# Emulating Internet Traffic in Load Tests

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One of the recurring questions in the GCaP class last week was: How can we make web-application load tests more representative of real Internet traffic? The sticking point is that conventional load-test simulators like LoadRunner, JMeter, and httperf, represent the load in terms of a finite number of virtual user (or vuser) scripts, whereas the Internet has an indeterminately large number of real users creating load.**Taking the Pith Out of Performance**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

From a queueing theory standpoint, we can view the question slightly differently. The test rig corresponds to a closed queueing system because no requests arrive from outside the system so, the sum of the queue lengths can never be larger than the number of vusers. On the other hand, a system driven by Internet traffic is referred to as an open queueing system in that the queues can be of an unbounded length (but not infinite). Moreover, we could put a probe at the entry point into a web server and measure the actual arrival rate in terms of connections per second. In any steady-state measurement period, the average arrival rate will be constant.

Since I don’t happen to have the right kind of measurements from a real test rig, I’m going to use PDQ to demonstrate how the question can be addressed. I’m fully aware that there are other tools and techniques to emulate Internet traffic, but that was not the question that came up in class.

**Standard Load Test (fixed Z)**

Suppose that a load test typically starts with N = 1500 vusers, each having a mean think time Z = 10 seconds, and continues adding load up to some prescribed maximum, say 15,000 vusers. The results might look like this:

**Table 1**

N Z X Q/Z 1500 10 149.47 0.53 3000 10 199.87 100.13 4500 10 199.96 250.04 7500 10 199.99 550.01 9000 10 199.99 700.01 10500 10 200.00 850.00 12000 10 200.00 1000.00 13500 10 200.00 1150.00 15000 10 200.00 1300.00

Note that the numbers in the Z column all have the same value of 10 seconds. The throughput (X) increases up to the point where some resource saturates and becomes the bottleneck. We know from queueing theory that the bottleneck is the resource with the longest service time. Referring to the PDQ model listed at the end, the longest service time is S

_{max}= 0.005 seconds, and it belongs to the queueing node labeled AppBox1. We also know from queueing theory that inverting S

_{max}tells us the maximum possible system throughput. Since 1/S

_{max}= 200 connections per second, we expect to see that number as the saturation throughput and indeed, we do, at N = 10,500 vusers.

The last column in Table 1 just shows that the total number of requests (total queue length, Q) in the SUT increases with increasing N. Here, the Q value is normalized by Z for reasons that will become clear in the next two sections.

**Internet Load Test (scaled Z)**

Now, suppose we know that the mean Internet arrival rate = 150 connections per second at the production web site during some period of interest, e.g., the one-hour peak traffic at 11 am each day. How can we modify the test procedure in the previous section to emulate that rate on the same test rig?

It seems obvious that we need to increase N to some large number—much larger than the 15,000 vusers in Table 1. The trick is, when we scale N up, we also need to increase the think time Z in the same proportion, so that the ratio N/Z remains constant. If we rerun the previous load test using this procedure, the results look like this:

**Table 2**

N Z X Q/Z 1500 10 149.47 0.53 15000 100 149.95 0.05 30000 200 149.97 0.03 45000 300 149.98 0.02 75000 500 149.99 0.01 150000 1000 149.99 0.01 300000 2000 150.00 0.00

The first row is identical to the standard load test in Table 1. This time, however, the throughput approaches the value X = 150 connections per second, not the saturation value of 200 connections per second. Note also that the quantity Q/Z now becomes vanishingly small. The queues are growing, as you would expect in an open queueing model, but not as fast as the scaled value of Z is growing. Therefore, Q/Z gets smaller.

I chose the initial values of N and Z to match the known Internet traffic arrival rate. You can do the same thing to emulate Internet loads. Just choose the ratio N/Z to be equivalent to the Internet connections per second and then incrementally increase N and Z together in your client scripts until the measured throughput on the test rig matches that Internet arrival rate.

**Why It Works**

To understand why this principle works, you should review my previous blog post “Using Think Times to Determine Arrival Rates.” There, I explain that the effective arrival rate in a closed queueing system (like the SUT in a test rig) is given by:

λ_{N} = (N − Q)/Z . | (1) |

It says that if all the requests were in the SUT, then Q = N and the effective arrival rate into the SUT during the next sample period would be zero. Conversely, if no requests have been issued to the SUT, then Q = 0 and the effective arrival rate is expected to be anything between 0 and N, in the next sample period. In other words, the effective arrival is variable in a closed queueing system, whereas we are trying to emulate an open queueing system that has a constant mean arrival rate, by definition.

Dividing through by Z, I can rewrite eqn.(1) as:

λ_{N} = (N/Z) − (Q/Z) . | (2) |

The first term is the ratio N/Z we discussed in the previous section, while the second term corresponds to the last column in Tables 1 and 2. As we make Z larger, that second term contributes less and less. Consequently, λ

_{N}gets closer and closer to N/Z, the desired constant arrival rate for an open system in steady state.

Whether or not you can actually muster the required massive number of vusers is another question whose solution will depend on other constraints such as: licensing costs, machine costs, and so on. Setting Z = 0 (or close to zero in this discussion) is one approach that is commonly employed as a workaround.

**PDQ-R Model**

Here is the PDQ code in R that I used to generate Tables 1 and 2. Refer to the online PDQ manual for details about the PDQ functions.

# Created by NJG on Sat May 15 14:46:13 2010 library(pdq) workname <- "httpGet" servname1 <- "AppBox1" servname2 <- "AppBox2" servname3 <- "AppBox3" servtime1 <- 0.005 servtime2 <- 0.004 servtime3 <- 0.003 usrinc <- c(1,2,3,5,6,7,8,9,10) sflinc <- c(1,10,20,30,50,100,200) cat(sprintf("%8s\t%4s\t%6s\t%8s\n", "N", "Z", "X", "Q/Z")) # Header for(scalefactor in sflinc) { vusers <- 1500 * scalefactor thinktime <- 10 * scalefactor # comment out for usr increments Init("HTTPd Closed Model") CreateClosed(workname, TERM, vusers, thinktime) CreateNode(servname1,CEN,FCFS) CreateNode(servname2,CEN,FCFS) CreateNode(servname3,CEN,FCFS) SetDemand(servname1,workname,servtime1) SetDemand(servname2,workname,servtime2) SetDemand(servname3,workname,servtime3) arrivrate <- GetThruput(TERM,workname) q1 <- GetQueueLength(servname1,workname,TERM) q2 <- GetQueueLength(servname2,workname,TERM) q3 <- GetQueueLength(servname3,workname,TERM) cat(sprintf("%8.f\t%4.f\t%6.2f\t%8.2f\n", vusers, thinktime, arrivrate, (q1+q2+q3)/thinktime)) }

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