"Compared to CPU time, CPU instruction is a better metric,
  as it reports the same numbers regardless of CPU models
  and CPU loads for the same request."

Also, what about a change in compiler version? That can also vary the number of instructions. Unless they are referring to a Python bytecode instruction as a CPU instruction.

Would measuring CPI be a better indicative of their efficiency? They could also track both, no need to settle for one.

But, honest question, what's the value of determining how fast can we schedule a million containers? This question is not just for Nomad but other cluster managers as well that have recently published similar benchmarks.

I see the value of scheduling thousands to perhaps hundreds of thousands of containers across many nodes, but millions seem excessive.

I think that is more valuable to measure what happens after you have 1 million containers running on your cluster. Such as: - What is the overhead keeping track of that many containers? - How do they impact the responsiveness of other API calls (list, delete)? - What happens when nodes go down and suddenly you lose a considerable amount of containers, can it recover quickly? - How does it impact the performance of running containers in the cluster?

Also, there are other important factors to test for: - what about image size? How does it impact scheduling time when non-cached? - container density per node - number of nodes - what about scheduling other workloads that Nomad support, like VMs and runtimes?

First, some terminology which I think is important for the discussion, also when I say 'job' this could be something like a user, HTTP request, RPC call, network packet, or some sort of task the system is asked to do, and can accomplish in some finite amount of time.

Closed-loop system, aka closed system - is a system where new job arrivals are only triggered by job completions, some examples are interactive terminal, batch systems like a CI build system.

Open-loop system, aka open system - is a system where new job arrivals are independent of job completions, some examples are the requesting the front page of Hacker news, or arriving packets to a network switch.

Partly-open system - is a system where new jobs arrive by some outside process as in an open system, and every time a job completes there is a probability p it makes a follow-up request, or probability (1 - p) it leaves the system. Some examples are web applications, where users request a page, and make follow-up requests, but each user is independent, and new users are arriving and leaving in their own.

Second, workload generators (e.g. JMeter, ab, Gatling, etc) can also be classified similarly. Workload generators that issue a request, and then block to wait for a response before making the next request are based on a closed system (e.g. JMeter[2], ab). Those generators that continue to issue requests independently of the response rate, regardless of the system throughput, are based on an open system (e.g. Gatling, wrk2[3])

Now, CO happens whenever a workload generator based on a closed system is used against an open system or partly open system, and the throughput of the system under load is slower than the injection rate of the workload generator.

For the sake of simplicity, assume we have an open system, say a simple web page, where multiple users arrive by some probability distribution and simply request the page, and then 'leave'. Assume the arrival probability distribution is uniform, where the p is 1.0 that a request will arrive every second.

In this example, if we use a workload generator based on a closed system to simulate this workload for 100 seconds, and the system under load never slows downs so it continuous to serve a response under 1 second, say that is always 500 ms. Then there's no CO here. In the end, we will have 100 samples of response times of 500ms, all the statistics (min, max, avg, etc) will be 500ms.

Now, say we are using the same workload generator at an injection rate of 1 request/s, but this time the system under load for the first 50 seconds will behave as before with responses taking 500 ms, and for the later 50 seconds the system stalls.

Since the system under load is an open system, we should expect 50 samples of response times with 500 ms, and 50 samples where response times linearly decrease from 50s to 1s. The statistics then would be

min=500ms, max=50s, avg=13s, median=0.75s, 90%ile=45.05s

But because we used a closed system workload generator, our samples are skewed. Instead, we get 50 samples of 500ms and only 1 samples of 50 seconds! This happens because the injection rate is slowed down by the response rate of the system. As you can see this is not even the workload we intended because essentially our workload generator backed off when the system stalled. The stats now look like this:

min=500ms, max=50s, avg=1.47s, median=500ms, 90%ile=500ms.

[1][pdf] http://repository.cmu.edu/cgi/viewcontent.cgi?article=1872&c... [2] http://jmeter.512774.n5.nabble.com/Coordinated-Omission-CO-p... [3] https://github.com/giltene/wrk2

Personally, I don't know of any other book that covers so well the application of queuing theory to computer systems.

[1] http://www.cs.cmu.edu/~harchol/PerformanceModeling/book.html

But since the paper assume there's an opening time, perhaps then is not applicable for the block device example I gave above, maybe a more comparable example would be a traffic spike to a web application after some announcement, and how an http framework/library might 'choose' http requests to service. My understanding is that most framework/libraries just implicitly delegate to the OS process scheduler.

Comments URL: https://news.ycombinator.com/item?id=9979959

Points: 2

# Comments: 1

Humans are more complex than that. I don't think you can assume that candidates will perform the same all the time. Sometimes an excellent candidate can perform badly for multiple reasons (e.g. nervousness, poor preparation, bad interviewer, personal problems, etc).

It seems to me, that rejecting a good candidate, and have him/her interview again after some time, if that candidate was a 'good-hire', then it would increase the chance of hiring him/her, since it is most likely they will prepare better, and know what to expect.

The only thing missing would be an out-of-box solution for distributed load generation, which I believe is being developed. But today, you can use a 'scale-out' approach[2] which gives you the ability to combine the data from multiple Gatling instances into a single report, but as a post-process step.

[1] http://gatling.io [2] http://gatling.io/docs/2.1.5/cookbook/scaling_out.html

[1] https://github.com/giltene/wrk2

Brendan Gregg has excellent info on these topics,

http://www.joyent.com/blog/benchmarking-the-cloud http://dtrace.org/blogs/brendan/2011/05/11/file-system-laten... http://dtrace.org/blogs/brendan/2012/10/23/active-benchmarki...

https://gist.github.com/3078651

Comments URL: https://news.ycombinator.com/item?id=3322868

Points: 2

# Comments: 0