EcoSystem TermAppISO - 2020/12/09 NFT

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Stephen (talk) 14:25, 30 December 2020 (UTC) [1]


Summary

  • The following test(s) were included in this test session.
|LABEL                       |Description                       |Start               |End                 |
|:---------------------------|:---------------------------------|:-------------------|:-------------------|
|TermAppISONFT_IR_Regression |TermAppISO Int Routing Regression |2020-12-09 12:48:00 |2020-12-09 14:10:00 |
  • For test TermAppISO Int Routing Regression/TermAppISONFT_IR_Regression (test 1, from 2020-12-09 12:48:00 to 2020-12-09 14:10:00) and for outcome disconnect, the following function(s) had response time(s) in excess of 1 second during the portion of the test where the load did not exceed the current production observed/projected peak.
|   |Basename                   |Outcome    | Count| Percent|  Resp| StdDev|
|:--|:--------------------------|:----------|-----:|-------:|-----:|------:|
|1  |authorisation_request_1100 |disconnect |   304|   1.602| 4.166|  0.205|
  • For test TermAppISO Int Routing Regression/TermAppISONFT_IR_Regression (test 1, from 2020-12-09 12:48:00 to 2020-12-09 14:10:00) and for outcome OK, the following function(s) had a success rate of less than 100% during the portion of the test where the load did not exceed the current production/projected peak.
|   |Basename                         |Outcome | Count| Percent|  Resp| StdDev|
|:--|:--------------------------------|:-------|-----:|-------:|-----:|------:|
|2  |authorisation_request_1100       |OK      | 18635|  98.182| 6.813|  8.841|
|4  |transaction_advice_response_1230 |OK      | 18553|  99.919| 0.736|  0.462|
  • For test TermAppISO Int Routing Regression/TermAppISONFT_IR_Regression (test 1, from 2020-12-09 12:48:00 to 2020-12-09 14:10:00) and for outcome OK, the following function(s) had response time(s) in excess of 1 second during the portion of the test where the load did not exceed the current production observed/projected peak.
|   |Basename                   |Outcome | Count| Percent|  Resp| StdDev|
|:--|:--------------------------|:-------|-----:|-------:|-----:|------:|
|2  |authorisation_request_1100 |OK      | 18635|  98.182| 6.813|  8.841|
  • For test TermAppISO Int Routing Regression/TermAppISONFT_IR_Regression (test 1, from 2020-12-09 12:48:00 to 2020-12-09 14:10:00) and for outcome timeout, the following function(s) had response time(s) in excess of 1 second during the portion of the test where the load did not exceed the current production observed/projected peak.
|   |Basename                         |Outcome | Count| Percent|   Resp| StdDev|
|:--|:--------------------------------|:-------|-----:|-------:|------:|------:|
|5  |transaction_advice_response_1230 |timeout |    15|   0.081| 99.999|      0|
|3  |authorisation_request_1100       |timeout |    41|   0.216| 99.999|      0|

D20201230 T142034 THREADS INSTANCES.png

D20201230 T142034 SUMMARY RATE.png

  • In the above graph, the items labelled Include and Exclude indicate, respectively, the intervals that should be included or excluded in the resource analysis. The resource analysis applies a linear regression of the resources consumed per server classification onto the total request arrival rate for that interval. Note that the analysis regresses to the arrival rate for the interval, but the determination of the outcome relates to the departures for that interval. The intervals should be long enough so that these numbers are reasonably close to each other.
  • The following table shows the full set of interval summary records obtained during the test. The Interval timestamp is the start time of the interval over which the metrics are summarised for that interval. A portion of the first metric record collected for each interval may relate to the previous interval. Long intervals or many intervals at the same load make this effect negligible.
  • The table of summary records provides the value of the load against which resource measurements are correlated. The correlation is determined using the least-squares linear module regressing the interval resource measurement onto the interval test loads.
  • Response time (Resp) in the table is the mean response time that an INSTANCE (device, customer, client, etc.) takes to complete a full cycle. A cycle is the set of states/actions that an INSTANCE undergoes from awakening from the idle state to the next time the same INSTANCE awakens from the idle state). The time for a cycle includes the idle time, the sum of all the individual response times of the functions/actions performed during the cycle of activity, and the think-times between the functions/actions performed during the cycle.
  • The Rate column shows the rate at which the INSTANCES exit the idle state, and hence, is the customer arrival rate measure in arrivals per second.
  • The summary table (or possibly a cut-down version of the summary table) is used to join against the corresponding resource usage tables, and over which the respective resource linear models are determined. The linear models include coefficients of the intercept (which is interpreted as resource usage unrelated to the applied test load) and the slope of the line (which is interpreted as the increment in the resource usage for each unit increment of the test load. This slope coefficient, therefore, gives us a measure of the resource usage for each additional customer arrival per second.
|   |Interval            |   Rate|    Resp|Analysis |
|:--|:-------------------|------:|-------:|:--------|
|1  |2020-12-09 12:49:00 |  0.833|  32.361|Include  |
|2  |2020-12-09 12:50:00 |  1.150|  60.719|Include  |
|3  |2020-12-09 12:51:00 |  1.733|  52.957|Include  |
|4  |2020-12-09 12:52:00 |  1.750|  77.719|Include  |
|5  |2020-12-09 12:53:00 |  2.483|  72.167|Include  |
|6  |2020-12-09 12:54:00 |  2.483|  91.564|Include  |
|7  |2020-12-09 12:55:00 |  3.050|  78.250|Include  |
|8  |2020-12-09 12:56:00 |  3.300|  99.141|Include  |
|9  |2020-12-09 12:57:00 |  4.183|  94.995|Include  |
|10 |2020-12-09 12:58:00 |  4.783|  98.181|Include  |
|11 |2020-12-09 12:59:00 |  5.083|  90.298|Include  |
|12 |2020-12-09 13:00:00 |  5.283| 104.621|Include  |
|13 |2020-12-09 13:01:00 |  6.367|  91.998|Include  |
|14 |2020-12-09 13:02:00 |  5.750|  91.209|Include  |
|15 |2020-12-09 13:03:00 |  6.450| 104.022|Include  |
|16 |2020-12-09 13:04:00 |  7.067|  96.973|Include  |
|17 |2020-12-09 13:05:00 |  7.550|  96.780|Include  |
|18 |2020-12-09 13:06:00 |  6.850| 106.961|Include  |
|19 |2020-12-09 13:07:00 |  8.317|  95.579|Include  |
|20 |2020-12-09 13:08:00 |  8.567| 106.981|Include  |
|21 |2020-12-09 13:09:00 |  8.967| 103.470|Include  |
|22 |2020-12-09 13:10:00 |  9.317| 104.358|Include  |
|23 |2020-12-09 13:11:00 |  9.967| 107.117|Include  |
|24 |2020-12-09 13:12:00 | 10.117| 110.579|Include  |
|25 |2020-12-09 13:13:00 | 10.733| 104.920|Include  |
|26 |2020-12-09 13:14:00 | 11.217| 105.362|Include  |
|27 |2020-12-09 13:15:00 | 11.667| 105.547|Include  |
|28 |2020-12-09 13:16:00 | 12.367| 100.520|Include  |
|29 |2020-12-09 13:17:00 | 12.233| 104.733|Include  |
|30 |2020-12-09 13:18:00 | 12.917| 105.243|Include  |
|31 |2020-12-09 13:19:00 | 12.817| 108.103|Include  |
|32 |2020-12-09 13:20:00 | 12.500| 107.559|Include  |
|33 |2020-12-09 13:21:00 | 12.800| 110.174|Include  |
|34 |2020-12-09 13:22:00 | 13.383| 112.671|Include  |
|35 |2020-12-09 13:23:00 | 12.683| 114.799|Include  |
|36 |2020-12-09 13:24:00 | 13.317| 110.233|Include  |
|37 |2020-12-09 13:25:00 | 13.350|  98.905|Exclude  |
|38 |2020-12-09 13:26:00 | 13.867| 107.117|Exclude  |
|39 |2020-12-09 13:27:00 | 14.650| 106.948|Exclude  |
|40 |2020-12-09 13:28:00 | 15.233| 103.980|Exclude  |
|41 |2020-12-09 13:29:00 | 14.317| 101.845|Exclude  |
|42 |2020-12-09 13:30:00 | 15.400| 108.810|Exclude  |
|43 |2020-12-09 13:31:00 | 16.583| 100.677|Exclude  |
|44 |2020-12-09 13:32:00 | 16.283| 105.890|Exclude  |
|45 |2020-12-09 13:33:00 | 16.667| 103.072|Exclude  |
|46 |2020-12-09 13:34:00 | 16.450| 108.248|Exclude  |
|47 |2020-12-09 13:35:00 | 16.883| 101.989|Exclude  |
|48 |2020-12-09 13:36:00 | 17.217| 110.521|Exclude  |
|49 |2020-12-09 13:37:00 | 18.000| 103.848|Exclude  |
|50 |2020-12-09 13:38:00 | 17.850| 111.535|Exclude  |
|51 |2020-12-09 13:39:00 | 18.150| 108.950|Exclude  |
|52 |2020-12-09 13:40:00 | 18.167| 111.392|Exclude  |
|53 |2020-12-09 13:41:00 | 20.200| 108.052|Exclude  |
|54 |2020-12-09 13:42:00 | 19.883| 109.412|Exclude  |
|55 |2020-12-09 13:43:00 | 21.033| 108.087|Exclude  |
|56 |2020-12-09 13:44:00 | 20.333| 108.100|Exclude  |
|57 |2020-12-09 13:45:00 | 21.783| 104.860|Exclude  |
|58 |2020-12-09 13:46:00 | 22.583| 109.896|Exclude  |
|59 |2020-12-09 13:47:00 | 22.817| 103.803|Exclude  |
|60 |2020-12-09 13:48:00 | 23.300| 103.912|Exclude  |
|61 |2020-12-09 13:49:00 | 22.967| 108.128|Exclude  |
|62 |2020-12-09 13:50:00 | 23.617| 103.592|Exclude  |
|63 |2020-12-09 13:51:00 | 23.917| 106.455|Exclude  |
|64 |2020-12-09 13:52:00 | 23.917| 107.553|Exclude  |
|65 |2020-12-09 13:53:00 | 24.833| 107.767|Exclude  |
|66 |2020-12-09 13:54:00 | 24.967| 108.062|Exclude  |
|67 |2020-12-09 13:55:00 | 25.717| 104.733|Exclude  |
|68 |2020-12-09 13:56:00 | 26.167| 110.003|Exclude  |
|69 |2020-12-09 13:57:00 | 26.250| 108.603|Exclude  |
|70 |2020-12-09 13:58:00 | 27.650| 112.600|Exclude  |
|71 |2020-12-09 13:59:00 | 28.950| 104.226|Exclude  |
|72 |2020-12-09 14:00:00 | 26.833| 104.947|Exclude  |
|73 |2020-12-09 14:01:00 | 29.417| 104.806|Exclude  |
|74 |2020-12-09 14:02:00 | 27.417| 110.839|Exclude  |
|75 |2020-12-09 14:03:00 | 29.300| 106.948|Exclude  |
|76 |2020-12-09 14:04:00 | 29.567| 106.652|Exclude  |
|77 |2020-12-09 14:05:00 | 28.833| 110.173|Exclude  |
|78 |2020-12-09 14:06:00 | 29.867| 111.183|Exclude  |
|79 |2020-12-09 14:07:00 | 30.183| 108.113|Exclude  |
|80 |2020-12-09 14:08:00 | 30.550| 111.022|Exclude  |
|81 |2020-12-09 14:09:00 | 32.633| 109.178|Exclude  |
|82 |2020-12-09 14:10:00 | 31.750| 109.435|Exclude  |
  • The following table is a cut down version of the full session summary table. The rows removed from the table are those that an inspection of the performance data and the observations made during the test indicate that they should not be included in the analysis. The primary reasons for excluding a row from the analysis include where the rows cover periods where the performance profile has changed for an identifiable reason unrelated to the test load (for example, interference in the test); a period where the test conditions were not met (for example, incorrect logging levels are set); periods where the applied load has saturated a component inducing an unacceptably high error rate, changing the profile of resource usage; etc.
|   |Interval            |   Rate|    Resp|Analysis |
|:--|:-------------------|------:|-------:|:--------|
|1  |2020-12-09 12:49:00 |  0.833|  32.361|Include  |
|2  |2020-12-09 12:50:00 |  1.150|  60.719|Include  |
|3  |2020-12-09 12:51:00 |  1.733|  52.957|Include  |
|4  |2020-12-09 12:52:00 |  1.750|  77.719|Include  |
|5  |2020-12-09 12:53:00 |  2.483|  72.167|Include  |
|6  |2020-12-09 12:54:00 |  2.483|  91.564|Include  |
|7  |2020-12-09 12:55:00 |  3.050|  78.250|Include  |
|8  |2020-12-09 12:56:00 |  3.300|  99.141|Include  |
|9  |2020-12-09 12:57:00 |  4.183|  94.995|Include  |
|10 |2020-12-09 12:58:00 |  4.783|  98.181|Include  |
|11 |2020-12-09 12:59:00 |  5.083|  90.298|Include  |
|12 |2020-12-09 13:00:00 |  5.283| 104.621|Include  |
|13 |2020-12-09 13:01:00 |  6.367|  91.998|Include  |
|14 |2020-12-09 13:02:00 |  5.750|  91.209|Include  |
|15 |2020-12-09 13:03:00 |  6.450| 104.022|Include  |
|16 |2020-12-09 13:04:00 |  7.067|  96.973|Include  |
|17 |2020-12-09 13:05:00 |  7.550|  96.780|Include  |
|18 |2020-12-09 13:06:00 |  6.850| 106.961|Include  |
|19 |2020-12-09 13:07:00 |  8.317|  95.579|Include  |
|20 |2020-12-09 13:08:00 |  8.567| 106.981|Include  |
|21 |2020-12-09 13:09:00 |  8.967| 103.470|Include  |
|22 |2020-12-09 13:10:00 |  9.317| 104.358|Include  |
|23 |2020-12-09 13:11:00 |  9.967| 107.117|Include  |
|24 |2020-12-09 13:12:00 | 10.117| 110.579|Include  |
|25 |2020-12-09 13:13:00 | 10.733| 104.920|Include  |
|26 |2020-12-09 13:14:00 | 11.217| 105.362|Include  |
|27 |2020-12-09 13:15:00 | 11.667| 105.547|Include  |
|28 |2020-12-09 13:16:00 | 12.367| 100.520|Include  |
|29 |2020-12-09 13:17:00 | 12.233| 104.733|Include  |
|30 |2020-12-09 13:18:00 | 12.917| 105.243|Include  |
|31 |2020-12-09 13:19:00 | 12.817| 108.103|Include  |
|32 |2020-12-09 13:20:00 | 12.500| 107.559|Include  |
|33 |2020-12-09 13:21:00 | 12.800| 110.174|Include  |
|34 |2020-12-09 13:22:00 | 13.383| 112.671|Include  |
|35 |2020-12-09 13:23:00 | 12.683| 114.799|Include  |
|36 |2020-12-09 13:24:00 | 13.317| 110.233|Include  |
  • The following tables show estimates and projections of CPU usage for each class (by hosted function) of server where some confidence in these numbers is reasonable. A coefficient is determined reasonable by the significance of the respective class coefficient's p-value.
  • Where there is some significant response of the resource utilisation to the applied load, and where there is some confidence in this correlation, an estimate of the resource usage can be determined for capacity planning and/or comparison purposes. Here, the column Est Use @ 30.000000/s is the expected CPU usage at 30 requests per second. The column Estimate should be used as a factor to multiply by the transaction rate in order to estimate the resource usage required to support that transaction rate. The column Std. Error should be used to determine the confidence interval around the estimate:
|   |Class |Test                              |Measure   |Units                    |Coef | Estimate| Std. Error| t value| p.value| Est Use @ 30.000000/s|
|:--|:-----|:---------------------------------|:---------|:------------------------|:----|--------:|----------:|-------:|-------:|---------------------:|
|1  |LOAD  |TermAppISO Int Routing Regression |CPU Usage |Percent single processor |Rate |  20.7203|     0.4634| 44.7170|       0|              621.6092|
|4  |MODEL |TermAppISO Int Routing Regression |CPU Usage |Percent single processor |Rate |  26.6450|     0.6526| 40.8301|       0|              799.3494|
  • In some cases, it is possible to estimate with confidence the background workload on the various servers and server groups, the resource usage which is independent of the applied test load. The column Estimate indicates this. The column Std. Error should be used to determine a confidence interval:
|   |Class |Test                              |Measure   |Units                    |Coef | Estimate| Std. Error| t value| p.value|
|:--|:-----|:---------------------------------|:---------|:------------------------|:----|--------:|----------:|-------:|-------:|
|1  |LOAD  |TermAppISO Int Routing Regression |CPU Usage |Percent single processor |Bias |  14.7311|     4.0598|  3.6286|   9e-04|
|4  |MODEL |TermAppISO Int Routing Regression |CPU Usage |Percent single processor |Bias |  26.6067|     5.7176|  4.6535|   0e+00|
  • The following tables show estimates and projections of Network usage for each class (by hosted function) of server where some confidence in these numbers is reasonable. A coefficient is determined reasonable by the significance of the respective class coefficient's p-value.
  • Where there is some significant response of the resource utilisation to the applied load, and where there is some confidence in this correlation, an estimate of the resource usage can be determined for capacity planning and/or comparison purposes. Here, the column Est Use @ 30.000000/s is the expected Network usage at 30 requests per second. The column Estimate should be used as a factor to multiply by the transaction rate in order to estimate the resource usage required to support that transaction rate. The column Std. Error should be used to determine the confidence interval around the estimate:
|   |Class |Test                              |Measure            |Units           |Coef | Estimate| Std. Error| t value| p.value| Est Use @ 30.000000/s|
|:--|:-----|:---------------------------------|:------------------|:---------------|:----|--------:|----------:|-------:|-------:|---------------------:|
|2  |LOAD  |TermAppISO Int Routing Regression |Network Usage recv |bits per second |Rate | 22366.38|   498.6453| 44.8543|       0|              670991.3|
|3  |LOAD  |TermAppISO Int Routing Regression |Network Usage sent |bits per second |Rate | 28100.64|  1178.6035| 23.8423|       0|              843019.3|
|5  |MODEL |TermAppISO Int Routing Regression |Network Usage recv |bits per second |Rate | 25105.21|   829.0048| 30.2836|       0|              753156.4|
|6  |MODEL |TermAppISO Int Routing Regression |Network Usage sent |bits per second |Rate | 23424.88|   681.3062| 34.3823|       0|              702746.5|
  • In some cases, it is possible to estimate with confidence the background workload on the various servers and server groups, the resource usage which is independent of the applied test load. The column Estimate indicates this. The column Std. Error should be used to determine a confidence interval:
|   |Class |Test                              |Measure            |Units           |Coef |  Estimate| Std. Error| t value| p.value|
|:--|:-----|:---------------------------------|:------------------|:---------------|:----|---------:|----------:|-------:|-------:|
|2  |LOAD  |TermAppISO Int Routing Regression |Network Usage recv |bits per second |Bias |  51189.74|   4368.860| 11.7170|       0|
|3  |LOAD  |TermAppISO Int Routing Regression |Network Usage sent |bits per second |Bias | 117329.42|  10326.287| 11.3622|       0|
|5  |MODEL |TermAppISO Int Routing Regression |Network Usage recv |bits per second |Bias |  48342.49|   7263.291|  6.6557|       0|
|6  |MODEL |TermAppISO Int Routing Regression |Network Usage sent |bits per second |Bias | 191480.04|   5969.236| 32.0778|       0|

Feedback

Record

Notes from the test

Landscape

  • The analysis is performed for each test within the test session and for each unique Classification of a server in the collected Server landscape/Classification list. For each Interval the resources are summed across all the servers in that class. This means that the resource usage coefficients and projections are for the total resource usage across the Classification.
|   |Classification |Server    |IP_Address     |OSType |Description   |Hardware_Support |Server_Support  |Application_Support |SOURCE_FILE                            |
|:--|:--------------|:---------|:--------------|:------|:-------------|:----------------|:---------------|:-------------------|:--------------------------------------|
|1  |LOAD           |LOAD0     |176.67.166.86  |Linux  |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |
|2  |LOAD           |LOAD1     |176.67.166.89  |Linux  |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |
|3  |MODEL          |MODELAPP0 |176.67.166.20  |Linux  |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |
|4  |MODEL          |MODELAPP1 |176.67.166.72  |Linux  |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |
|5  |NETWORK        |NETWORK   |109.123.111.17 |Linux  |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |

Analysis

Test: TermAppISO Int Routing Regression

  • This test started at 2020-12-09 12:48:00 and ended at 2020-12-09 14:10:00.

Resource usage for platform Linux

Server class LOAD CPU usage

CPU Usage - cpu.cpu.system.user
Call:
lm(formula = Value.sum ~ Rate, data = summ.class.include)

Residuals:
    Min      1Q  Median      3Q     Max 
-21.838  -6.124   1.070   5.548  24.418 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  14.7311     4.0598   3.629 0.000925 ***
Rate         20.7203     0.4634  44.717  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 11.21 on 34 degrees of freedom
Multiple R-squared:  0.9833,	Adjusted R-squared:  0.9828 
F-statistic:  2000 on 1 and 34 DF,  p-value: < 2.2e-16

D20201230 T142034 Linux CPU cpucpusystemuser USAGE BY LOAD CPUUsagePercentsingleprocessor LOAD TermAppISONFT IR Regression 1.png

Server class LOAD Network usage

Network Usage recv - recv
Call:
lm(formula = Value.sum ~ Rate, data = summ.class.include)

Residuals:
   Min     1Q Median     3Q    Max 
-27674  -7056   -652   6361  32529 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  51189.7     4368.9   11.72 1.74e-13 ***
Rate         22366.4      498.6   44.85  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 12060 on 34 degrees of freedom
Multiple R-squared:  0.9834,	Adjusted R-squared:  0.9829 
F-statistic:  2012 on 1 and 34 DF,  p-value: < 2.2e-16

D20201230 T142034 Linux Network recv USAGE BY LOAD NetworkUsagerecvbitspersecond LOAD TermAppISONFT IR Regression 1.png

Network Usage sent - sent
Call:
lm(formula = Value.sum ~ Rate, data = summ.class.include)

Residuals:
   Min     1Q Median     3Q    Max 
-42797 -15649  -4421   5678 108186 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   117329      10326   11.36 4.02e-13 ***
Rate           28101       1179   23.84  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 28510 on 34 degrees of freedom
Multiple R-squared:  0.9436,	Adjusted R-squared:  0.9419 
F-statistic: 568.5 on 1 and 34 DF,  p-value: < 2.2e-16

D20201230 T142034 Linux Network sent USAGE BY LOAD NetworkUsagesentbitspersecond LOAD TermAppISONFT IR Regression 1.png

Server class MODEL CPU usage

CPU Usage - cpu.cpu.system.user
Call:
lm(formula = Value.sum ~ Rate, data = summ.class.include)

Residuals:
     Min       1Q   Median       3Q      Max 
-30.5387 -12.1501  -0.7277   7.8843  30.0418 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  26.6067     5.7176   4.653 4.81e-05 ***
Rate         26.6450     0.6526  40.830  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.79 on 34 degrees of freedom
Multiple R-squared:   0.98,	Adjusted R-squared:  0.9794 
F-statistic:  1667 on 1 and 34 DF,  p-value: < 2.2e-16

D20201230 T142034 Linux CPU cpucpusystemuser USAGE BY LOAD CPUUsagePercentsingleprocessor MODEL TermAppISONFT IR Regression 1.png

Server class MODEL Network usage

Network Usage recv - recv
Call:
lm(formula = Value.sum ~ Rate, data = summ.class.include)

Residuals:
   Min     1Q Median     3Q    Max 
-34370 -14137   -711   9581  57356 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)    48342       7263   6.656 1.23e-07 ***
Rate           25105        829  30.284  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 20050 on 34 degrees of freedom
Multiple R-squared:  0.9643,	Adjusted R-squared:  0.9632 
F-statistic: 917.1 on 1 and 34 DF,  p-value: < 2.2e-16

D20201230 T142034 Linux Network recv USAGE BY LOAD NetworkUsagerecvbitspersecond MODEL TermAppISONFT IR Regression 1.png

Network Usage sent - sent
Call:
lm(formula = Value.sum ~ Rate, data = summ.class.include)

Residuals:
   Min     1Q Median     3Q    Max 
-31656 -11547    609  12572  32049 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 191480.0     5969.2   32.08   <2e-16 ***
Rate         23424.9      681.3   34.38   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 16480 on 34 degrees of freedom
Multiple R-squared:  0.972,	Adjusted R-squared:  0.9712 
F-statistic:  1182 on 1 and 34 DF,  p-value: < 2.2e-16

D20201230 T142034 Linux Network sent USAGE BY LOAD NetworkUsagesentbitspersecond MODEL TermAppISONFT IR Regression 1.png

Performance

  • For test TermAppISO Int Routing Regression/TermAppISONFT_IR_Regression (Test 1, from 2020-12-09 12:48:00 to 2020-12-09 14:10:00), summary of response times by operation/function-outcome over the range of loads where the arrival rate did not exceed the observed or required peak target rate of 15 arrivals per second (this rate is as opposed to the projected/future peak rate). This cut-off time occured at 2020-12-09 13:27:00. The descriptive statistics mean, median and sd are calculated over the entire time-frame that the arrival rate did not exceed the target arrival rate, which includes any outliers caused by possible external influences (which are removed for the regression analysis).
  • Distribution of response times where rate does not exceed the target cutoff target rate for outcome disconnect:

D20201230 T142034 RESP SUMM 01 TermAppISONFT IR Regression disconnect.png

|   |Basename                   |Outcome    | Count| Percent|  Resp| StdDev|
|:--|:--------------------------|:----------|-----:|-------:|-----:|------:|
|1  |authorisation_request_1100 |disconnect |   304|   1.602| 4.166|  0.205|
  • Distribution of response times where rate does not exceed the target cutoff target rate for outcome OK:

D20201230 T142034 RESP SUMM 01 TermAppISONFT IR Regression OK.png

|   |Basename                         |Outcome | Count| Percent|  Resp| StdDev|
|:--|:--------------------------------|:-------|-----:|-------:|-----:|------:|
|2  |authorisation_request_1100       |OK      | 18635|  98.182| 6.813|  8.841|
|4  |transaction_advice_response_1230 |OK      | 18553|  99.919| 0.736|  0.462|
  • Distribution of response times where rate does not exceed the target cutoff target rate for outcome timeout:

D20201230 T142034 RESP SUMM 01 TermAppISONFT IR Regression timeout.png

|   |Basename                         |Outcome | Count| Percent|   Resp| StdDev|
|:--|:--------------------------------|:-------|-----:|-------:|------:|------:|
|5  |transaction_advice_response_1230 |timeout |    15|   0.081| 99.999|      0|
|3  |authorisation_request_1100       |timeout |    41|   0.216| 99.999|      0|

Operation authorisation_request_1100

D20201230 T142034 Function authorisation request 1100 RESP.png

D20201230 T142034 Function authorisation request 1100 RATE.png

Operation transaction_advice_response_1230

D20201230 T142034 Function transaction advice response 1230 RESP.png

D20201230 T142034 Function transaction advice response 1230 RATE.png

Resource Usage

Linux Server CPU Usage

TermAppISO Int Routing Regression

Server LOAD0

D20201230 T142034 CMLXLINP CPU USAGE 01 TermAppISONFT IR Regression LOAD0.png

Server LOAD1

D20201230 T142034 CMLXLINP CPU USAGE 01 TermAppISONFT IR Regression LOAD1.png

Server MODELAPP0

D20201230 T142034 CMLXLINP CPU USAGE 01 TermAppISONFT IR Regression MODELAPP0.png

Server MODELAPP1

D20201230 T142034 CMLXLINP CPU USAGE 01 TermAppISONFT IR Regression MODELAPP1.png

Linux Server Disk Activity

Server LOAD1

D20201230 T142034 CMLXLINP DISK METRIC LOAD1 reads.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD1 reads merged.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD1 read sectors.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD1 read milli.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD1 writes.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD1 writes merged.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD1 write sectors.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD1 write milli.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD1 io inprogress.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD1 io milli.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD1 io weighted milli.png

Server MODELAPP1

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP1 reads.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP1 reads merged.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP1 read sectors.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP1 read milli.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP1 writes.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP1 writes merged.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP1 write sectors.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP1 write milli.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP1 io inprogress.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP1 io milli.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP1 io weighted milli.png

Server MODELAPP0

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP0 reads.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP0 reads merged.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP0 read sectors.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP0 read milli.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP0 writes.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP0 writes merged.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP0 write sectors.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP0 write milli.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP0 io inprogress.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP0 io milli.png

D20201230 T142034 CMLXLINP DISK METRIC MODELAPP0 io weighted milli.png

Server LOAD0

D20201230 T142034 CMLXLINP DISK METRIC LOAD0 reads.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD0 reads merged.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD0 read sectors.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD0 read milli.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD0 writes.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD0 writes merged.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD0 write sectors.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD0 write milli.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD0 io inprogress.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD0 io milli.png

D20201230 T142034 CMLXLINP DISK METRIC LOAD0 io weighted milli.png

Linux Server Memory Usage

Server LOAD1

D20201230 T142034 CMLXLINP MEMMEM METRIC LOAD1.png

Server MODELAPP1

D20201230 T142034 CMLXLINP MEMMEM METRIC MODELAPP1.png

Server MODELAPP0

D20201230 T142034 CMLXLINP MEMMEM METRIC MODELAPP0.png

Server LOAD0

D20201230 T142034 CMLXLINP MEMMEM METRIC LOAD0.png

Linux Server Swap Usage

Server LOAD1

D20201230 T142034 CMLXLINP MEMSWAP METRIC LOAD1.png

Server MODELAPP1

D20201230 T142034 CMLXLINP MEMSWAP METRIC MODELAPP1.png

Server MODELAPP0

D20201230 T142034 CMLXLINP MEMSWAP METRIC MODELAPP0.png

Server LOAD0

D20201230 T142034 CMLXLINP MEMSWAP METRIC LOAD0.png

Linux Server Network Usage

Server LOAD1

D20201230 T142034 CMLXLINP NET METRIC LOAD1 ibytes.png

D20201230 T142034 CMLXLINP NET METRIC LOAD1 ipackets.png

D20201230 T142034 CMLXLINP NET METRIC LOAD1 obytes.png

D20201230 T142034 CMLXLINP NET METRIC LOAD1 opackets.png

Server MODELAPP1

D20201230 T142034 CMLXLINP NET METRIC MODELAPP1 ibytes.png

D20201230 T142034 CMLXLINP NET METRIC MODELAPP1 ipackets.png

D20201230 T142034 CMLXLINP NET METRIC MODELAPP1 obytes.png

D20201230 T142034 CMLXLINP NET METRIC MODELAPP1 opackets.png

Server MODELAPP0

D20201230 T142034 CMLXLINP NET METRIC MODELAPP0 ibytes.png

D20201230 T142034 CMLXLINP NET METRIC MODELAPP0 ipackets.png

D20201230 T142034 CMLXLINP NET METRIC MODELAPP0 obytes.png

D20201230 T142034 CMLXLINP NET METRIC MODELAPP0 opackets.png

Server LOAD0

D20201230 T142034 CMLXLINP NET METRIC LOAD0 ibytes.png

D20201230 T142034 CMLXLINP NET METRIC LOAD0 ipackets.png

D20201230 T142034 CMLXLINP NET METRIC LOAD0 obytes.png

D20201230 T142034 CMLXLINP NET METRIC LOAD0 opackets.png

Wintel Server Resource Usage

Test TermAppISO Int Routing Regression

Wintel Server Resource Usage

Test TermAppISO Int Routing Regression

pSeries AIX Server Reource Usage

Test TermAppISO Int Routing Regression

* Code Magus Limited