Update observer to collect CPU utilization data #616

blt · 2023-07-05T18:53:25Z

What does this PR do?

This commit updates the observer with the aim of calculating CPU utilization. We maintain output of user and kernel space CPU seconds but add utilization with the understanding that this is useful to users that need to consider utilization and not ticks.

Additional Notes

Note, if there is CPU use in the system that is not in a parent, child relationship but should still be counted this change will not address that.

Related issues

REF SMP-613
#613

This commit updates the observer with the aim of calculating CPU utilization. We maintain output of user and kernel space CPU seconds but add utilization with the understanding that this is useful to users that need to consider utilization and not ticks. Note, if there is CPU use in the system that is not in a parent, child relationship but should still be counted this change will not address that. REF SMP-612 Signed-off-by: Brian L. Troutwine <[email protected]>

github-actions · 2023-07-05T19:24:07Z

Regression Detector Results

Run ID: 621c321e-974b-40cb-993a-235632f9479d
Baseline: 155fcd5
Comparison: a6e3e6a
Total lading-target CPUs: 4

Explanation

A regression test is an integrated performance test for lading-target in a repeatable rig, with varying configuration for lading-target. What follows is a statistical summary of a brief lading-target run for each configuration across SHAs given above. The goal of these tests are to determine quickly if lading-target performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.
Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
apache_common_http_both_directions_this_doesnt_make_sense	ingress throughput	-0.20	[-0.22, -0.18]	100.00%
blackhole_from_apache_common_http	ingress throughput	-0.29	[-0.34, -0.24]	100.00%

src/observer.rs

goxberry · 2023-07-05T20:24:00Z

src/observer.rs

-        let ticks_per_second: f64 = procfs::ticks_per_second() as f64;
+        let limits = process.limits().map_err(Error::ProcError)?;
+        // NOTE units on the CPU limits are 'CPU / second'
+        let max_cpu_time: Limit = limits.max_cpu_time;


Does this value come from the RLIMIT_CPU field of the struct rlimit queried by the getrlimit syscall (man page)? I'm wondering if the units of this value are seconds or CPU*seconds.

It's read out of /proc/pid/limits, see https://docs.rs/procfs/0.15.1/src/procfs/process/limit.rs.html. So yeah, RLIMIT_CPU. "This is a limit, in seconds, on the amount of CPU time that the process can consume." Which I think would be, in the notation I'm using here, CPU units per second.

Signed-off-by: Brian L. Troutwine <[email protected]>

goxberry · 2023-07-05T20:26:36Z

src/observer.rs

+                        let kernel_utilization_soft = (kernel_time_seconds_diff / process_uptime_seconds_diff) / soft_cpu_limit;
+                        let kernel_utilization_hard = (kernel_time_seconds_diff / process_uptime_seconds_diff) / hard_cpu_limit;
+                        let user_utilization_soft = (user_time_seconds_diff / process_uptime_seconds_diff) / soft_cpu_limit;
+                        let user_utilization_hard = (user_time_seconds_diff / process_uptime_seconds_diff) / hard_cpu_limit;
+                        let utilization_soft = (time_seconds_diff / process_uptime_seconds_diff) / soft_cpu_limit;
+                        let utilization_hard = (time_seconds_diff / process_uptime_seconds_diff) / hard_cpu_limit;


Related to my comment above asking about the units of max_cpu_time above, I'm wondering if the division by process_uptime_seconds_diff is necessary in this group of calculations.

github-actions · 2023-07-05T20:52:43Z

Regression Detector Results

Run ID: d0fa7a88-b486-49c4-ae6a-d75fbab014d8
Baseline: 155fcd5
Comparison: b4723bc
Total lading-target CPUs: 4

Explanation

A regression test is an integrated performance test for lading-target in a repeatable rig, with varying configuration for lading-target. What follows is a statistical summary of a brief lading-target run for each configuration across SHAs given above. The goal of these tests are to determine quickly if lading-target performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.
Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
blackhole_from_apache_common_http	ingress throughput	+0.21	[+0.16, +0.27]	100.00%
apache_common_http_both_directions_this_doesnt_make_sense	ingress throughput	-0.11	[-0.13, -0.09]	100.00%

src/observer.rs

Signed-off-by: Brian L. Troutwine <[email protected]>

github-actions · 2023-07-05T21:51:13Z

Regression Detector Results

Run ID: a099ac78-7638-40c1-9f86-18b139ec50e4
Baseline: 155fcd5
Comparison: ac005df
Total lading-target CPUs: 4

Explanation

A regression test is an integrated performance test for lading-target in a repeatable rig, with varying configuration for lading-target. What follows is a statistical summary of a brief lading-target run for each configuration across SHAs given above. The goal of these tests are to determine quickly if lading-target performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.
Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
blackhole_from_apache_common_http	ingress throughput	+0.20	[+0.15, +0.25]	100.00%
apache_common_http_both_directions_this_doesnt_make_sense	ingress throughput	-0.34	[-0.36, -0.32]	100.00%

github-actions · 2023-07-05T22:01:22Z

Regression Detector Results

Run ID: c6f15f7f-ff55-4377-b9b1-f8057975b93d
Baseline: 155fcd5
Comparison: 67af413
Total lading-target CPUs: 4

Explanation

A regression test is an integrated performance test for lading-target in a repeatable rig, with varying configuration for lading-target. What follows is a statistical summary of a brief lading-target run for each configuration across SHAs given above. The goal of these tests are to determine quickly if lading-target performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.
Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
blackhole_from_apache_common_http	ingress throughput	+0.12	[+0.07, +0.17]	99.69%
apache_common_http_both_directions_this_doesnt_make_sense	ingress throughput	-0.16	[-0.18, -0.14]	100.00%

Signed-off-by: Brian L. Troutwine <[email protected]>

github-actions · 2023-07-06T00:58:20Z

Regression Detector Results

Run ID: b478c7f5-4935-4bd5-a937-232e474eef65
Baseline: 155fcd5
Comparison: 9a28083
Total lading-target CPUs: 4

Explanation

A regression test is an integrated performance test for lading-target in a repeatable rig, with varying configuration for lading-target. What follows is a statistical summary of a brief lading-target run for each configuration across SHAs given above. The goal of these tests are to determine quickly if lading-target performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.
Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
blackhole_from_apache_common_http	ingress throughput	-0.09	[-0.15, -0.04]	98.43%
apache_common_http_both_directions_this_doesnt_make_sense	ingress throughput	-0.10	[-0.12, -0.07]	100.00%

Signed-off-by: Brian L. Troutwine <[email protected]>

github-actions · 2023-07-06T02:10:34Z

Regression Detector Results

Run ID: f3826227-606b-4041-bbd1-42eccb33229d
Baseline: 155fcd5
Comparison: ee286f3
Total lading-target CPUs: 4

Explanation

A regression test is an integrated performance test for lading-target in a repeatable rig, with varying configuration for lading-target. What follows is a statistical summary of a brief lading-target run for each configuration across SHAs given above. The goal of these tests are to determine quickly if lading-target performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.
Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
blackhole_from_apache_common_http	ingress throughput	-0.08	[-0.13, -0.03]	94.84%
apache_common_http_both_directions_this_doesnt_make_sense	ingress throughput	-0.27	[-0.29, -0.25]	100.00%

Signed-off-by: Brian L. Troutwine <[email protected]>

github-actions · 2023-07-06T17:43:44Z

Regression Detector Results

Run ID: 88f9d812-e19d-4a37-b7dd-9eeb23014a7c
Baseline: 155fcd5
Comparison: 914a20d
Total lading-target CPUs: 4

Explanation

A regression test is an integrated performance test for lading-target in a repeatable rig, with varying configuration for lading-target. What follows is a statistical summary of a brief lading-target run for each configuration across SHAs given above. The goal of these tests are to determine quickly if lading-target performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.
Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
apache_common_http_both_directions_this_doesnt_make_sense	ingress throughput	+0.01	[-0.01, +0.03]	42.97%
blackhole_from_apache_common_http	ingress throughput	+0.00	[-0.05, +0.05]	5.68%

We have found in practice that many of our users set their throttle to 'stable'. While predictive throttle is valuable and we wish to retain it this being the default is often confusing. This change is technically breaking. Should be merged behind PR #616 to appear in the changelog. Signed-off-by: Brian L. Troutwine <[email protected]>

* Adjust the default throttle to 'stable' We have found in practice that many of our users set their throttle to 'stable'. While predictive throttle is valuable and we wish to retain it this being the default is often confusing. This change is technically breaking. Should be merged behind PR #616 to appear in the changelog. Signed-off-by: Brian L. Troutwine <[email protected]> * Update changelog, propose 0.17.0 Signed-off-by: Brian L. Troutwine <[email protected]> --------- Signed-off-by: Brian L. Troutwine <[email protected]>

blt requested a review from a team July 5, 2023 18:53

blt force-pushed the observer_calculate_utilization branch from 9cfef5b to a6e3e6a Compare July 5, 2023 18:59

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b4723bc

Signed-off-by: Brian L. Troutwine <[email protected]>

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blt added 2 commits July 5, 2023 21:23

Update unit comments

ac005df

Signed-off-by: Brian L. Troutwine <[email protected]>

0.16.2-rc1

67af413

Signed-off-by: Brian L. Troutwine <[email protected]>

Perform utilization calculation in terms of ticks

87dff92

Signed-off-by: Brian L. Troutwine <[email protected]>

blt requested a review from goxberry July 6, 2023 00:25

clippy dings

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Signed-off-by: Brian L. Troutwine <[email protected]>

goxberry approved these changes Jul 6, 2023

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adjust utilization to Agent percentage method

ee286f3

Signed-off-by: Brian L. Troutwine <[email protected]>

logical not physical cores

914a20d

Signed-off-by: Brian L. Troutwine <[email protected]>

blt mentioned this pull request Jul 6, 2023

Adjust the default throttle to 'stable' #617

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blt merged commit c06adaf into main Jul 6, 2023
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Update observer to collect CPU utilization data #616

Update observer to collect CPU utilization data #616

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Update observer to collect CPU utilization data #616

Update observer to collect CPU utilization data #616

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What does this PR do?

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Regression Detector Results

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Regression Detector Results

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Regression Detector Results

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Regression Detector Results

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