EcoSystem TermAppISO - 2022/10/06 NFT

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Contents

Contributors

Hayward (talk) 07:03, 7 October 2022 (UTC) mailto:email@codemagus.com


Summary

  • The following test(s) were included in this test session.
|LABEL         |Description |Start               |End                 |
|:-------------|:-----------|:-------------------|:-------------------|
|TermAppISONFT |TermAppISO  |2022-10-06 17:13:01 |2022-10-06 21:55:02 |

20221006 171301 THREADS INSTANCES.png

20221006 171301 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   |2022-10-06 17:15:00 |   0.800|  67.848|Include  |
|2   |2022-10-06 17:16:00 |   1.583|  63.340|Include  |
|3   |2022-10-06 17:17:00 |   1.683|  80.353|Include  |
|4   |2022-10-06 17:18:00 |   2.517|  76.146|Include  |
|5   |2022-10-06 17:19:00 |   2.183|  94.815|Include  |
|6   |2022-10-06 17:20:00 |   2.950|  93.964|Include  |
|7   |2022-10-06 17:21:00 |   3.367| 101.333|Include  |
|8   |2022-10-06 17:22:00 |   4.450|  92.316|Include  |
|9   |2022-10-06 17:23:00 |   4.533|  91.116|Include  |
|10  |2022-10-06 17:24:00 |   5.017|  93.857|Include  |
|11  |2022-10-06 17:25:00 |   5.367|  93.093|Include  |
|12  |2022-10-06 17:26:00 |   6.067|  94.574|Include  |
|13  |2022-10-06 17:27:00 |   6.400| 107.899|Include  |
|14  |2022-10-06 17:28:00 |   6.700|  88.562|Include  |
|15  |2022-10-06 17:29:00 |   6.817| 103.750|Include  |
|16  |2022-10-06 17:30:00 |   7.817|  95.906|Include  |
|17  |2022-10-06 17:31:00 |   7.733|  97.027|Include  |
|18  |2022-10-06 17:32:00 |   8.367|  97.201|Include  |
|19  |2022-10-06 17:33:00 |   8.100| 105.189|Include  |
|20  |2022-10-06 17:34:00 |   9.550| 104.800|Include  |
|21  |2022-10-06 17:35:00 |   9.200| 111.798|Include  |
|22  |2022-10-06 17:36:00 |  10.100| 104.560|Include  |
|23  |2022-10-06 17:37:00 |  10.383| 107.169|Include  |
|24  |2022-10-06 17:38:00 |  10.733|  99.769|Include  |
|25  |2022-10-06 17:39:00 |  10.867| 106.134|Include  |
|26  |2022-10-06 17:40:00 |  12.867| 102.309|Include  |
|27  |2022-10-06 17:41:00 |  11.800| 100.452|Include  |
|28  |2022-10-06 17:42:00 |  12.283| 102.444|Include  |
|29  |2022-10-06 17:43:00 |  13.017| 107.970|Include  |
|30  |2022-10-06 17:44:00 |  14.000| 104.474|Include  |
|31  |2022-10-06 17:45:00 |  13.200| 105.570|Include  |
|32  |2022-10-06 17:46:00 |  14.383| 109.142|Include  |
|33  |2022-10-06 17:47:00 |  15.000| 107.583|Include  |
|34  |2022-10-06 17:48:00 |  15.533|  99.676|Include  |
|35  |2022-10-06 17:49:00 |  15.700| 104.924|Include  |
|36  |2022-10-06 17:50:00 |  16.233| 109.536|Include  |
|37  |2022-10-06 17:51:00 |  16.150| 102.128|Include  |
|38  |2022-10-06 17:52:00 |  17.567| 105.823|Include  |
|39  |2022-10-06 17:53:00 |  16.783| 107.044|Include  |
|40  |2022-10-06 17:54:00 |  17.750| 103.968|Include  |
|41  |2022-10-06 17:55:00 |  18.183| 108.760|Include  |
|42  |2022-10-06 17:56:00 |  18.700| 108.113|Include  |
|43  |2022-10-06 17:57:00 |  19.517| 108.279|Include  |
|44  |2022-10-06 17:58:00 |  19.817| 102.952|Include  |
|45  |2022-10-06 17:59:00 |  19.600| 109.283|Include  |
|46  |2022-10-06 18:00:00 |  19.867| 108.288|Include  |
|47  |2022-10-06 18:01:00 |  21.583| 106.509|Include  |
|48  |2022-10-06 18:02:00 |  21.550| 105.111|Include  |
|49  |2022-10-06 18:03:00 |  20.933| 106.854|Include  |
|50  |2022-10-06 18:04:00 |  21.817| 105.874|Include  |
|51  |2022-10-06 18:05:00 |  21.500| 106.201|Include  |
|52  |2022-10-06 18:06:00 |  22.883| 110.774|Include  |
|53  |2022-10-06 18:07:00 |  23.433| 112.948|Include  |
|54  |2022-10-06 18:08:00 |  24.250| 102.189|Include  |
|55  |2022-10-06 18:09:00 |  24.033| 108.187|Include  |
|56  |2022-10-06 18:10:00 |  25.567| 112.066|Include  |
|57  |2022-10-06 18:11:00 |  25.017| 105.000|Include  |
|58  |2022-10-06 18:12:00 |  25.650| 108.145|Include  |
|59  |2022-10-06 18:13:00 |  25.933| 104.846|Include  |
|60  |2022-10-06 18:14:00 |  26.533| 107.493|Include  |
|61  |2022-10-06 18:15:00 |  26.850| 106.837|Include  |
|62  |2022-10-06 18:16:00 |  27.183| 108.476|Include  |
|63  |2022-10-06 18:17:00 |  27.550| 110.807|Include  |
|64  |2022-10-06 18:18:00 |  28.600| 104.437|Include  |
|65  |2022-10-06 18:19:00 |  28.267| 110.172|Include  |
|66  |2022-10-06 18:20:00 |  29.183| 106.025|Include  |
|67  |2022-10-06 18:21:00 |  29.567| 107.445|Include  |
|68  |2022-10-06 18:22:00 |  30.883| 107.060|Include  |
|69  |2022-10-06 18:23:00 |  30.167| 106.690|Include  |
|70  |2022-10-06 18:24:00 |  31.433| 104.634|Include  |
|71  |2022-10-06 18:25:00 |  32.517| 108.011|Include  |
|72  |2022-10-06 18:26:00 |  31.567| 104.941|Include  |
|73  |2022-10-06 18:27:00 |  31.217| 105.405|Include  |
|74  |2022-10-06 18:28:00 |  32.883| 107.565|Include  |
|75  |2022-10-06 18:29:00 |  31.933| 109.093|Include  |
|76  |2022-10-06 18:30:00 |  33.483| 108.677|Include  |
|77  |2022-10-06 18:31:00 |  33.067| 109.338|Include  |
|78  |2022-10-06 18:32:00 |  35.250| 108.146|Include  |
|79  |2022-10-06 18:33:00 |  34.633| 110.400|Include  |
|80  |2022-10-06 18:34:00 |  34.833| 106.787|Include  |
|81  |2022-10-06 18:35:00 |  34.900| 105.281|Include  |
|82  |2022-10-06 18:36:00 |  35.517| 110.214|Include  |
|83  |2022-10-06 18:37:00 |  36.883| 110.688|Include  |
|84  |2022-10-06 18:38:00 |  37.183| 110.777|Include  |
|85  |2022-10-06 18:39:00 |  37.667| 108.559|Include  |
|86  |2022-10-06 18:40:00 |  37.650| 104.127|Include  |
|87  |2022-10-06 18:41:00 |  37.567| 107.726|Include  |
|88  |2022-10-06 18:42:00 |  38.883| 108.599|Include  |
|89  |2022-10-06 18:43:00 |  38.133| 108.027|Include  |
|90  |2022-10-06 18:44:00 |  39.333| 106.925|Include  |
|91  |2022-10-06 18:45:00 |  38.650| 110.777|Include  |
|92  |2022-10-06 18:46:00 |  40.433| 108.921|Include  |
|93  |2022-10-06 18:47:00 |  40.517| 108.902|Include  |
|94  |2022-10-06 18:48:00 |  39.600| 102.959|Include  |
|95  |2022-10-06 18:49:00 |  42.150| 114.011|Include  |
|96  |2022-10-06 18:50:00 |  42.500| 109.865|Include  |
|97  |2022-10-06 18:51:00 |  41.733| 109.886|Include  |
|98  |2022-10-06 18:52:00 |  41.933| 107.073|Include  |
|99  |2022-10-06 18:53:00 |  44.300| 109.507|Include  |
|100 |2022-10-06 18:54:00 |  45.533| 109.336|Include  |
|101 |2022-10-06 18:55:00 |  43.017| 108.902|Include  |
|102 |2022-10-06 18:56:00 |  44.300| 108.613|Include  |
|103 |2022-10-06 18:57:00 |  45.767| 108.359|Include  |
|104 |2022-10-06 18:58:00 |  46.167| 106.516|Include  |
|105 |2022-10-06 18:59:00 |  46.000| 107.977|Include  |
|106 |2022-10-06 19:00:00 |  47.733| 105.410|Include  |
|107 |2022-10-06 19:01:00 |  46.700| 109.141|Include  |
|108 |2022-10-06 19:02:00 |  47.883| 106.683|Include  |
|109 |2022-10-06 19:03:00 |  48.883| 105.559|Include  |
|110 |2022-10-06 19:04:00 |  47.667| 109.393|Include  |
|111 |2022-10-06 19:05:00 |  48.750| 106.928|Include  |
|112 |2022-10-06 19:06:00 |  50.000| 107.482|Include  |
|113 |2022-10-06 19:07:00 |  50.067| 104.197|Include  |
|114 |2022-10-06 19:08:00 |  49.650| 108.035|Include  |
|115 |2022-10-06 19:09:00 |  51.150| 108.813|Include  |
|116 |2022-10-06 19:10:00 |  49.333| 109.283|Include  |
|117 |2022-10-06 19:11:00 |  50.167| 108.962|Include  |
|118 |2022-10-06 19:12:00 |  52.433| 110.405|Include  |
|119 |2022-10-06 19:13:00 |  50.467| 107.941|Include  |
|120 |2022-10-06 19:14:00 |  52.850| 108.796|Include  |
|121 |2022-10-06 19:15:00 |  53.667| 109.761|Include  |
|122 |2022-10-06 19:16:00 |  53.550| 106.836|Include  |
|123 |2022-10-06 19:17:00 |  54.150| 112.161|Include  |
|124 |2022-10-06 19:18:00 |  53.000| 106.870|Include  |
|125 |2022-10-06 19:19:00 |  55.567| 109.526|Include  |
|126 |2022-10-06 19:20:00 |  55.267| 106.439|Include  |
|127 |2022-10-06 19:21:00 |  57.000| 105.989|Include  |
|128 |2022-10-06 19:22:00 |  55.217| 109.684|Include  |
|129 |2022-10-06 19:23:00 |  56.267| 110.564|Include  |
|130 |2022-10-06 19:24:00 |  56.583| 106.918|Include  |
|131 |2022-10-06 19:25:00 |  57.433| 107.156|Include  |
|132 |2022-10-06 19:26:00 |  56.950| 108.550|Include  |
|133 |2022-10-06 19:27:00 |  58.250| 109.527|Include  |
|134 |2022-10-06 19:28:00 |  59.533| 107.199|Include  |
|135 |2022-10-06 19:29:00 |  58.933| 110.418|Include  |
|136 |2022-10-06 19:30:00 |  58.317| 108.238|Include  |
|137 |2022-10-06 19:31:00 |  61.317| 108.977|Exclude  |
|138 |2022-10-06 19:32:00 |  60.250| 104.695|Exclude  |
|139 |2022-10-06 19:33:00 |  58.917| 109.049|Include  |
|140 |2022-10-06 19:34:00 |  59.567| 112.095|Include  |
|141 |2022-10-06 19:35:00 |  61.817| 108.934|Exclude  |
|142 |2022-10-06 19:36:00 |  62.400| 109.306|Exclude  |
|143 |2022-10-06 19:37:00 |  61.267| 109.858|Exclude  |
|144 |2022-10-06 19:38:00 |  64.650| 110.087|Exclude  |
|145 |2022-10-06 19:39:00 |  62.850| 107.212|Exclude  |
|146 |2022-10-06 19:40:00 |  63.583| 109.242|Exclude  |
|147 |2022-10-06 19:41:00 |  65.417| 106.777|Exclude  |
|148 |2022-10-06 19:42:00 |  64.967| 108.154|Exclude  |
|149 |2022-10-06 19:43:00 |  63.633| 107.653|Exclude  |
|150 |2022-10-06 19:44:00 |  66.767| 109.041|Exclude  |
|151 |2022-10-06 19:45:00 |  67.267| 105.799|Exclude  |
|152 |2022-10-06 19:46:00 |  65.433| 107.399|Exclude  |
|153 |2022-10-06 19:47:00 |  66.733| 111.526|Exclude  |
|154 |2022-10-06 19:48:00 |  68.300| 108.830|Exclude  |
|155 |2022-10-06 19:49:00 |  66.917| 109.977|Exclude  |
|156 |2022-10-06 19:50:00 |  67.967| 108.902|Exclude  |
|157 |2022-10-06 19:51:00 |  68.817| 107.789|Exclude  |
|158 |2022-10-06 19:52:00 |  67.883| 108.004|Exclude  |
|159 |2022-10-06 19:53:00 |  68.850| 109.203|Exclude  |
|160 |2022-10-06 19:54:00 |  71.117| 109.296|Exclude  |
|161 |2022-10-06 19:55:00 |  69.367| 108.240|Exclude  |
|162 |2022-10-06 19:56:00 |  70.017| 107.356|Exclude  |
|163 |2022-10-06 19:57:00 |  71.517| 109.603|Exclude  |
|164 |2022-10-06 19:58:00 |  69.983| 108.330|Exclude  |
|165 |2022-10-06 19:59:00 |  71.550| 109.835|Exclude  |
|166 |2022-10-06 20:00:00 |  72.117| 111.907|Exclude  |
|167 |2022-10-06 20:01:00 |  73.533| 108.550|Exclude  |
|168 |2022-10-06 20:02:00 |  72.783| 108.563|Exclude  |
|169 |2022-10-06 20:03:00 |  73.817| 109.824|Exclude  |
|170 |2022-10-06 20:04:00 |  74.400| 110.450|Exclude  |
|171 |2022-10-06 20:05:00 |  72.300| 107.902|Exclude  |
|172 |2022-10-06 20:06:00 |  75.817| 109.510|Exclude  |
|173 |2022-10-06 20:07:00 |  73.917| 109.814|Exclude  |
|174 |2022-10-06 20:08:00 |  76.750| 107.447|Exclude  |
|175 |2022-10-06 20:09:00 |  74.917| 110.693|Exclude  |
|176 |2022-10-06 20:10:00 |  76.283| 109.307|Exclude  |
|177 |2022-10-06 20:11:00 |  77.283| 111.799|Exclude  |
|178 |2022-10-06 20:12:00 |  75.750| 107.806|Exclude  |
|179 |2022-10-06 20:13:00 |  77.283| 109.853|Exclude  |
|180 |2022-10-06 20:14:00 |  80.000| 110.713|Exclude  |
|181 |2022-10-06 20:15:00 |  77.000| 109.681|Exclude  |
|182 |2022-10-06 20:16:00 |  79.683| 108.028|Exclude  |
|183 |2022-10-06 20:17:00 |  80.517| 109.404|Exclude  |
|184 |2022-10-06 20:18:00 |  80.300| 108.968|Exclude  |
|185 |2022-10-06 20:19:00 |  80.500| 110.562|Exclude  |
|186 |2022-10-06 20:20:00 |  79.967| 108.903|Exclude  |
|187 |2022-10-06 20:21:00 |  80.267| 108.775|Exclude  |
|188 |2022-10-06 20:22:00 |  82.283| 108.424|Exclude  |
|189 |2022-10-06 20:23:00 |  81.333| 106.896|Exclude  |
|190 |2022-10-06 20:24:00 |  80.500| 110.022|Exclude  |
|191 |2022-10-06 20:25:00 |  81.850| 110.928|Exclude  |
|192 |2022-10-06 20:26:00 |  83.217| 110.768|Exclude  |
|193 |2022-10-06 20:27:00 |  82.917| 112.022|Exclude  |
|194 |2022-10-06 20:28:00 |  85.500| 110.986|Exclude  |
|195 |2022-10-06 20:29:00 |  83.800| 109.127|Exclude  |
|196 |2022-10-06 20:30:00 |  85.083| 108.673|Exclude  |
|197 |2022-10-06 20:31:00 |  83.900| 110.403|Exclude  |
|198 |2022-10-06 20:32:00 |  85.750| 110.287|Exclude  |
|199 |2022-10-06 20:33:00 |  85.800| 109.410|Exclude  |
|200 |2022-10-06 20:34:00 |  87.867| 108.607|Exclude  |
|201 |2022-10-06 20:35:00 |  86.933| 109.982|Exclude  |
|202 |2022-10-06 20:36:00 |  88.850| 109.397|Exclude  |
|203 |2022-10-06 20:37:00 |  88.567| 108.642|Exclude  |
|204 |2022-10-06 20:38:00 |  89.033| 108.976|Exclude  |
|205 |2022-10-06 20:39:00 |  89.333| 108.952|Exclude  |
|206 |2022-10-06 20:40:00 |  88.683| 109.142|Exclude  |
|207 |2022-10-06 20:41:00 |  90.300| 108.736|Exclude  |
|208 |2022-10-06 20:42:00 |  91.267| 108.826|Exclude  |
|209 |2022-10-06 20:43:00 |  92.900| 107.864|Exclude  |
|210 |2022-10-06 20:44:00 |  91.767| 106.417|Exclude  |
|211 |2022-10-06 20:45:00 |  91.717| 109.913|Exclude  |
|212 |2022-10-06 20:46:00 |  92.350| 110.035|Exclude  |
|213 |2022-10-06 20:47:00 |  91.983| 109.301|Exclude  |
|214 |2022-10-06 20:48:00 |  90.517| 110.302|Exclude  |
|215 |2022-10-06 20:49:00 |  92.400| 110.717|Exclude  |
|216 |2022-10-06 20:50:00 |  92.367| 108.268|Exclude  |
|217 |2022-10-06 20:51:00 |  95.917| 111.509|Exclude  |
|218 |2022-10-06 20:52:00 |  94.267| 108.900|Exclude  |
|219 |2022-10-06 20:53:00 |  96.900| 106.855|Exclude  |
|220 |2022-10-06 20:54:00 |  94.650| 109.332|Exclude  |
|221 |2022-10-06 20:55:00 |  96.483| 111.685|Exclude  |
|222 |2022-10-06 20:56:00 |  94.500| 108.045|Exclude  |
|223 |2022-10-06 20:57:00 |  96.083| 109.810|Exclude  |
|224 |2022-10-06 20:58:00 |  97.100| 110.710|Exclude  |
|225 |2022-10-06 20:59:00 |  99.167| 108.078|Exclude  |
|226 |2022-10-06 21:00:00 |  96.533| 107.385|Exclude  |
|227 |2022-10-06 21:01:00 |  98.667| 110.387|Exclude  |
|228 |2022-10-06 21:02:00 |  99.883| 109.790|Exclude  |
|229 |2022-10-06 21:03:00 |  99.633| 110.036|Exclude  |
|230 |2022-10-06 21:04:00 | 100.467| 107.608|Exclude  |
|231 |2022-10-06 21:05:00 | 102.333| 109.344|Exclude  |
|232 |2022-10-06 21:06:00 | 102.150| 107.924|Exclude  |
|233 |2022-10-06 21:07:00 | 101.900| 107.236|Exclude  |
|234 |2022-10-06 21:08:00 | 103.533| 108.936|Exclude  |
|235 |2022-10-06 21:09:00 | 102.550| 108.545|Exclude  |
|236 |2022-10-06 21:10:00 | 102.250| 107.971|Exclude  |
|237 |2022-10-06 21:11:00 | 100.000| 109.485|Exclude  |
|238 |2022-10-06 21:12:00 | 102.667| 109.749|Exclude  |
|239 |2022-10-06 21:13:00 | 102.817| 111.261|Exclude  |
|240 |2022-10-06 21:14:00 | 104.000| 110.322|Exclude  |
|241 |2022-10-06 21:15:00 | 104.483| 109.004|Exclude  |
|242 |2022-10-06 21:16:00 | 105.867| 108.569|Exclude  |
|243 |2022-10-06 21:17:00 | 103.033| 110.004|Exclude  |
|244 |2022-10-06 21:18:00 | 107.467| 109.879|Exclude  |
|245 |2022-10-06 21:19:00 | 106.333| 109.041|Exclude  |
|246 |2022-10-06 21:20:00 | 104.633| 110.625|Exclude  |
|247 |2022-10-06 21:21:00 | 105.917| 109.672|Exclude  |
|248 |2022-10-06 21:22:00 | 106.417| 110.634|Exclude  |
|249 |2022-10-06 21:23:00 | 109.367| 109.916|Exclude  |
|250 |2022-10-06 21:24:00 | 108.850| 107.974|Exclude  |
|251 |2022-10-06 21:25:00 | 110.150| 110.494|Exclude  |
|252 |2022-10-06 21:26:00 | 111.550| 108.984|Exclude  |
|253 |2022-10-06 21:27:00 | 109.667| 107.524|Exclude  |
|254 |2022-10-06 21:28:00 | 110.417| 107.654|Exclude  |
|255 |2022-10-06 21:29:00 | 111.050| 111.111|Exclude  |
|256 |2022-10-06 21:30:00 | 111.883| 106.987|Exclude  |
|257 |2022-10-06 21:31:00 | 111.750| 108.407|Exclude  |
|258 |2022-10-06 21:32:00 | 109.650| 110.731|Exclude  |
|259 |2022-10-06 21:33:00 | 113.333| 108.318|Exclude  |
|260 |2022-10-06 21:34:00 | 111.467| 109.370|Exclude  |
|261 |2022-10-06 21:35:00 | 113.067| 109.632|Exclude  |
|262 |2022-10-06 21:36:00 | 113.333| 110.382|Exclude  |
|263 |2022-10-06 21:37:00 | 114.167| 112.061|Exclude  |
|264 |2022-10-06 21:38:00 | 114.183| 108.273|Exclude  |
|265 |2022-10-06 21:39:00 | 116.600| 109.479|Exclude  |
|266 |2022-10-06 21:40:00 | 115.100| 107.678|Exclude  |
|267 |2022-10-06 21:41:00 | 115.317| 108.555|Exclude  |
|268 |2022-10-06 21:42:00 | 113.900| 110.613|Exclude  |
|269 |2022-10-06 21:43:00 | 117.150| 109.332|Exclude  |
|270 |2022-10-06 21:44:00 | 114.067| 109.341|Exclude  |
|271 |2022-10-06 21:45:00 | 116.817| 111.278|Exclude  |
|272 |2022-10-06 21:46:00 | 116.867| 110.331|Exclude  |
|273 |2022-10-06 21:47:00 | 117.600| 110.115|Exclude  |
|274 |2022-10-06 21:48:00 | 118.133| 109.703|Exclude  |
|275 |2022-10-06 21:49:00 | 118.500| 110.224|Exclude  |
|276 |2022-10-06 21:50:00 | 120.467| 110.471|Exclude  |
|277 |2022-10-06 21:51:00 | 122.617| 108.240|Exclude  |
|278 |2022-10-06 21:52:00 | 120.933| 106.230|Exclude  |
|279 |2022-10-06 21:53:00 | 122.083| 109.318|Exclude  |
|280 |2022-10-06 21:54:00 | 120.333| 109.301|Exclude  |
|281 |2022-10-06 21:55:00 | 120.850| 110.192|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   |2022-10-06 17:15:00 |  0.800|  67.848|Include  |
|2   |2022-10-06 17:16:00 |  1.583|  63.340|Include  |
|3   |2022-10-06 17:17:00 |  1.683|  80.353|Include  |
|4   |2022-10-06 17:18:00 |  2.517|  76.146|Include  |
|5   |2022-10-06 17:19:00 |  2.183|  94.815|Include  |
|6   |2022-10-06 17:20:00 |  2.950|  93.964|Include  |
|7   |2022-10-06 17:21:00 |  3.367| 101.333|Include  |
|8   |2022-10-06 17:22:00 |  4.450|  92.316|Include  |
|9   |2022-10-06 17:23:00 |  4.533|  91.116|Include  |
|10  |2022-10-06 17:24:00 |  5.017|  93.857|Include  |
|11  |2022-10-06 17:25:00 |  5.367|  93.093|Include  |
|12  |2022-10-06 17:26:00 |  6.067|  94.574|Include  |
|13  |2022-10-06 17:27:00 |  6.400| 107.899|Include  |
|14  |2022-10-06 17:28:00 |  6.700|  88.562|Include  |
|15  |2022-10-06 17:29:00 |  6.817| 103.750|Include  |
|16  |2022-10-06 17:30:00 |  7.817|  95.906|Include  |
|17  |2022-10-06 17:31:00 |  7.733|  97.027|Include  |
|18  |2022-10-06 17:32:00 |  8.367|  97.201|Include  |
|19  |2022-10-06 17:33:00 |  8.100| 105.189|Include  |
|20  |2022-10-06 17:34:00 |  9.550| 104.800|Include  |
|21  |2022-10-06 17:35:00 |  9.200| 111.798|Include  |
|22  |2022-10-06 17:36:00 | 10.100| 104.560|Include  |
|23  |2022-10-06 17:37:00 | 10.383| 107.169|Include  |
|24  |2022-10-06 17:38:00 | 10.733|  99.769|Include  |
|25  |2022-10-06 17:39:00 | 10.867| 106.134|Include  |
|26  |2022-10-06 17:40:00 | 12.867| 102.309|Include  |
|27  |2022-10-06 17:41:00 | 11.800| 100.452|Include  |
|28  |2022-10-06 17:42:00 | 12.283| 102.444|Include  |
|29  |2022-10-06 17:43:00 | 13.017| 107.970|Include  |
|30  |2022-10-06 17:44:00 | 14.000| 104.474|Include  |
|31  |2022-10-06 17:45:00 | 13.200| 105.570|Include  |
|32  |2022-10-06 17:46:00 | 14.383| 109.142|Include  |
|33  |2022-10-06 17:47:00 | 15.000| 107.583|Include  |
|34  |2022-10-06 17:48:00 | 15.533|  99.676|Include  |
|35  |2022-10-06 17:49:00 | 15.700| 104.924|Include  |
|36  |2022-10-06 17:50:00 | 16.233| 109.536|Include  |
|37  |2022-10-06 17:51:00 | 16.150| 102.128|Include  |
|38  |2022-10-06 17:52:00 | 17.567| 105.823|Include  |
|39  |2022-10-06 17:53:00 | 16.783| 107.044|Include  |
|40  |2022-10-06 17:54:00 | 17.750| 103.968|Include  |
|41  |2022-10-06 17:55:00 | 18.183| 108.760|Include  |
|42  |2022-10-06 17:56:00 | 18.700| 108.113|Include  |
|43  |2022-10-06 17:57:00 | 19.517| 108.279|Include  |
|44  |2022-10-06 17:58:00 | 19.817| 102.952|Include  |
|45  |2022-10-06 17:59:00 | 19.600| 109.283|Include  |
|46  |2022-10-06 18:00:00 | 19.867| 108.288|Include  |
|47  |2022-10-06 18:01:00 | 21.583| 106.509|Include  |
|48  |2022-10-06 18:02:00 | 21.550| 105.111|Include  |
|49  |2022-10-06 18:03:00 | 20.933| 106.854|Include  |
|50  |2022-10-06 18:04:00 | 21.817| 105.874|Include  |
|51  |2022-10-06 18:05:00 | 21.500| 106.201|Include  |
|52  |2022-10-06 18:06:00 | 22.883| 110.774|Include  |
|53  |2022-10-06 18:07:00 | 23.433| 112.948|Include  |
|54  |2022-10-06 18:08:00 | 24.250| 102.189|Include  |
|55  |2022-10-06 18:09:00 | 24.033| 108.187|Include  |
|56  |2022-10-06 18:10:00 | 25.567| 112.066|Include  |
|57  |2022-10-06 18:11:00 | 25.017| 105.000|Include  |
|58  |2022-10-06 18:12:00 | 25.650| 108.145|Include  |
|59  |2022-10-06 18:13:00 | 25.933| 104.846|Include  |
|60  |2022-10-06 18:14:00 | 26.533| 107.493|Include  |
|61  |2022-10-06 18:15:00 | 26.850| 106.837|Include  |
|62  |2022-10-06 18:16:00 | 27.183| 108.476|Include  |
|63  |2022-10-06 18:17:00 | 27.550| 110.807|Include  |
|64  |2022-10-06 18:18:00 | 28.600| 104.437|Include  |
|65  |2022-10-06 18:19:00 | 28.267| 110.172|Include  |
|66  |2022-10-06 18:20:00 | 29.183| 106.025|Include  |
|67  |2022-10-06 18:21:00 | 29.567| 107.445|Include  |
|68  |2022-10-06 18:22:00 | 30.883| 107.060|Include  |
|69  |2022-10-06 18:23:00 | 30.167| 106.690|Include  |
|70  |2022-10-06 18:24:00 | 31.433| 104.634|Include  |
|71  |2022-10-06 18:25:00 | 32.517| 108.011|Include  |
|72  |2022-10-06 18:26:00 | 31.567| 104.941|Include  |
|73  |2022-10-06 18:27:00 | 31.217| 105.405|Include  |
|74  |2022-10-06 18:28:00 | 32.883| 107.565|Include  |
|75  |2022-10-06 18:29:00 | 31.933| 109.093|Include  |
|76  |2022-10-06 18:30:00 | 33.483| 108.677|Include  |
|77  |2022-10-06 18:31:00 | 33.067| 109.338|Include  |
|78  |2022-10-06 18:32:00 | 35.250| 108.146|Include  |
|79  |2022-10-06 18:33:00 | 34.633| 110.400|Include  |
|80  |2022-10-06 18:34:00 | 34.833| 106.787|Include  |
|81  |2022-10-06 18:35:00 | 34.900| 105.281|Include  |
|82  |2022-10-06 18:36:00 | 35.517| 110.214|Include  |
|83  |2022-10-06 18:37:00 | 36.883| 110.688|Include  |
|84  |2022-10-06 18:38:00 | 37.183| 110.777|Include  |
|85  |2022-10-06 18:39:00 | 37.667| 108.559|Include  |
|86  |2022-10-06 18:40:00 | 37.650| 104.127|Include  |
|87  |2022-10-06 18:41:00 | 37.567| 107.726|Include  |
|88  |2022-10-06 18:42:00 | 38.883| 108.599|Include  |
|89  |2022-10-06 18:43:00 | 38.133| 108.027|Include  |
|90  |2022-10-06 18:44:00 | 39.333| 106.925|Include  |
|91  |2022-10-06 18:45:00 | 38.650| 110.777|Include  |
|92  |2022-10-06 18:46:00 | 40.433| 108.921|Include  |
|93  |2022-10-06 18:47:00 | 40.517| 108.902|Include  |
|94  |2022-10-06 18:48:00 | 39.600| 102.959|Include  |
|95  |2022-10-06 18:49:00 | 42.150| 114.011|Include  |
|96  |2022-10-06 18:50:00 | 42.500| 109.865|Include  |
|97  |2022-10-06 18:51:00 | 41.733| 109.886|Include  |
|98  |2022-10-06 18:52:00 | 41.933| 107.073|Include  |
|99  |2022-10-06 18:53:00 | 44.300| 109.507|Include  |
|100 |2022-10-06 18:54:00 | 45.533| 109.336|Include  |
|101 |2022-10-06 18:55:00 | 43.017| 108.902|Include  |
|102 |2022-10-06 18:56:00 | 44.300| 108.613|Include  |
|103 |2022-10-06 18:57:00 | 45.767| 108.359|Include  |
|104 |2022-10-06 18:58:00 | 46.167| 106.516|Include  |
|105 |2022-10-06 18:59:00 | 46.000| 107.977|Include  |
|106 |2022-10-06 19:00:00 | 47.733| 105.410|Include  |
|107 |2022-10-06 19:01:00 | 46.700| 109.141|Include  |
|108 |2022-10-06 19:02:00 | 47.883| 106.683|Include  |
|109 |2022-10-06 19:03:00 | 48.883| 105.559|Include  |
|110 |2022-10-06 19:04:00 | 47.667| 109.393|Include  |
|111 |2022-10-06 19:05:00 | 48.750| 106.928|Include  |
|112 |2022-10-06 19:06:00 | 50.000| 107.482|Include  |
|113 |2022-10-06 19:07:00 | 50.067| 104.197|Include  |
|114 |2022-10-06 19:08:00 | 49.650| 108.035|Include  |
|115 |2022-10-06 19:09:00 | 51.150| 108.813|Include  |
|116 |2022-10-06 19:10:00 | 49.333| 109.283|Include  |
|117 |2022-10-06 19:11:00 | 50.167| 108.962|Include  |
|118 |2022-10-06 19:12:00 | 52.433| 110.405|Include  |
|119 |2022-10-06 19:13:00 | 50.467| 107.941|Include  |
|120 |2022-10-06 19:14:00 | 52.850| 108.796|Include  |
|121 |2022-10-06 19:15:00 | 53.667| 109.761|Include  |
|122 |2022-10-06 19:16:00 | 53.550| 106.836|Include  |
|123 |2022-10-06 19:17:00 | 54.150| 112.161|Include  |
|124 |2022-10-06 19:18:00 | 53.000| 106.870|Include  |
|125 |2022-10-06 19:19:00 | 55.567| 109.526|Include  |
|126 |2022-10-06 19:20:00 | 55.267| 106.439|Include  |
|127 |2022-10-06 19:21:00 | 57.000| 105.989|Include  |
|128 |2022-10-06 19:22:00 | 55.217| 109.684|Include  |
|129 |2022-10-06 19:23:00 | 56.267| 110.564|Include  |
|130 |2022-10-06 19:24:00 | 56.583| 106.918|Include  |
|131 |2022-10-06 19:25:00 | 57.433| 107.156|Include  |
|132 |2022-10-06 19:26:00 | 56.950| 108.550|Include  |
|133 |2022-10-06 19:27:00 | 58.250| 109.527|Include  |
|134 |2022-10-06 19:28:00 | 59.533| 107.199|Include  |
|135 |2022-10-06 19:29:00 | 58.933| 110.418|Include  |
|136 |2022-10-06 19:30:00 | 58.317| 108.238|Include  |
|137 |2022-10-06 19:33:00 | 58.917| 109.049|Include  |
|138 |2022-10-06 19:34:00 | 59.567| 112.095|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 @ 120.00/s is the expected CPU usage at 120.00 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 @ 120.00/s|
|:--|:-------|:----------|:---------|:------------------------|:----|--------:|----------:|-------:|-------:|------------------:|
|1  |LOAD    |TermAppISO |CPU Usage |Percent single processor |Rate |   0.3368|     0.0041| 83.1516|       0|            40.4166|
|4  |MODEL   |TermAppISO |CPU Usage |Percent single processor |Rate |   0.6840|     0.0203| 33.6491|       0|            82.0743|
|7  |NETWORK |TermAppISO |CPU Usage |Percent single processor |Rate |   0.0477|     0.0098|  4.8936|       0|             5.7286|
  • 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|
|:--|:-------|:----------|:---------|:------------------------|:----|--------:|----------:|--------:|-------:|
|5  |NETWORK |TermAppISO |CPU Usage |Percent single processor |Bias | 101.2633|     0.3426| 295.5679|       0|
  • 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 @ 120.00/s is the expected Network usage at 120.00 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 @ 120.00/s|
|:--|:-------|:----------|:------------------|:---------------|:----|---------:|----------:|--------:|-------:|------------------:|
|2  |LOAD    |TermAppISO |Network Usage recv |bits per second |Rate | 25160.897|   150.8127| 166.8354|  0.0000|          3019307.6|
|3  |LOAD    |TermAppISO |Network Usage sent |bits per second |Rate | 27837.378|   135.1475| 205.9777|  0.0000|          3340485.4|
|5  |MODEL   |TermAppISO |Network Usage recv |bits per second |Rate | 26665.464|   176.5792| 151.0114|  0.0000|          3199855.6|
|6  |MODEL   |TermAppISO |Network Usage sent |bits per second |Rate | 26021.141|   420.1808|  61.9284|  0.0000|          3122537.0|
|8  |NETWORK |TermAppISO |Network Usage recv |bits per second |Rate |  2850.785|   917.4990|   3.1071|  0.0023|           342094.1|
  • 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 |Network Usage recv |bits per second |Bias |  162425.5|   5296.490| 30.6666|  0.0000|
|2  |LOAD    |TermAppISO |Network Usage sent |bits per second |Bias |  380402.0|   4746.333| 80.1465|  0.0000|
|3  |MODEL   |TermAppISO |Network Usage recv |bits per second |Bias |  171207.6|   6201.399| 27.6079|  0.0000|
|4  |MODEL   |TermAppISO |Network Usage sent |bits per second |Bias |  302797.1|  14756.602| 20.5194|  0.0000|
|6  |NETWORK |TermAppISO |Network Usage recv |bits per second |Bias |  708685.9|  32222.241| 21.9937|  0.0000|
|7  |NETWORK |TermAppISO |Network Usage sent |bits per second |Bias | 1572541.6| 901747.632|  1.7439|  0.0834|

Change Control

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           |LOAD0_SNMP     |176.67.166.86  |LinuxSNMP |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |
|3  |LOAD           |LOAD1          |176.67.166.89  |Linux     |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |
|4  |LOAD           |LOAD1_SNMP     |176.67.166.89  |LinuxSNMP |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |
|5  |MODEL          |MODELAPP0      |176.67.166.20  |Linux     |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |
|6  |MODEL          |MODELAPP0_SNMP |176.67.166.20  |LinuxSNMP |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |
|7  |MODEL          |MODELAPP1      |176.67.166.72  |Linux     |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |
|8  |MODEL          |MODELAPP1_SNMP |176.67.166.72  |LinuxSNMP |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |
|9  |NETWORK        |NETWORK        |109.123.111.17 |Linux     |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |
|10 |NETWORK        |NETWORK_SNMP   |109.123.111.17 |LinuxSNMP |CML EcoSystem |Patrick Hayward  |Patrick Hayward |Patrick Hayward     |CMLEcoSystem_Server_Classification.csv |

Analysis

Test: TermAppISO

  • This test started at 2022-10-06 17:13:01 and ended at 2022-10-06 21:55:02.

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 
-1.87447 -0.57489 -0.07292  0.56203  2.90338 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.31139    0.14225  -2.189   0.0303 *  
Rate         0.33681    0.00405  83.152   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.821 on 136 degrees of freedom
Multiple R-squared:  0.9807,	Adjusted R-squared:  0.9806 
F-statistic:  6914 on 1 and 136 DF,  p-value: < 2.2e-16

20221006 171301 Linux CPU cpucpusystemuser USAGE BY LOAD CPUUsagePercentsingleprocessor LOAD TermAppISONFT 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 
-67454 -25256  -3488  22829  70540 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 162425.5     5296.5   30.67   <2e-16 ***
Rate         25160.9      150.8  166.84   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 30570 on 136 degrees of freedom
Multiple R-squared:  0.9951,	Adjusted R-squared:  0.9951 
F-statistic: 2.783e+04 on 1 and 136 DF,  p-value: < 2.2e-16

20221006 171301 Linux Network recv USAGE BY LOAD NetworkUsagerecvbitspersecond LOAD TermAppISONFT 1.png

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

Residuals:
   Min     1Q Median     3Q    Max 
-89109 -16099   2494  15781  65068 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 380402.0     4746.3   80.15   <2e-16 ***
Rate         27837.4      135.1  205.98   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 27390 on 136 degrees of freedom
Multiple R-squared:  0.9968,	Adjusted R-squared:  0.9968 
F-statistic: 4.243e+04 on 1 and 136 DF,  p-value: < 2.2e-16

20221006 171301 Linux Network sent USAGE BY LOAD NetworkUsagesentbitspersecond LOAD TermAppISONFT 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 
-7.5662 -3.4392 -0.2075  2.3761 12.1246 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.57294    0.71384   0.803    0.424    
Rate         0.68395    0.02033  33.649   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.12 on 136 degrees of freedom
Multiple R-squared:  0.8928,	Adjusted R-squared:  0.892 
F-statistic:  1132 on 1 and 136 DF,  p-value: < 2.2e-16

20221006 171301 Linux CPU cpucpusystemuser USAGE BY LOAD CPUUsagePercentsingleprocessor MODEL TermAppISONFT 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 
-75937 -26226  -3537  22758 102701 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 171207.6     6201.4   27.61   <2e-16 ***
Rate         26665.5      176.6  151.01   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 35790 on 136 degrees of freedom
Multiple R-squared:  0.9941,	Adjusted R-squared:  0.994 
F-statistic: 2.28e+04 on 1 and 136 DF,  p-value: < 2.2e-16

20221006 171301 Linux Network recv USAGE BY LOAD NetworkUsagerecvbitspersecond MODEL TermAppISONFT 1.png

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

Residuals:
    Min      1Q  Median      3Q     Max 
-188419  -52002   -2119   63090  179309 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 302797.1    14756.6   20.52   <2e-16 ***
Rate         26021.1      420.2   61.93   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 85170 on 136 degrees of freedom
Multiple R-squared:  0.9658,	Adjusted R-squared:  0.9655 
F-statistic:  3835 on 1 and 136 DF,  p-value: < 2.2e-16

20221006 171301 Linux Network sent USAGE BY LOAD NetworkUsagesentbitspersecond MODEL TermAppISONFT 1.png

Server class NETWORK CPU usage

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

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7718 -1.1906 -0.0868  0.6541 12.3675 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.013e+02  3.426e-01 295.568  < 2e-16 ***
Rate        4.774e-02  9.755e-03   4.894 2.76e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.977 on 136 degrees of freedom
Multiple R-squared:  0.1497,	Adjusted R-squared:  0.1435 
F-statistic: 23.95 on 1 and 136 DF,  p-value: 2.759e-06

20221006 171301 Linux CPU cpucpusystemuser USAGE BY LOAD CPUUsagePercentsingleprocessor NETWORK TermAppISONFT 1.png

Server class NETWORK Network usage

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

Residuals:
    Min      1Q  Median      3Q     Max 
-171443  -94028  -39165   56403 1409467 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 708685.9    32222.2  21.994   <2e-16 ***
Rate          2850.8      917.5   3.107   0.0023 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 186000 on 136 degrees of freedom
Multiple R-squared:  0.06628,	Adjusted R-squared:  0.05942 
F-statistic: 9.654 on 1 and 136 DF,  p-value: 0.0023

20221006 171301 Linux Network recv USAGE BY LOAD NetworkUsagerecvbitspersecond NETWORK TermAppISONFT 1.png

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

Residuals:
     Min       1Q   Median       3Q      Max 
-1512668 -1131154  -780864  -434123 40543880 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  1572542     901748   1.744   0.0834 .
Rate          -21436      25676  -0.835   0.4053  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5205000 on 136 degrees of freedom
Multiple R-squared:  0.005099,	Adjusted R-squared:  -0.002217 
F-statistic: 0.697 on 1 and 136 DF,  p-value: 0.4053

20221006 171301 Linux Network sent USAGE BY LOAD NetworkUsagesentbitspersecond NETWORK TermAppISONFT 1.png

Performance

  • For test TermAppISO/TermAppISONFT (Test 1, from 2022-10-06 17:13:01 to 2022-10-06 21:55:02), 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 60.00 arrivals per second (this rate is as opposed to the projected/future peak rate). This cut-off time occurred at 2022-10-06 19:30:00. 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 AUTHORISATION_RESPONSE_1110_OK:

20221006 171301 RESP SUMM 01 TermAppISONFT AUTHORISATION RESPONSE 1110 OK.png

|   |Basename                   |Outcome                        |  Count| Percent|  Resp| StdDev|
|:--|:--------------------------|:------------------------------|------:|-------:|-----:|------:|
|1  |authorisation_request_1100 |AUTHORISATION_RESPONSE_1110_OK | 246152|     100| 0.205|  0.013|
  • Distribution of response times where rate does not exceed the target cutoff target rate for outcome TRANSACTION_ADVICE_RESPONSE_1230_OK:

20221006 171301 RESP SUMM 01 TermAppISONFT TRANSACTION ADVICE RESPONSE 1230 OK.png

|   |Basename                         |Outcome                             |  Count| Percent|  Resp| StdDev|
|:--|:--------------------------------|:-----------------------------------|------:|-------:|-----:|------:|
|2  |transaction_advice_response_1230 |TRANSACTION_ADVICE_RESPONSE_1230_OK | 245921|     100| 0.104|  0.008|

Operation authorisation_request_1100

20221006 171301 Function authorisation request 1100 RESP.png

20221006 171301 Function authorisation request 1100 RATE.png

Operation transaction_advice_response_1230

20221006 171301 Function transaction advice response 1230 RESP.png

20221006 171301 Function transaction advice response 1230 RATE.png

Resource Usage

Linux Server CPU Usage

TermAppISO

Server LOAD0

20221006 171301 CMLXLINP CPU USAGE 01 TermAppISONFT LOAD0.png

Server LOAD1

20221006 171301 CMLXLINP CPU USAGE 01 TermAppISONFT LOAD1.png

Server MODELAPP0

20221006 171301 CMLXLINP CPU USAGE 01 TermAppISONFT MODELAPP0.png

Server MODELAPP1

20221006 171301 CMLXLINP CPU USAGE 01 TermAppISONFT MODELAPP1.png

Server NETWORK

20221006 171301 CMLXLINP CPU USAGE 01 TermAppISONFT NETWORK.png

Linux Server Disk Activity

Server LOAD0

20221006 171301 CMLXLINP DISK METRIC LOAD0 reads.png

20221006 171301 CMLXLINP DISK METRIC LOAD0 reads merged.png

20221006 171301 CMLXLINP DISK METRIC LOAD0 read sectors.png

20221006 171301 CMLXLINP DISK METRIC LOAD0 read milli.png

20221006 171301 CMLXLINP DISK METRIC LOAD0 writes.png

20221006 171301 CMLXLINP DISK METRIC LOAD0 writes merged.png

20221006 171301 CMLXLINP DISK METRIC LOAD0 write sectors.png

20221006 171301 CMLXLINP DISK METRIC LOAD0 write milli.png

20221006 171301 CMLXLINP DISK METRIC LOAD0 io inprogress.png

20221006 171301 CMLXLINP DISK METRIC LOAD0 io milli.png

20221006 171301 CMLXLINP DISK METRIC LOAD0 io weighted milli.png

Server LOAD1

20221006 171301 CMLXLINP DISK METRIC LOAD1 reads.png

20221006 171301 CMLXLINP DISK METRIC LOAD1 reads merged.png

20221006 171301 CMLXLINP DISK METRIC LOAD1 read sectors.png

20221006 171301 CMLXLINP DISK METRIC LOAD1 read milli.png

20221006 171301 CMLXLINP DISK METRIC LOAD1 writes.png

20221006 171301 CMLXLINP DISK METRIC LOAD1 writes merged.png

20221006 171301 CMLXLINP DISK METRIC LOAD1 write sectors.png

20221006 171301 CMLXLINP DISK METRIC LOAD1 write milli.png

20221006 171301 CMLXLINP DISK METRIC LOAD1 io inprogress.png

20221006 171301 CMLXLINP DISK METRIC LOAD1 io milli.png

20221006 171301 CMLXLINP DISK METRIC LOAD1 io weighted milli.png

Server MODELAPP0

20221006 171301 CMLXLINP DISK METRIC MODELAPP0 reads.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP0 reads merged.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP0 read sectors.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP0 read milli.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP0 writes.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP0 writes merged.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP0 write sectors.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP0 write milli.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP0 io inprogress.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP0 io milli.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP0 io weighted milli.png

Server NETWORK

20221006 171301 CMLXLINP DISK METRIC NETWORK reads.png

20221006 171301 CMLXLINP DISK METRIC NETWORK reads merged.png

20221006 171301 CMLXLINP DISK METRIC NETWORK read sectors.png

20221006 171301 CMLXLINP DISK METRIC NETWORK read milli.png

20221006 171301 CMLXLINP DISK METRIC NETWORK writes.png

20221006 171301 CMLXLINP DISK METRIC NETWORK writes merged.png

20221006 171301 CMLXLINP DISK METRIC NETWORK write sectors.png

20221006 171301 CMLXLINP DISK METRIC NETWORK write milli.png

20221006 171301 CMLXLINP DISK METRIC NETWORK io milli.png

20221006 171301 CMLXLINP DISK METRIC NETWORK io weighted milli.png

20221006 171301 CMLXLINP DISK METRIC NETWORK io inprogress.png

Server MODELAPP1

20221006 171301 CMLXLINP DISK METRIC MODELAPP1 reads.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP1 reads merged.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP1 read sectors.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP1 read milli.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP1 writes.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP1 writes merged.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP1 write sectors.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP1 write milli.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP1 io inprogress.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP1 io milli.png

20221006 171301 CMLXLINP DISK METRIC MODELAPP1 io weighted milli.png

Linux Server Memory Usage

Server LOAD0

20221006 171301 CMLXLINP MEMMEM METRIC LOAD0.png

Server LOAD1

20221006 171301 CMLXLINP MEMMEM METRIC LOAD1.png

Server MODELAPP0

20221006 171301 CMLXLINP MEMMEM METRIC MODELAPP0.png

Server NETWORK

20221006 171301 CMLXLINP MEMMEM METRIC NETWORK.png

Server MODELAPP1

20221006 171301 CMLXLINP MEMMEM METRIC MODELAPP1.png

Linux Server Swap Usage

Server LOAD0

20221006 171301 CMLXLINP MEMSWAP METRIC LOAD0.png

Server LOAD1

20221006 171301 CMLXLINP MEMSWAP METRIC LOAD1.png

Server MODELAPP0

20221006 171301 CMLXLINP MEMSWAP METRIC MODELAPP0.png

Server NETWORK

20221006 171301 CMLXLINP MEMSWAP METRIC NETWORK.png

Server MODELAPP1

20221006 171301 CMLXLINP MEMSWAP METRIC MODELAPP1.png

Linux Server Network Usage

Server LOAD0

20221006 171301 CMLXLINP NET METRIC LOAD0 ibytes.png

20221006 171301 CMLXLINP NET METRIC LOAD0 ipackets.png

20221006 171301 CMLXLINP NET METRIC LOAD0 obytes.png

20221006 171301 CMLXLINP NET METRIC LOAD0 opackets.png

Server LOAD1

20221006 171301 CMLXLINP NET METRIC LOAD1 ibytes.png

20221006 171301 CMLXLINP NET METRIC LOAD1 ipackets.png

20221006 171301 CMLXLINP NET METRIC LOAD1 obytes.png

20221006 171301 CMLXLINP NET METRIC LOAD1 opackets.png

Server MODELAPP0

20221006 171301 CMLXLINP NET METRIC MODELAPP0 ibytes.png

20221006 171301 CMLXLINP NET METRIC MODELAPP0 ipackets.png

20221006 171301 CMLXLINP NET METRIC MODELAPP0 obytes.png

20221006 171301 CMLXLINP NET METRIC MODELAPP0 opackets.png

Server NETWORK

20221006 171301 CMLXLINP NET METRIC NETWORK ibytes.png

20221006 171301 CMLXLINP NET METRIC NETWORK ipackets.png

20221006 171301 CMLXLINP NET METRIC NETWORK obytes.png

20221006 171301 CMLXLINP NET METRIC NETWORK opackets.png

Server MODELAPP1

20221006 171301 CMLXLINP NET METRIC MODELAPP1 ibytes.png

20221006 171301 CMLXLINP NET METRIC MODELAPP1 ipackets.png

20221006 171301 CMLXLINP NET METRIC MODELAPP1 obytes.png

20221006 171301 CMLXLINP NET METRIC MODELAPP1 opackets.png

Wintel Server Resource Usage

Test TermAppISO

Wintel Server Resource Usage

Test TermAppISO

pSeries AIX Server Reource Usage

Test TermAppISO

* Code Magus Limited