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Thanks for the explanation.Wouldn’t the front end tier be also a closed system because just like the database, the web servers/ngonx/haproxy would also have a limited number of preforked processes/threads? How about a nodejs app that’s single threaded but evented?
Thanks again for a very thorough explanation. I will try the open circuit approach.Much appreciated!Radu
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Hi,
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Hi,
* I have 5 machines ( haproxy ) each with a limit of 4096 max connections
* If m = 5 I get an error from PDQ : "Arrival rate 200.000 for stream 'Customers' exceeds saturation thruput 21.739 of node 'Haproxy' with demand 0.046".
* There are other considerations as to why there are 5 machines, unrelated to the performance of the system
* If I look at daily averages, they range from 278 to 158 req / sec. Not much variation in the response time either
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Here is the data
| day | avg latency | call count | arrival rate |
|-----+-------------+------------+--------------|
| 1 | 219.640 | 13402893 | 155.12608 |
| 2 | 230.897 | 12957504 | 149.97111 |
| 3 | 252.011 | 19258354 | 222.89762 |
| 4 | 278.888 | 20332638 | 235.33146 |
| 5 | 158.004 | 16014755 | 185.35596 |
| 6 | 242.859 | 21902988 | 253.50681 |
| Avg | 230.38317 | 17311522 | 200.36484 |Arrival rate is calculated by dividing the call count column ( total requests within that day ) by 86400 (secs in a day )The last row is the avg of per columnAs observed from the metrics haproxy exposes there is no connect / queuing time, so I considered W = 0Radu
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RaduThanks,For example to answer questions like, if service X would become more popular ( say 3x current traffic ) how would capacity look like ? Although given the current values, it looks way overprovisionedThanks a lot!Is there value in going down the route of different backend services and their respective service time ?
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One of the great things about PDQ models is that you can totally ignore reality (whatever that is).You can play around with any and all of the PDQ input parameters/metrics to your heart's content. Nothing will be harmed. The only proviso for multiple workloads is, you'll need to have the respective service times and arrival rates for each workload class you want to distinguish; either from data or good guesses. That's how they're defined in PDQ.
On Tuesday, November 14, 2017 at 11:31:44 AM UTC-8, Radu Brumariu wrote:
RaduThanks,For example to answer questions like, if service X would become more popular ( say 3x current traffic ) how would capacity look like ? Although given the current values, it looks way overprovisionedThanks a lot!Is there value in going down the route of different backend services and their respective service time ?
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If I were to obtain service times at higher arrival rate, how could I use that in the model. As I mentioned, I don’t think the real throughput is linear ( b/c the service time will not be )
On Tue, Nov 14, 2017 at 2:42 PM 'DrQ' via Guerrilla Capacity Planning <guerrilla-capacity-planning@googlegroups.com> wrote:
One of the great things about PDQ models is that you can totally ignore reality (whatever that is).You can play around with any and all of the PDQ input parameters/metrics to your heart's content. Nothing will be harmed. The only proviso for multiple workloads is, you'll need to have the respective service times and arrival rates for each workload class you want to distinguish; either from data or good guesses. That's how they're defined in PDQ.
On Tuesday, November 14, 2017 at 11:31:44 AM UTC-8, Radu Brumariu wrote:
RaduThanks,For example to answer questions like, if service X would become more popular ( say 3x current traffic ) how would capacity look like ? Although given the current values, it looks way overprovisionedThanks a lot!Is there value in going down the route of different backend services and their respective service time ?
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