assumption. Coherency delay is the factor that you can use to model the imperfection.⁵ It is the duration that a task spends communicating and coordinating access to a shared resource. Like response time, service time, and queuing delay, coherency delay is measured in time per task execution, as in seconds per click.

I won't describe a mathematical model for predicting coherency delay, but the good news is that if you profile your software task executions, you'll see it when it occurs. In Oracle, timed events such as the following are examples of coherency delay:

• enqueue

• buffer busy waits

• latch free

You can't model such coherency delays with M/M/m. That's because M/M/m assumes that all m of your service channels are parallel, homogeneous, and independent. That means the model assumes that after you wait politely in a FIFO queue for long enough that all the requests that enqueued ahead of you have exited the queue for service, it will be your turn to be serviced. Coherency delays don't work like that, however.

Example 10. Imagine an HTML data-entry form in which one button labeled "Update" executes a SQL update statement, and another button labeled "Save" executes a SQL commit statement. An application built like this would almost guarantee abysmal performance. That's because the design makes it possible—quite likely, actually—for a user to click Update, look at his calendar, realize "uh-oh, I'm late for lunch," and then go to lunch for two hours before clicking Save later that afternoon.

The impact to other tasks on this system that wanted to update the same row would be devastating. Each task would necessarily wait for a lock on the row (or, on some systems, worse: a lock on the row's page) until the locking user decided to go ahead and click Save—or until a database administrator killed the user's session, which of course would have unsavory side effects to the person who thought he had updated a row.

In this case, the amount of time a task would wait for the lock to be released would have nothing to do with how busy the system was. It would be dependent upon random factors that exist outside of the system's various resource utilizations. That's why you can't model this kind of thing in M/M/m, and it's why you can never assume that a performance test executed in a unit-testing type of environment is sufficient for a making a go/no-go decision about insertion of new code into a production system.

Performance Testing

All this talk of queuing delays and coherency delays leads to a very difficult question: How can you possibly test a new application enough to be confident that you're not going to wreck your production implementation with performance problems?

You can model. And you can test.¹ Nothing you do will be perfect, however. It is extremely difficult to create models and tests in which you'll foresee all your production problems in advance of actually encountering those problems in production.

Some people allow the apparent futility of this observation to justify not testing at all. Don't get trapped in that mentality. The following points are certain:

• You'll catch a lot more problems if you try to catch them prior to production than if you don't even try.

• You'll never catch all your problems in preproduction testing. That's why you need a reliable and efficient method for solving the problems that leak through your preproduction testing processes.

Somewhere in the middle between "no testing" and "complete production emulation" is the right amount of testing. The right amount of testing for aircraft manufacturers is probably more than the right amount of testing for companies that sell baseball caps. But don't skip performance testing altogether. At the very least, your performance-test plan will make you a more competent diagnostician (and clearer thinker) when the time comes to fix the performance problems that will inevitably occur during production operation.

Measuring

People feel throughput and response time. Throughput is usually easy to measure, response time is much more difficult. (Remember, throughput and response time are not reciprocals.) It may not be difficult to time an end-user action with a stopwatch, but it might be very difficult to get what you really need, which is the ability to drill down into the details of why a given response time is as large as it is.

Unfortunately, people tend to measure what's easy to measure, which is not necessarily what they should be measuring. It's a bug. Measures that aren't what you need, but that are easy enough to obtain and seem related to what you need are called surrogate measures. Examples include subroutine call counts and samples of subroutine call execution durations.

I'm ashamed that I don't have greater command of my native language than to say it this way, but here is a catchy, modern way to express what I think about surrogate measures: surrogate measures suck.

Here, unfortunately, suck doesn't mean never work. It would actually be better if surrogate measures never worked. Then nobody would use them. The problem is that surrogate measures work sometimes. This inspires people's confidence that the measures they're using should work all the time, and then they don't. Surrogate measures have two big problems.

• They can tell you your system's OK when it's not. That's what statisticians call type I error, the false positive.

• They can tell you that something is a problem when it's not. That's a type II error, the false negative. I've seen each type of error waste years of people's time.

When the time comes to assess the specifics of a real system, your success is at the mercy of how good the measurements are that your system allows you to obtain. I've been fortunate to work in the Oracle market segment, where the software vendor at the center of our universe participates actively in making it possible to measure systems the right way. Getting application software developers to use the tools that Oracle offers is another story, but at least the capabilities are there in the product.

Performance is a Feature

Performance is a software application feature, just like recognizing that it's convenient for a string of the form "Case 1234" to automatically hyperlink over to case 1234 in your bug-tracking system. (FogBugz, which is software that I enjoy using, does this.) Performance, like any other feature, doesn't just happen; it has to be designed and built. To do performance well, you have to think about it, study it, write extra code for it, test it, and support it.

Like many other features, however, you can't know exactly how performance is going to work out while you're still writing, studying, designing, and creating the application. For many applications (arguably, for the vast majority), performance is completely unknown until the production phase of the software development life cycle. What this leaves you with is this: since you can't know how your application is going to perform in production, you need to write your application so that it's easy to fix performance in production.

As David Garvin has taught us, it's much easier to manage something that's easy to measure.³ Writing an application that's easy to fix in production begins with an application that's easy to measure in production.

Usually, when I mention the concept of production performance measurement, people drift into a state of worry about the measurement-intrusion effect of performance instrumentation. They immediately enter a mode of data-collection compromise, leaving only surrogate measures on the table. Won't software with an extra code path to measure timings be slower than the same software without that extra code path?

I like an answer that I once heard Tom Kyte give in response to this question.⁷ He estimated that the measurement-intrusion effect of Oracle's extensive performance instrumentation is -10 percent or less (where or less means or better, as in -20 percent, -30 percent, etc.). He went on to explain to a now-vexed questioner that the product is at least 10 percent faster now because of the knowledge that Oracle Corporation has gained from its performance instrumentation code, more than making up for any "overhead" the instrumentation might have caused.

I think that vendors tend to spend too much time worrying about how to make their measurement code path efficient without figuring out first how to make it effective. It lands squarely upon the idea that Knuth wrote about in 1974 when he said that "premature optimization is the root of all evil."⁶ The software designer who integrates performance measurement into a product is much more likely to create a fast application and—more importantly—one that will become faster over time. Q

Open Debate about Knees

In this article, I write about knees in performance curves, their relevance, and their application. Whether it's even worthwhile to try to define the concept of knee, however, has been the subject of debate going back at least 20 years.

There is significant historical basis for the idea that the thing I've described as a knee in fact isn't really meaningful. In 1988, Stephen Samson argued that, at least for M/M/1 queuing systems, no "knee" appears in the performance curve. "The choice of a guideline number is not easy, but the rule-of-thumb makers go right on. In most cases there is not a knee, no matter how much we wish to find one," wrote Samson.^3a

The whole problem reminds me, as I wrote in 1999,^2a of the parable of the frog and the boiling water. The story says that if you drop a frog into a pan of boiling water, he will escape. But if you put a frog into a pan of cool water and slowly heat it, then the frog will sit patiently in place until he is boiled to death.

With utilization, just as with boiling water, there is clearly a "death zone," a range of values in which you can't afford to run a system with random arrivals. But where is the border of the death zone? If you are trying to implement a procedural approach to managing utilization, you need to know.

My friend Neil Gunther has debated with me privately that, first, the term knee is completely the wrong word to use here, in the absence of a functional discontinuity. Second, he asserts that the boundary value of .5 for an M/M/1 system is wastefully low, that you ought to be able to run such a system successfully at a much higher utilization value than that. Finally, he argues that any such special utilization value should be defined expressly as the utilization value beyond which your average response time exceeds your tolerance for average response time (figure A). Thus, Gunther argues that any useful not-to-exceed utilization value is derivable only from inquiries about human preferences, not from mathematics. (See here for more information about his argument.)

The problem I see with this argument is illustrated in figure B. Imagine that your tolerance for average response time is T, which creates a maximum tolerated utilization value of ρ_T. Notice that even a tiny fluctuation in average utilization near ρ_T will result in a huge fluctuation in average response time.

I believe that your customers feel the variance, not the mean. Perhaps they say they will accept average response times up to T, but humans will not be tolerant of performance on a system when a 1 percent change in average utilization over a one-minute period results in, say, a tenfold increase in average response time over that period.

I do understand the perspective that the knee values I've listed in this article are below the utilization values that many people feel safe in exceeding, especially for lower-order systems such as M/M/1. It is important, however, to avoid running resources at average utilization values where small fluctuations in utilization yield too-large fluctuations in response time.

Having said that, I don't yet have a good definition for a too-large fluctuation. Perhaps, like response-time tolerances, different people have different tolerances for fluctuation. But perhaps there is a fluctuation tolerance factor that applies with reasonable universality across all users. The Apdex Application Performance Index standard, for example, assumes that the response time F at which users become "frustrated" is universally four times the response time T at which their attitude shifts from being "satisfied" to merely "tolerating."^1a

The knee, regardless of how you define it or what we end up calling it, is an important parameter to the capacity-planning procedure that I described earlier in the main text of this article, and I believe it is an important parameter to the daily process of computer system workload management.

I will keep studying.

References

1a. Apdex; http://www.apdex.org.

2a. Millsap, C. 1999. Performance management: myths and facts; http://method-r.com.

3a. Samson, S. 1988. MVS performance legends. In Computer Measurement Group Conference Proceedings: 148-159.

References

1. CMG (Computer Measurement Group, a network of professionals who study these problems very, very seriously); http://www.cmg.org.

2. Eight-second rule; http://en.wikipedia.org/wiki/Network_performance#8-second_rule.

3. Garvin, D. 1993. Building a learning organization. Harvard Business Review (July).

4. General Electric Company. What is Six Sigma? The roadmap to customer impact. http://www.ge.com/sixsigma/SixSigma.pdf.

5. Gunther, N. 1993. Universal Law of Computational Scalability; http://en.wikipedia.org/wiki/Neil_J._Gunther#Universal_Law_of_Computational_Scalability.

6. Knuth, D. 1974. Structured programming with Go To statements. ACM Computing Surveys 6(4): 268.

7. Kyte, T. 2009. A couple of links and an advert...; http://tkyte.blogspot.com/2009/02/couple-of-links-and-advert.html.

8. Millsap, C. 2009. My whole system is slow. Now what? http://carymillsap.blogspot.com/2009/12/my-whole-system-is-slow-now-what.html.

9. Millsap, C. 2009. On the importance of diagnosing before resolving. http://carymillsap.blogspot.com/2009/09/on-importance-of-diagnosing-before.html.

10. Millsap, C. 2009. Performance optimization with Global Entry. Or not? http://carymillsap.blogspot.com/2009/11/performance-optimization-with-global.html.

11. Millsap, C., Holt, J. 2003. Optimizing Oracle Performance. Sebastopol, CA: O'Reilly.

12. Oak Table Network; http://www.oaktable.net.

Acknowledgments

Thank you, Baron Schwartz, for the e-mail conversation in which you thought I was helping you, but in actual fact, you were helping me come to grips with the need for introducing coherency delay more prominently into my thinking. Thank you, Jeff Holt, Ron Crisco, Ken Ferlita, and Harold Palacio, for the daily work that keeps the company going and for the lunchtime conversations that keep my imagination going. Thank you, Tom Kyte, for your continued inspiration and support. Thank you, Mark Farnham, for your helpful suggestions. And thank you, Neil Gunther, for your patience and generosity in our ongoing discussions about knees.

Cary Millsap is the founder and president of Method R Corporation (http://method-r.com), a company devoted to software performance. He is the author (with Jeff Holt) of Optimizing Oracle Performance (O'Reilly) and a co-author of Oracle Insights: Tales of the Oak Table (Apress). He is the former vice president of Oracle Corporation's System Performance Group and a co-founder of Hotsos. He is also an Oracle ACE Director and a founding partner of the Oak Table Network, an informal association of well-known "Oracle scientists." He blogs at http://carymillsap.blogspot.com, and he tweets at http://twitter.com/CaryMillsap.

Originally published in Queue vol. 8, no. 9— see this item in the ACM Digital Library

Ulan Degenbaev, Jochen Eisinger, Manfred Ernst, Ross McIlroy, Hannes Payer - Idle-Time Garbage-Collection Scheduling Taking advantage of idleness to reduce dropped frames and memory consumption

Neil Gunther, Paul Puglia, Kristofer Tomasette - Hadoop Superlinear Scalability The perpetual motion of parallel performance

Robert Sproull, Jim Waldo - The API Performance Contract How can the expected interactions between caller and implementation be guaranteed?

Patrick Meenan - How Fast is Your Web Site? Web site performance data has never been more readily available.

Thinking Methodically about Performance

http://queue.acm.org/detail.cfm?id=2413037

===========================================

Performance

This and other acmqueue articles have been translated into Portuguese ACM Q em Língua Portuguesa

December 11, 2012 Volume 10, issue 12

Thinking Methodically about Performance

The USE method addresses shortcomings in other commonly used methodologies.

Brendan Gregg, Joyent

Performance issues can be complex and mysterious, providing little or no clue to their origin. In the absence of a starting point—or a methodology to provide one—performance issues are often analyzed randomly: guessing where the problem may be and then changing things until it goes away. While this can deliver results—if you guess correctly—it can also be time-consuming, disruptive, and may ultimately overlook certain issues. This article describes system-performance issues and the methodologies in use today for analyzing them, and it proposes a new methodology for approaching and solving a class of issues.

Systems-performance analysis is complex because of the number of components in a typical system and their interactions. An environment may be composed of databases, Web servers, load balancers, and custom applications, all running upon operating systems—either bare-metal or virtual. And that's just the software. Hardware and firmware, including external storage systems and network infrastructure, add many more components to the environment, any of which is a potential source of issues. Each of these components may require its own field of expertise, and a company may not have staff knowledgeable about all the components in its environment.

Performance issues may also arise from complex interactions between components that work well in isolation. Solving this type of problem may require multiple domains of expertise to work together.

As an example of such complexity within an environment, consider a mysterious performance issue we encountered at Joyent for a cloud-computing customer: the problem appeared to be a memory leak, but from an unknown location. This was not reproducible in a lab environment with the components in isolation. The production environment included the operating system and system libraries, the customer's own application code written in node.js, and a Riak database running on the Erlang VM (virtual machine). Finding the root cause required knowledge of the customer's code, node.js, Riak, Erlang, and the operating system, each of which was provided by one or more different engineers. The problem turned out to be in the system library, identified by engineers with operating-systems expertise.

Another complicating factor is that "good" or "bad" performance can be subjective: unacceptable latency for one user may be acceptable for another. Without a means of clearly identifying issues, it can be difficult to know not only if an issue is present, but also when it is fixed. The ability to measure performance issues—for example, in terms of response time—allows them to be quantified and different issues ranked in order of importance.

Performance-analysis methodology can provide an efficient means of analyzing a system or component and identifying the root cause(s) of problems, without requiring deep expertise. Methodology can also provide ways of identifying and quantifying issues, allowing them to be known and ranked.

Performance texts have provided methodologies for various activities, such as capacity planning,^1,16 benchmarking,¹⁸ and modeling systems.^7,8,10 Methodologies for finding the root causes of performance issues, however, are uncommon. One example is the drill-down analysis method introduced in Solaris Performance and Tools,¹³ which describes a three-stage procedure for moving from a high-level symptom down to analyzing the cause. Performance texts have typically covered analysis by use of ad-hoc checklists of recent tips and tuning, and by teaching operating-systems internals and tools.^2,11,12,15 This allows performance analysts to develop their own methodologies, although this can take considerable time to accomplish.

Ad-hoc performance checklists have been a popular resource. For example, Sun Performance and Tuning² includes "Quick Reference for Common Tuning Tips," which lists 11 tips, intended to find disk bottlenecks, NFS (network file system), memory, and CPU issues, and is both easy to follow and prescriptive. Support staff groups often use these lists because they provide a consistent check of all items, including the most egregious issues. This approach poses some problems, however. Observability is limited to the specific items in the list, and they are usually point-in-time recommendations that go out of date and require updates. These checklists also focus on issues for which there are known fixes that can be easily documented, such as the setting of tunable parameters, but not custom fixes to the source code or environment.

The sections that follow summarize several other methodologies for systems-performance analysis, including the USE method, which is explained in detail. Let's begin by describing two commonly used anti-methodologies—the blame-someone-else anti-method and the streetlight anti-method—which serve as comparisons with later methodologies.

Anti-Methodologies

The first anti-methodology, blame-someone-else, follows these simple steps:

1. Find a system or environment component you are not responsible for.

2. Hypothesize that the issue is with that component.

3. Redirect the issue to the responsible team.

4. When proven wrong, go back to step 1.

For example, "Maybe it's the network. Can you check with the network team to see if they have had dropped packets or something?"

Instead of investigating performance issues, this methodology makes them someone else's problem, which can be wasteful of other teams' resources. A lack of data analysis—or even data to begin with—leads to the hypothesis. Ask for screen shots showing which tools were run and how their output was interpreted. These can be taken to someone else for a second opinion.

While running tools and collecting data is better than wild hypotheses, it isn't sufficient for effective performance analysis, as shown by the streetlight anti-method. This is the absence of any deliberate methodology. The user analyzes performance by selecting observability tools that are familiar, found on the Internet, or found at random and then seeing whether anything obvious shows up. This hit-or-miss approach can overlook many types of issues.

Finding the right tool can take a while. The most familiar tools are run first, even if they don't make the most sense. This is related to an observational bias called the streetlight effect,¹⁷ named after a parable:

A policeman sees a drunk hunting for something under a streetlight and asks what he is looking for. The drunk says he has lost his keys. The policeman can't find them either, and asks if he lost them under the streetlight. The drunk replies: "No, but this is where the light is best."

The performance equivalent would be looking at top(1), not because it makes sense but because the user doesn't know how to read other tools.

Learning more tools helps but is still a limited approach. Certain system components or resources may be overlooked because of a lack of observability tools or metrics. Furthermore, the user, unaware that the view is incomplete, has no way of identifying "unknown unknowns."

Existing Performance-analysis Methodologies

Better performance-analysis methodologies are available that may solve issues before you run any tools at all. These include the problem statement method, workload characterization, and drill-down analysis.

Problem Statement Method

The problem statement method, commonly used by support staff for collecting information about a problem, has been adapted for performance analysis.⁹ It can be the first methodology attempted for performance issues.

The intent is to collect a detailed description of the issue—the problem statement—which directs deeper analysis. The description on its own may even solve the issue. This is typically entered into a ticketing system by asking the following questions:

• What makes you think there is a performance problem?

• Has this system ever performed well?

• What has changed recently? (Software? Hardware? Load?)

• Can the performance degradation be expressed in terms of latency or runtime?

• Does the problem affect other people or applications (or is it just you)?

• What is the environment? What software and hardware are used? Versions? Configuration?

These questions may be customized to the environment. While the questions may seem obvious, the answers often resolve a class of issues, requiring no deeper methodologies. When that is not the case, other methodologies can be called into service, including workload characterization and drill-down analysis.

The Workload Characterization Method

The workload can be characterized by answering questions such as:

• Who is causing the load? Process ID, user ID, remote IP address?

• Why is the load being called? Code path?

• What are other characteristics of the load? IOPS, throughput, type?

• How is the load changing over time?

This helps to separate problems of load from problems of architecture, by identifying the former.

The best performance wins often arise from eliminating unnecessary work. Sometimes these bottlenecks are caused by applications malfunctioning (e.g., a thread stuck in a loop) or bad configurations (system-wide backups running during the day). With maintenance or reconfiguration, such unnecessary work can be eliminated. Characterizing the load can identify this class of issue.

The Drill-Down Analysis Method

Drill-down analysis involves peeling away layers of software and hardware to find the core of the issue—moving from a high-level view to deeper details. These deeper details may include examining kernel internals—for example, by using profiling to sample kernel stack traces, or dynamic tracing to examine the execution of kernel functions.

Solaris Performance and Tools¹³ provides a drill-down analysis methodology for system performance. It follows three stages:

• Monitoring. This continually records high-level statistics over time across many systems, identifying or alerting if a problem is present.

• Identification. Given a system with a suspected problem, this narrows the investigation to particular resources or areas of interest using system tools and identifying possible bottlenecks.

• Analysis. This stage provides further examination of particular system areas, identifying the root cause(s) and quantifying the issue.

The analysis stage may follow its own drill-down approach, beginning with applications at the top of the software stack and drilling down into system libraries, system calls, kernel internals, device drivers, and hardware.

While drill-down analysis often pinpoints the root cause of issues, it can be time-consuming, and when drilling in the wrong direction, it can waste a great deal of time.

The Need for a New Methodology

I recently analyzed a database performance issue on the Joyent public cloud, which began with a ticket containing a problem statement as described in the previous section. The statement indicated that there was a real issue that needed deeper analysis.

The issue had been intermittent, with some database queries taking seconds to complete. The customer blamed the network, hypothesizing that the query latency was caused by dropped network packets. This wasn't a wild hypothesis, as the ticket included output from ping(1) showing occasional high latency; ping(1) is a common and familiar tool, however, and with no other supporting evidence, this seemed to be an example of the streetlight anti-method.

The support team ran tools to investigate the network in much more detail, including examining TCP/IP stack network counters, without finding any problems. This analysis took time because there are dozens of such statistics, some of which are difficult to interpret and must be examined over time to look for correlations. While logged into the systems, the team also checked CPU usage vs. the cloud-imposed limits, following their own ad hoc checklist of common issues. Their conclusion was that there was no issue while they were watching: the network and CPUs were fine.

At this point, many system components and tens of thousands of system statistics had not yet been checked, as they were assumed to be unrelated to the issue. Without a direction to follow, checking everything across all systems in the customer's cloud environment could take days. The analysis to date hadn't found any evidence of a real issue, which was discouraging.

The next step was to try dynamic tracing of the originally reported problem (network packet drops), in the hope of finding something that the standard network counters had missed. I've used the DTrace tool many times to perform drill-down analysis of the TCP/IP stack. This can provide many details beyond the standard network observability toolset, including inspection of kernel-dropped packets and internal TCP state. It still can take hours to catch intermittent issues, however. I was tempted to begin drill-down analysis from the database query latency, in case the issue was not network-related, or to begin characterizing the database workload over time, in case the problem was caused by a burst of load, but these approaches are also time-consuming.

Before beginning deeper analysis, I wanted to perform a quick check of all system components, not just the network and CPUs, to look for bottlenecks or errors. For this to be quick, it would need to check only a limited number of statistics per system, not the tens of thousands available. And for this to be complete, it would need to check all components, including those that might be missed because they have no observability tools or statistics by default.

The USE method provided one way of doing this. It quickly revealed that the database system was out of memory and was paging, and that the disks were occasionally running at saturation. Focusing troubleshooting efforts on networking early on had meant that these areas were overlooked in the team's analysis. The real issues were in the system memory and disks, which were much quicker to read and interpret.

I developed the USE method while teaching classes in operating systems performance. The goal was to help my students find common issues and to ensure that they weren't overlooking important areas. I've used it successfully many times in enterprise and cloud-computing environments, but it doesn't solve all types of problems and should be treated as just one methodology in the toolbox.

The USE Method

The USE (for utilization, saturation, and errors) method is intended to be used early in a performance investigation, after the problem-statement method, to identify systemic bottlenecks quickly. It can be summarized as:

For every resource, check utilization, saturation, and errors.

Resource in this case means all physical server functional components (CPUs, disks, buses, etc.) examined individually. Some software resources can be examined using the same methodology, provided the metrics make sense.

Utilization is the percentage of time that the resource is busy servicing work during a specific time interval. While busy, the resource may still be able to accept more work; the degree to which it cannot do so is identified by saturation. That extra work is often waiting in a queue.

For some resource types, including main memory, utilization is the capacity of the resource that is used. This is different from the time-based definition. Once a capacity resource reaches 100-percent utilization, no more work can be accepted, and it either queues the work (saturation) or returns errors, either of which is identified by the USE method.

Errors in terms of the USE method refers to the count of error events. Errors should be investigated because they can degrade performance, and they may not be immediately noticed when the failure mode is recoverable. This includes operations that fail and are retried, as well as devices that fail in a pool of redundant devices.

In contrast to the streetlight anti-method, the USE method iterates over system resources instead of starting with tools. This creates a complete list of questions to ask, and only then searches for the tools to answer them. Even when tools cannot be found to answer the questions, the knowledge that these questions are unanswered can be extremely useful for the performance analyst: they are now "known unknowns."

The USE method also directs analysis to a limited number of key metrics, so that all system resources are checked as quickly as possible. After this, if no issues have been found, you can turn to other methodologies.

Expressing Metrics

The key metrics of the USE method are usually expressed as follows:

•Utilization as a percent over a time interval (e.g., one CPU is running at 90-percent utilization).

•Saturation as a wait queue length (e.g., the CPUs have an average run queue length of four).

•Errors as the number of errors reported (e.g., the network interface has had 50 late collisions).

It's also important to express the time interval for the measurement. Though it may seem counterintuitive, a short burst of high utilization can cause saturation and performance issues, even though the overall utilization is low over a long interval. Some monitoring tools report utilization as five-minute averages. CPU utilization, for example, can vary dramatically from second to second, and a five-minute average can disguise short periods of 100-percent utilization and, therefore, saturation.

Resource List

The first step in the USE method is to create a list of resources. Try to be as complete as possible. Here is a generic list of server hardware resources, with specific examples:

•CPUs—Sockets, cores, hardware threads (virtual CPUs).

•Main memory—DRAM.

•Network interfaces—Ethernet ports.

•Storage devices—Disks.

•Controllers—Storage, network.

•Interconnects—CPU, memory, I/O.

Each component typically acts as a single resource type. For example, main memory is a capacity resource, and a network interface is an I/O resource, which can be measured in either IOPS (I/O operations per second) or throughput. Some components can behave as multiple resource types—for example, a storage device is both an I/O resource and a capacity resource. Consider all types that can lead to performance bottlenecks. Also note that I/O resources can be further studied as queueing systems, which queue and then service these requests.

Some physical components can be left off your checklist, such as hardware caches (e.g., MMU TLB/TSB, CPU Level-1/2/3). The USE method is most effective for resources that suffer performance degradation under high utilization or saturation, leading to bottlenecks; caches improve performance under high utilization.

Cache hit rates and other performance attributes can be checked after the USE method has been applied—that is, after systemic bottlenecks have been ruled out. If you are unsure whether to include a resource, go ahead and include it and then see how well the metrics work in practice.

Function Block Diagram

Another way to iterate over resources is to find or draw a function block diagram³ for the system. This type of diagram also shows relationships, which can be very useful when looking for bottlenecks in the flow of data. Figure 1 is a generic diagram showing a two-socket system.

While determining utilization for the various buses, annotate each one on the functional diagram with its maximum bandwidth. The resulting diagram may pinpoint systemic bottlenecks before a single measurement has been taken. (This is also a useful exercise during hardware product design, while you still have time to change physical components.)

CPU, memory, and I/O interconnects are often overlooked. Fortunately, they aren't commonly the cause of system bottlenecks. Unfortunately, when they are, the problem can be difficult to solve (maybe you can upgrade the main board or reduce load (e.g., "zero copy" projects lighten memory bus load). At least the USE method takes interconnect performance into consideration. (See Analyzing the HyperTransport⁴ for an example of an interconnect issue identified in this way.)

Metrics

Once you have your list of resources, consider the types of metrics you need for each (utilization, saturation, and errors). Table 1 lists some example resources and metric types, along with possible metrics (from generic Unix/Linux). These metrics can be expressed either as averages per interval or as counts.

Repeat for all combinations and include instructions for fetching each metric. Take note of metrics that are not currently available: these are the "known unknowns." You'll end up with a list of about 30 metrics, some of which are difficult to measure and some of which cannot be measured at all. Example checklists have been built for Linux- and Solaris-based systems.^5,6

Fortunately, the most common issues are usually found with the easier metrics (e.g., CPU saturation, memory capacity saturation, network interface utilization, disk utilization), so these can be checked first.

Harder Metrics

Table 2 lists some examples of harder combinations. Some of these metrics may not be available from standard operating-system tools. I often have to write my own software for such metrics, using either static or dynamic tracing (DTrace) or the CPU performance counter facility.

Software Resources

Some software resources can be similarly examined. This usually applies to smaller components of software, not to entire applications. For example:

• Mutex locks. Utilization may be defined as the time the lock was held, saturation by those threads queued waiting on the lock.

• Thread pools. Utilization may be defined as the time threads were busy processing work, saturation by the number of requests waiting to be serviced by the thread pool.

• Process/thread capacity. The system may have a limited number of processes or threads whose current usage may be defined as utilization; waiting on allocation may indicate saturation; and errors occur when the allocation fails (e.g., "cannot fork").

• File descriptor capacity. This is similar to the above, but for file descriptors.

If the metrics work well, then use them; otherwise, software troubleshooting can be left to other methodologies.

Suggested Interpretations

The USE method helps identify which metrics to use. After you learn how to read them from the operating system, your next task is to interpret their current values. For some metrics, interpretation may be obvious (and well documented). Others are not so obvious and may depend on workload requirements or expectations. Here are some general suggestions for interpreting metric types:

• Utilization. 100-percent utilization is usually a sign of a bottleneck (check saturation and its effect to confirm). High utilization (e.g., beyond 60 percent) can begin to be a problem for a couple of reasons. First, when utilization is measured over a relatively long time period (multiple seconds or minutes), a total utilization of, say, 60 percent can hide short bursts of 100-percent utilization. Second, some system resources, such as hard disks, usually cannot be interrupted during an operation, even for higher-priority work. Compare this with CPUs, which can be interrupted ("preempted") at almost any moment. Once disk utilization is above 60 percent, queueing delays can become more frequent and noticeable, as the tail of the queue becomes longer. This can be quantified using queuing theory to model response time vs. utilization (e.g., modeling a disk as M/M/1).

• Saturation. Any degree of saturation can be a problem (non-zero). This may be measured as the length of a wait queue or time spent waiting on the queue.

• Errors. Non-zero error counters are worth investigating, especially if they are still increasing while performance is poor.

It's easy to interpret the negative cases: low utilization, no saturation, no errors. This is more useful than it sounds. Narrowing the scope of an investigation can help you focus quickly on the problem area.

Cloud Computing

In a cloud-computing environment, software resource controls may be in place to limit or throttle tenants who are sharing one system. At Joyent, we primarily use operating-system virtualization (the SmartOS-based SmartMachine), which imposes memory and CPU limits, as well as storage I/O throttling. Each of these resource limits can be examined with the USE method, similar to examining the physical resources.

For example, in our environment memory capacity utilization can be the tenant's memory usage vs. its memory cap. Memory capacity saturation can be seen by anonymous paging activity, even though the traditional Unix page scanner may be idle.

Strategy

The USE method is pictured as a flowchart in figure 2. Errors come first, because they are usually easier and quicker to interpret than utilization and saturation.

The USE method identifies problems that are likely to be system bottlenecks. Unfortunately, a system may be suffering more than one performance problem, so the first issue you find may be a problem but not the problem. You can investigate each discovery using further methodologies before returning to the USE method as needed to iterate over more resources. Or you may find it more efficient to complete the USE method checklist first and list all problems found, then to investigate each based on its likely priority.

Methodologies for further analysis include the workload characterization and drill-down analysis methods, summarized earlier. After completing these (if needed), you should have evidence to determine whether the corrective action needed is to adjust the load applied or to tune the resource itself.

Other Methodologies

While the previous methodologies may solve most server issues, latency-based methodologies (e.g., Method R¹⁴) can approach finding 100 percent of all issues. These methodologies, however, can take much more time if you are unfamiliar with software internals, and they may be more suited for database administrators or application developers who already have this familiarity.

Conclusion

Systems-performance analysis can be complex, and issues can arise from any component, including from interactions among them. Methodologies in common use today sometimes resemble guesswork: trying familiar tools or posing hypotheses without solid evidence.

The USE Method was developed to address shortcomings in other commonly used methodologies and is a simple strategy for performing a complete check of system health. It considers all resources so as to avoid overlooking issues, and it uses limited metrics so that it can be followed quickly. This is especially important for distributed environments, including cloud computing, where many systems may need to be checked. This methodology will, however, find only certain types of issues—bottlenecks and errors—and should be considered as one tool in a larger methodology toolbox.

References

1. Allspaw, J. 2008. The Art of Capacity Planning. O'Reilly.

2. Cockcroft, A. 1995. Sun Performance and Tuning. Prentice Hall.

3. Function block diagram; http://en.wikipedia.org/wiki/Function_block_diagram.

4. Gregg, B. 2009. 7410 hardware update, and analyzing the HyperTransport; dtrace.org/blogs/brendan/2009/09/22/7410-hardware-update-and-analyzing-thehypertransport/.

5. Gregg, B. 2012. The USE method: Linux performance checklist; http://dtrace.org/blogs/brendan/2012/03/07/the-use-method-linux-performance-checklist/.

6. Gregg, B. 2012. The USE method: Solaris performance checklist; http://dtrace.org/blogs/brendan/2012/03/01/the-use-method-solaris-performance-checklist/.

7. Gunther, N. 2007. Guerrilla Capacity Planning. Springer.

8. Gunther, N. 1997. The Practical Performance Analyst. McGraw Hill.

9. Hargreaves, A. 2011. I have a performance problem; http://alanhargreaves.wordpress.com/2011/06/27/i-have-a-performance-problem/.

10. Jain, R. 1991. The Art of Computer Systems Performance Analysis. Wiley.

11. Loukidas, M. 1990. System Performance Tuning. O'Reilly.

12. McDougall, R., Mauro, J. 2006. Solaris Internals—Solaris 10 and OpenSolaris Kernel Architecture. Prentice Hall.

13. McDougall, R., Mauro, J., Gregg, B. 2006. Solaris Performance and Tools: DTrace and MDB Techniques for Solaris 10 and OpenSolaris. Prentice Hall.

14. Millsap, C., Holt, J. 2003. Optimizing Oracle Performance. O'Reilly.

15. Musumeci, G. D., Loukidas, M. 2002. System Performance Tuning, 2nd Edition. O'Reilly

16. Schlossnagle, T. 2006. Scalable Internet Architectures. Sams Publishing.

17. Streetlight effect; http://en.wikipedia.org/wiki/Streetlight_effect.

18. Wong, B. 1997. Configuration and Capacity Planning for Solaris Servers. Prentice Hall.

Acknowledgments

Carry Millsap's methodology research including Method R has been inspirational and my impetus for documenting system methodologies. Thanks to Bryan Cantrill for helping with this article and for fathering DTrace—which has allowed system methodologies to be developed and used in practice far beyond what traditional observability allows. And thanks to Deirdré Straughan for editing and feedback.

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Brendan Gregg is the lead performance engineer at Joyent, where he analyzes performance and scalability at any level of the software stack. He is the primary author of DTrace (Prentice Hall, 2011), and co-author of Solaris Performance and Tools (Prentice Hall, 2006), and numerous articles about systems performance. He was previously a performance lead and kernel engineer at Sun Microsystems, where he developed the ZFS L2ARC. He has also developed numerous performance analysis tools, including some that are shipped by default in Mac OS X and Oracle Solaris 11. His recent work has included performance visualizations for illumos and Linux kernel analysis.

Originally published in Queue vol. 10, no. 12—
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