EcoSystem TermAppISO - 2022/08/23 NFT
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Contents
- 1 Contributors
- 2 Summary
- 3 Change Control
- 4 Feedback
- 5 Record
- 6 Landscape
- 7 Analysis
- 7.1 Test: TermAppISO
- 7.1.1 Resource usage for platform Appliance
- 7.1.2 Resource usage for platform Linux
- 7.1 Test: TermAppISO
- 8 Performance
- 9 Resource Usage
- 9.1 Linux Server CPU Usage
- 9.2 Linux Server Disk Activity
- 9.3 Linux Server Memory Usage
- 9.4 Linux Server Swap Usage
- 9.5 Linux Server Network Usage
- 9.6 Linux Server Usage from SNMP MIB Data
- 9.7 Wintel Server Resource Usage
- 9.8 Wintel Server Resource Usage
- 9.9 pSeries AIX Server Reource Usage
Contributors
Hayward (talk) 21:49, 23 August 2022 (UTC) mailto:email@codemagus.com
Summary
- The following test(s) were included in this test session.
|LABEL |Description |Start |End | |:-------------|:-----------|:-------------------|:-------------------| |TermAppISONFT |TermAppISO |2022-08-23 12:04:01 |2022-08-23 17:26:00 |
- 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 |2022-08-23 12:06:00 | 0.833| 59.139|Include | |2 |2022-08-23 12:07:00 | 1.633| 68.218|Include | |3 |2022-08-23 12:08:00 | 1.450| 76.975|Include | |4 |2022-08-23 12:09:00 | 2.317| 81.463|Include | |5 |2022-08-23 12:10:00 | 2.817| 88.757|Include | |6 |2022-08-23 12:11:00 | 3.700| 77.692|Include | |7 |2022-08-23 12:12:00 | 3.417| 92.533|Include | |8 |2022-08-23 12:13:00 | 3.950| 88.875|Include | |9 |2022-08-23 12:14:00 | 4.367| 95.646|Include | |10 |2022-08-23 12:15:00 | 5.183| 94.341|Include | |11 |2022-08-23 12:16:00 | 4.883| 95.267|Include | |12 |2022-08-23 12:17:00 | 6.200| 99.091|Include | |13 |2022-08-23 12:18:00 | 5.733| 103.586|Include | |14 |2022-08-23 12:19:00 | 6.417| 95.447|Include | |15 |2022-08-23 12:20:00 | 7.217| 104.920|Include | |16 |2022-08-23 12:21:00 | 7.633| 102.191|Include | |17 |2022-08-23 12:22:00 | 7.783| 100.362|Include | |18 |2022-08-23 12:23:00 | 8.033| 99.164|Include | |19 |2022-08-23 12:24:00 | 8.233| 105.389|Include | |20 |2022-08-23 12:25:00 | 9.667| 103.203|Include | |21 |2022-08-23 12:26:00 | 9.250| 104.310|Include | |22 |2022-08-23 12:27:00 | 10.700| 102.215|Include | |23 |2022-08-23 12:28:00 | 11.217| 97.532|Include | |24 |2022-08-23 12:29:00 | 12.033| 97.052|Include | |25 |2022-08-23 12:30:00 | 10.867| 97.760|Include | |26 |2022-08-23 12:31:00 | 11.367| 104.163|Include | |27 |2022-08-23 12:32:00 | 12.700| 106.219|Include | |28 |2022-08-23 12:33:00 | 12.867| 101.907|Include | |29 |2022-08-23 12:34:00 | 14.000| 104.777|Include | |30 |2022-08-23 12:35:00 | 13.817| 106.018|Include | |31 |2022-08-23 12:36:00 | 13.300| 104.315|Include | |32 |2022-08-23 12:37:00 | 14.067| 101.657|Include | |33 |2022-08-23 12:38:00 | 14.817| 105.824|Include | |34 |2022-08-23 12:39:00 | 15.933| 105.192|Exclude | |35 |2022-08-23 12:40:00 | 15.783| 98.830|Exclude | |36 |2022-08-23 12:41:00 | 16.517| 102.491|Exclude | |37 |2022-08-23 12:42:00 | 16.983| 101.186|Exclude | |38 |2022-08-23 12:43:00 | 17.533| 106.616|Exclude | |39 |2022-08-23 12:44:00 | 16.183| 106.289|Exclude | |40 |2022-08-23 12:45:00 | 17.900| 107.376|Exclude | |41 |2022-08-23 12:46:00 | 17.800| 105.705|Exclude | |42 |2022-08-23 12:47:00 | 18.383| 105.431|Exclude | |43 |2022-08-23 12:48:00 | 19.250| 111.754|Exclude | |44 |2022-08-23 12:49:00 | 20.550| 104.860|Exclude | |45 |2022-08-23 12:50:00 | 19.333| 107.153|Exclude | |46 |2022-08-23 12:51:00 | 21.117| 107.054|Exclude | |47 |2022-08-23 12:52:00 | 20.017| 103.685|Exclude | |48 |2022-08-23 12:53:00 | 21.750| 107.821|Exclude | |49 |2022-08-23 12:54:00 | 21.450| 107.295|Exclude | |50 |2022-08-23 12:55:00 | 21.700| 110.412|Exclude | |51 |2022-08-23 12:56:00 | 22.517| 111.286|Exclude | |52 |2022-08-23 12:57:00 | 23.200| 102.970|Exclude | |53 |2022-08-23 12:58:00 | 23.450| 108.105|Exclude | |54 |2022-08-23 12:59:00 | 24.083| 103.395|Exclude | |55 |2022-08-23 13:00:00 | 22.883| 104.874|Exclude | |56 |2022-08-23 13:01:00 | 25.017| 110.849|Exclude | |57 |2022-08-23 13:02:00 | 24.650| 112.240|Exclude | |58 |2022-08-23 13:03:00 | 25.950| 106.213|Exclude | |59 |2022-08-23 13:04:00 | 25.200| 112.457|Exclude | |60 |2022-08-23 13:05:00 | 27.033| 107.902|Exclude | |61 |2022-08-23 13:06:00 | 26.117| 109.406|Exclude | |62 |2022-08-23 13:07:00 | 27.567| 106.475|Exclude | |63 |2022-08-23 13:08:00 | 27.717| 105.948|Exclude | |64 |2022-08-23 13:09:00 | 28.767| 107.931|Exclude | |65 |2022-08-23 13:10:00 | 29.000| 105.155|Exclude | |66 |2022-08-23 13:11:00 | 29.150| 108.902|Exclude | |67 |2022-08-23 13:12:00 | 30.017| 107.783|Exclude | |68 |2022-08-23 13:13:00 | 29.717| 106.044|Exclude | |69 |2022-08-23 13:14:00 | 30.100| 105.925|Exclude | |70 |2022-08-23 13:15:00 | 30.717| 108.602|Exclude | |71 |2022-08-23 13:16:00 | 30.567| 107.146|Exclude | |72 |2022-08-23 13:17:00 | 32.650| 109.586|Exclude | |73 |2022-08-23 13:18:00 | 32.633| 108.766|Exclude | |74 |2022-08-23 13:19:00 | 33.250| 109.051|Exclude | |75 |2022-08-23 13:20:00 | 32.483| 101.215|Exclude | |76 |2022-08-23 13:21:00 | 32.933| 105.153|Exclude | |77 |2022-08-23 13:22:00 | 33.017| 109.039|Exclude | |78 |2022-08-23 13:23:00 | 35.033| 110.184|Exclude | |79 |2022-08-23 13:24:00 | 34.717| 105.959|Exclude | |80 |2022-08-23 13:25:00 | 35.167| 109.542|Exclude | |81 |2022-08-23 13:26:00 | 34.967| 109.639|Exclude | |82 |2022-08-23 13:27:00 | 35.600| 108.035|Exclude | |83 |2022-08-23 13:28:00 | 35.400| 110.608|Exclude | |84 |2022-08-23 13:29:00 | 36.367| 109.920|Exclude | |85 |2022-08-23 13:30:00 | 37.267| 108.123|Exclude | |86 |2022-08-23 13:31:00 | 37.867| 110.013|Exclude | |87 |2022-08-23 13:32:00 | 37.167| 107.174|Exclude | |88 |2022-08-23 13:33:00 | 38.750| 105.863|Exclude | |89 |2022-08-23 13:34:00 | 38.700| 111.195|Exclude | |90 |2022-08-23 13:35:00 | 39.183| 107.993|Exclude | |91 |2022-08-23 13:36:00 | 39.267| 110.128|Exclude | |92 |2022-08-23 13:37:00 | 40.783| 107.436|Exclude | |93 |2022-08-23 13:38:00 | 41.033| 110.260|Exclude | |94 |2022-08-23 13:39:00 | 40.717| 107.814|Exclude | |95 |2022-08-23 13:40:00 | 40.833| 112.346|Exclude | |96 |2022-08-23 13:41:00 | 42.450| 105.982|Exclude | |97 |2022-08-23 13:42:00 | 41.900| 108.880|Exclude | |98 |2022-08-23 13:43:00 | 42.750| 106.224|Exclude | |99 |2022-08-23 13:44:00 | 43.667| 111.267|Exclude | |100 |2022-08-23 13:45:00 | 44.933| 105.064|Exclude | |101 |2022-08-23 13:46:00 | 43.700| 106.727|Exclude | |102 |2022-08-23 13:47:00 | 45.450| 109.257|Exclude | |103 |2022-08-23 13:48:00 | 45.500| 109.688|Exclude | |104 |2022-08-23 13:49:00 | 46.367| 106.487|Exclude | |105 |2022-08-23 13:50:00 | 45.817| 110.304|Exclude | |106 |2022-08-23 13:51:00 | 46.233| 106.256|Exclude | |107 |2022-08-23 13:52:00 | 46.117| 107.085|Exclude | |108 |2022-08-23 13:53:00 | 47.033| 108.830|Exclude | |109 |2022-08-23 13:54:00 | 48.150| 110.335|Exclude | |110 |2022-08-23 13:55:00 | 48.767| 108.784|Exclude | |111 |2022-08-23 13:56:00 | 47.733| 108.251|Exclude | |112 |2022-08-23 13:57:00 | 48.083| 105.984|Exclude | |113 |2022-08-23 13:58:00 | 49.367| 110.372|Exclude | |114 |2022-08-23 13:59:00 | 49.517| 107.514|Exclude | |115 |2022-08-23 14:00:00 | 49.517| 107.490|Exclude | |116 |2022-08-23 14:01:00 | 50.650| 110.143|Exclude | |117 |2022-08-23 14:02:00 | 49.367| 110.219|Exclude | |118 |2022-08-23 14:03:00 | 52.800| 108.218|Exclude | |119 |2022-08-23 14:04:00 | 50.867| 110.906|Exclude | |120 |2022-08-23 14:05:00 | 51.150| 110.696|Exclude | |121 |2022-08-23 14:06:00 | 53.650| 108.903|Exclude | |122 |2022-08-23 14:07:00 | 51.950| 109.011|Exclude | |123 |2022-08-23 14:08:00 | 53.633| 108.081|Exclude | |124 |2022-08-23 14:09:00 | 55.383| 109.733|Exclude | |125 |2022-08-23 14:10:00 | 53.950| 110.312|Exclude | |126 |2022-08-23 14:11:00 | 55.133| 108.852|Exclude | |127 |2022-08-23 14:12:00 | 56.483| 107.284|Exclude | |128 |2022-08-23 14:13:00 | 56.733| 107.309|Exclude | |129 |2022-08-23 14:14:00 | 57.233| 108.524|Exclude | |130 |2022-08-23 14:15:00 | 56.400| 107.246|Exclude | |131 |2022-08-23 14:16:00 | 57.117| 109.610|Exclude | |132 |2022-08-23 14:17:00 | 57.317| 106.671|Exclude | |133 |2022-08-23 14:18:00 | 57.267| 110.677|Exclude | |134 |2022-08-23 14:19:00 | 58.217| 107.954|Exclude | |135 |2022-08-23 14:20:00 | 60.250| 108.076|Exclude | |136 |2022-08-23 14:21:00 | 59.000| 109.935|Exclude | |137 |2022-08-23 14:22:00 | 59.950| 108.744|Exclude | |138 |2022-08-23 14:23:00 | 61.667| 108.359|Exclude | |139 |2022-08-23 14:24:00 | 60.467| 108.371|Exclude | |140 |2022-08-23 14:25:00 | 62.583| 105.952|Exclude | |141 |2022-08-23 14:26:00 | 60.033| 107.410|Exclude | |142 |2022-08-23 14:27:00 | 62.117| 108.782|Exclude | |143 |2022-08-23 14:28:00 | 60.667| 109.515|Exclude | |144 |2022-08-23 14:29:00 | 62.750| 112.420|Exclude | |145 |2022-08-23 14:30:00 | 61.583| 108.140|Exclude | |146 |2022-08-23 14:31:00 | 62.450| 111.590|Exclude | |147 |2022-08-23 14:32:00 | 63.650| 109.760|Exclude | |148 |2022-08-23 14:33:00 | 63.083| 110.330|Exclude | |149 |2022-08-23 14:34:00 | 65.600| 111.415|Exclude | |150 |2022-08-23 14:35:00 | 65.617| 107.759|Exclude | |151 |2022-08-23 14:36:00 | 66.250| 108.459|Exclude | |152 |2022-08-23 14:37:00 | 66.550| 107.299|Exclude | |153 |2022-08-23 14:38:00 | 64.717| 110.525|Exclude | |154 |2022-08-23 14:39:00 | 66.667| 107.370|Exclude | |155 |2022-08-23 14:40:00 | 67.417| 108.226|Exclude | |156 |2022-08-23 14:41:00 | 66.817| 107.359|Exclude | |157 |2022-08-23 14:42:00 | 66.317| 109.379|Exclude | |158 |2022-08-23 14:43:00 | 68.233| 109.202|Exclude | |159 |2022-08-23 14:44:00 | 66.400| 111.016|Exclude | |160 |2022-08-23 14:45:00 | 66.667| 109.052|Exclude | |161 |2022-08-23 14:46:00 | 67.650| 108.398|Exclude | |162 |2022-08-23 14:47:00 | 67.617| 109.783|Exclude | |163 |2022-08-23 14:48:00 | 69.850| 115.633|Exclude | |164 |2022-08-23 14:49:00 | 68.617| 109.109|Exclude | |165 |2022-08-23 14:50:00 | 67.667| 106.999|Exclude | |166 |2022-08-23 14:51:00 | 69.617| 111.110|Exclude | |167 |2022-08-23 14:52:00 | 69.883| 110.994|Exclude | |168 |2022-08-23 14:53:00 | 70.650| 109.342|Exclude | |169 |2022-08-23 14:54:00 | 70.667| 107.765|Exclude | |170 |2022-08-23 14:55:00 | 71.067| 108.518|Exclude | |171 |2022-08-23 14:56:00 | 70.600| 108.434|Exclude | |172 |2022-08-23 14:57:00 | 71.583| 108.159|Exclude | |173 |2022-08-23 14:58:00 | 69.133| 107.901|Exclude | |174 |2022-08-23 14:59:00 | 70.850| 108.053|Exclude | |175 |2022-08-23 15:00:00 | 71.233| 109.560|Exclude | |176 |2022-08-23 15:01:00 | 71.117| 107.956|Exclude | |177 |2022-08-23 15:02:00 | 72.217| 110.621|Exclude | |178 |2022-08-23 15:03:00 | 72.317| 108.639|Exclude | |179 |2022-08-23 15:04:00 | 71.633| 111.339|Exclude | |180 |2022-08-23 15:05:00 | 72.367| 107.513|Exclude | |181 |2022-08-23 15:06:00 | 74.433| 111.263|Exclude | |182 |2022-08-23 15:07:00 | 71.183| 110.123|Exclude | |183 |2022-08-23 15:08:00 | 74.117| 107.161|Exclude | |184 |2022-08-23 15:09:00 | 73.350| 110.559|Exclude | |185 |2022-08-23 15:10:00 | 72.300| 110.462|Exclude | |186 |2022-08-23 15:11:00 | 75.433| 109.795|Exclude | |187 |2022-08-23 15:12:00 | 71.800| 108.956|Exclude | |188 |2022-08-23 15:13:00 | 75.567| 110.568|Exclude | |189 |2022-08-23 15:14:00 | 74.333| 111.376|Exclude | |190 |2022-08-23 15:15:00 | 75.250| 109.682|Exclude | |191 |2022-08-23 15:16:00 | 73.767| 109.660|Exclude | |192 |2022-08-23 15:17:00 | 76.633| 108.970|Exclude | |193 |2022-08-23 15:18:00 | 81.117| 102.985|Exclude | |194 |2022-08-23 15:19:00 | 81.483| 107.257|Exclude | |195 |2022-08-23 15:20:00 | 80.300| 107.585|Exclude | |196 |2022-08-23 15:21:00 | 81.433| 108.085|Exclude | |197 |2022-08-23 15:22:00 | 81.967| 108.492|Exclude | |198 |2022-08-23 15:23:00 | 82.400| 109.366|Exclude | |199 |2022-08-23 15:24:00 | 80.067| 110.298|Exclude | |200 |2022-08-23 15:25:00 | 82.750| 108.376|Exclude | |201 |2022-08-23 15:26:00 | 82.483| 112.124|Exclude | |202 |2022-08-23 15:27:00 | 83.783| 107.135|Exclude | |203 |2022-08-23 15:28:00 | 84.500| 106.494|Exclude | |204 |2022-08-23 15:29:00 | 83.817| 110.918|Exclude | |205 |2022-08-23 15:30:00 | 85.567| 108.658|Exclude | |206 |2022-08-23 15:31:00 | 86.850| 111.549|Exclude | |207 |2022-08-23 15:32:00 | 87.500| 107.659|Exclude | |208 |2022-08-23 15:33:00 | 87.467| 110.889|Exclude | |209 |2022-08-23 15:34:00 | 86.533| 108.014|Exclude | |210 |2022-08-23 15:35:00 | 89.900| 107.151|Exclude | |211 |2022-08-23 15:36:00 | 88.500| 108.082|Exclude | |212 |2022-08-23 15:37:00 | 86.867| 109.580|Exclude | |213 |2022-08-23 15:38:00 | 91.517| 105.898|Exclude | |214 |2022-08-23 15:39:00 | 89.917| 111.079|Exclude | |215 |2022-08-23 15:40:00 | 89.183| 112.351|Exclude | |216 |2022-08-23 15:41:00 | 92.500| 106.539|Exclude | |217 |2022-08-23 15:42:00 | 88.833| 108.466|Exclude | |218 |2022-08-23 15:43:00 | 90.733| 111.423|Exclude | |219 |2022-08-23 15:44:00 | 91.317| 111.869|Exclude | |220 |2022-08-23 15:45:00 | 93.000| 109.101|Exclude | |221 |2022-08-23 15:46:00 | 91.683| 110.073|Exclude | |222 |2022-08-23 15:47:00 | 94.333| 110.722|Exclude | |223 |2022-08-23 15:48:00 | 97.100| 108.680|Exclude | |224 |2022-08-23 15:49:00 | 97.667| 108.446|Exclude | |225 |2022-08-23 15:50:00 | 95.733| 111.232|Exclude | |226 |2022-08-23 15:51:00 | 96.017| 107.474|Exclude | |227 |2022-08-23 15:52:00 | 97.167| 108.063|Exclude | |228 |2022-08-23 15:53:00 | 96.933| 110.177|Exclude | |229 |2022-08-23 15:54:00 | 96.617| 110.599|Exclude | |230 |2022-08-23 15:55:00 | 99.167| 110.407|Exclude | |231 |2022-08-23 15:56:00 | 99.217| 110.831|Exclude | |232 |2022-08-23 15:57:00 | 99.867| 107.965|Exclude | |233 |2022-08-23 15:58:00 | 99.517| 109.573|Exclude | |234 |2022-08-23 15:59:00 | 100.150| 110.856|Exclude | |235 |2022-08-23 16:00:00 | 104.000| 111.251|Exclude | |236 |2022-08-23 16:01:00 | 103.867| 107.959|Exclude | |237 |2022-08-23 16:02:00 | 102.950| 105.886|Exclude | |238 |2022-08-23 16:03:00 | 103.083| 110.023|Exclude | |239 |2022-08-23 16:04:00 | 104.750| 108.657|Exclude | |240 |2022-08-23 16:05:00 | 106.250| 109.043|Exclude | |241 |2022-08-23 16:06:00 | 106.117| 107.719|Exclude | |242 |2022-08-23 16:07:00 | 105.133| 108.682|Exclude | |243 |2022-08-23 16:08:00 | 104.283| 108.644|Exclude | |244 |2022-08-23 16:09:00 | 106.733| 110.768|Exclude | |245 |2022-08-23 16:10:00 | 105.750| 109.096|Exclude | |246 |2022-08-23 16:11:00 | 108.283| 111.338|Exclude | |247 |2022-08-23 16:12:00 | 105.683| 109.452|Exclude | |248 |2022-08-23 16:13:00 | 110.700| 108.528|Exclude | |249 |2022-08-23 16:14:00 | 107.417| 109.225|Exclude | |250 |2022-08-23 16:15:00 | 111.150| 109.374|Exclude | |251 |2022-08-23 16:16:00 | 107.433| 109.223|Exclude | |252 |2022-08-23 16:17:00 | 111.733| 108.924|Exclude | |253 |2022-08-23 16:18:00 | 110.300| 109.593|Exclude | |254 |2022-08-23 16:19:00 | 113.117| 108.165|Exclude | |255 |2022-08-23 16:20:00 | 110.700| 109.531|Exclude | |256 |2022-08-23 16:21:00 | 113.283| 109.285|Exclude | |257 |2022-08-23 16:22:00 | 112.900| 110.872|Exclude | |258 |2022-08-23 16:23:00 | 112.783| 106.584|Exclude | |259 |2022-08-23 16:24:00 | 111.833| 110.327|Exclude | |260 |2022-08-23 16:25:00 | 114.267| 111.036|Exclude | |261 |2022-08-23 16:26:00 | 113.417| 109.443|Exclude | |262 |2022-08-23 16:27:00 | 115.350| 109.085|Exclude | |263 |2022-08-23 16:28:00 | 114.767| 111.173|Exclude | |264 |2022-08-23 16:29:00 | 117.267| 108.670|Exclude | |265 |2022-08-23 16:30:00 | 117.533| 108.144|Exclude | |266 |2022-08-23 16:31:00 | 117.033| 108.857|Exclude | |267 |2022-08-23 16:32:00 | 116.733| 109.441|Exclude | |268 |2022-08-23 16:33:00 | 120.183| 110.631|Exclude | |269 |2022-08-23 16:34:00 | 118.150| 107.032|Exclude | |270 |2022-08-23 16:35:00 | 118.150| 109.625|Exclude | |271 |2022-08-23 16:36:00 | 122.367| 109.675|Exclude | |272 |2022-08-23 16:37:00 | 122.650| 109.475|Exclude | |273 |2022-08-23 16:38:00 | 121.467| 109.730|Exclude | |274 |2022-08-23 16:39:00 | 123.367| 110.014|Exclude | |275 |2022-08-23 16:40:00 | 123.133| 109.612|Exclude | |276 |2022-08-23 16:41:00 | 124.833| 107.145|Exclude | |277 |2022-08-23 16:42:00 | 122.883| 109.070|Exclude | |278 |2022-08-23 16:43:00 | 123.217| 108.417|Exclude | |279 |2022-08-23 16:44:00 | 123.700| 109.825|Exclude | |280 |2022-08-23 16:45:00 | 125.417| 109.615|Exclude | |281 |2022-08-23 16:46:00 | 126.367| 109.323|Exclude | |282 |2022-08-23 16:47:00 | 126.000| 110.344|Exclude | |283 |2022-08-23 16:48:00 | 126.617| 107.729|Exclude | |284 |2022-08-23 16:49:00 | 125.850| 109.271|Exclude | |285 |2022-08-23 16:50:00 | 126.250| 108.375|Exclude | |286 |2022-08-23 16:51:00 | 129.033| 109.131|Exclude | |287 |2022-08-23 16:52:00 | 127.067| 110.225|Exclude | |288 |2022-08-23 16:53:00 | 129.133| 106.589|Exclude | |289 |2022-08-23 16:54:00 | 128.800| 109.127|Exclude | |290 |2022-08-23 16:55:00 | 128.467| 108.886|Exclude | |291 |2022-08-23 16:56:00 | 128.667| 109.866|Exclude | |292 |2022-08-23 16:57:00 | 132.267| 108.927|Exclude | |293 |2022-08-23 16:58:00 | 130.917| 108.875|Exclude | |294 |2022-08-23 16:59:00 | 127.533| 109.564|Exclude | |295 |2022-08-23 17:00:00 | 131.467| 110.747|Exclude | |296 |2022-08-23 17:01:00 | 133.300| 110.278|Exclude | |297 |2022-08-23 17:02:00 | 131.483| 110.090|Exclude | |298 |2022-08-23 17:03:00 | 132.150| 110.310|Exclude | |299 |2022-08-23 17:04:00 | 133.783| 108.657|Exclude | |300 |2022-08-23 17:05:00 | 132.750| 107.997|Exclude | |301 |2022-08-23 17:06:00 | 136.667| 110.599|Exclude | |302 |2022-08-23 17:07:00 | 135.317| 110.088|Exclude | |303 |2022-08-23 17:08:00 | 138.733| 109.462|Exclude | |304 |2022-08-23 17:09:00 | 134.783| 108.235|Exclude | |305 |2022-08-23 17:10:00 | 136.467| 111.212|Exclude | |306 |2022-08-23 17:11:00 | 136.783| 109.678|Exclude | |307 |2022-08-23 17:12:00 | 136.050| 107.934|Exclude | |308 |2022-08-23 17:13:00 | 134.867| 110.068|Exclude | |309 |2022-08-23 17:14:00 | 140.083| 108.564|Exclude | |310 |2022-08-23 17:15:00 | 141.550| 107.201|Exclude | |311 |2022-08-23 17:16:00 | 138.700| 109.266|Exclude | |312 |2022-08-23 17:17:00 | 136.583| 110.294|Exclude | |313 |2022-08-23 17:18:00 | 136.933| 109.635|Exclude | |314 |2022-08-23 17:19:00 | 139.883| 110.623|Exclude | |315 |2022-08-23 17:20:00 | 142.433| 109.385|Exclude | |316 |2022-08-23 17:21:00 | 142.283| 109.398|Exclude | |317 |2022-08-23 17:22:00 | 143.050| 108.095|Exclude | |318 |2022-08-23 17:23:00 | 141.250| 107.463|Exclude | |319 |2022-08-23 17:24:00 | 140.400| 109.569|Exclude | |320 |2022-08-23 17:25:00 | 143.317| 108.621|Exclude | |321 |2022-08-23 17:26:00 | 141.783| 110.817|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 |2022-08-23 12:06:00 | 0.833| 59.139|Include | |2 |2022-08-23 12:07:00 | 1.633| 68.218|Include | |3 |2022-08-23 12:08:00 | 1.450| 76.975|Include | |4 |2022-08-23 12:09:00 | 2.317| 81.463|Include | |5 |2022-08-23 12:10:00 | 2.817| 88.757|Include | |6 |2022-08-23 12:11:00 | 3.700| 77.692|Include | |7 |2022-08-23 12:12:00 | 3.417| 92.533|Include | |8 |2022-08-23 12:13:00 | 3.950| 88.875|Include | |9 |2022-08-23 12:14:00 | 4.367| 95.646|Include | |10 |2022-08-23 12:15:00 | 5.183| 94.341|Include | |11 |2022-08-23 12:16:00 | 4.883| 95.267|Include | |12 |2022-08-23 12:17:00 | 6.200| 99.091|Include | |13 |2022-08-23 12:18:00 | 5.733| 103.586|Include | |14 |2022-08-23 12:19:00 | 6.417| 95.447|Include | |15 |2022-08-23 12:20:00 | 7.217| 104.920|Include | |16 |2022-08-23 12:21:00 | 7.633| 102.191|Include | |17 |2022-08-23 12:22:00 | 7.783| 100.362|Include | |18 |2022-08-23 12:23:00 | 8.033| 99.164|Include | |19 |2022-08-23 12:24:00 | 8.233| 105.389|Include | |20 |2022-08-23 12:25:00 | 9.667| 103.203|Include | |21 |2022-08-23 12:26:00 | 9.250| 104.310|Include | |22 |2022-08-23 12:27:00 | 10.700| 102.215|Include | |23 |2022-08-23 12:28:00 | 11.217| 97.532|Include | |24 |2022-08-23 12:29:00 | 12.033| 97.052|Include | |25 |2022-08-23 12:30:00 | 10.867| 97.760|Include | |26 |2022-08-23 12:31:00 | 11.367| 104.163|Include | |27 |2022-08-23 12:32:00 | 12.700| 106.219|Include | |28 |2022-08-23 12:33:00 | 12.867| 101.907|Include | |29 |2022-08-23 12:34:00 | 14.000| 104.777|Include | |30 |2022-08-23 12:35:00 | 13.817| 106.018|Include | |31 |2022-08-23 12:36:00 | 13.300| 104.315|Include | |32 |2022-08-23 12:37:00 | 14.067| 101.657|Include | |33 |2022-08-23 12:38:00 | 14.817| 105.824|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 @ 30.00/sis the expected CPU usage at 30.00 requests per second. The columnEstimateshould 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 columnStd. Errorshould be used to determine the confidence interval around the estimate:
| |Class |Test |Measure |Units |Coef | Estimate| Std. Error| t value| p.value| Est Use @ 30.00/s| |:--|:-------|:----------|:---------|:------------------------|:----|--------:|----------:|-------:|-------:|-----------------:| |5 |LOAD |TermAppISO |CPU Usage |Percent single processor |Rate | 0.2639| 0.0105| 25.0938| 0| 7.9173| |8 |MODEL |TermAppISO |CPU Usage |Percent single processor |Rate | 0.4953| 0.0277| 17.8938| 0| 14.8599| |11 |NETWORK |TermAppISO |CPU Usage |Percent single processor |Rate | 0.6055| 0.1200| 5.0478| 0| 18.1651|
- 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
Estimateindicates this. The columnStd. Errorshould be used to determine a confidence interval:
| |Class |Test |Measure |Units |Coef | Estimate| Std. Error| t value| p.value| |:--|:-------|:----------|:---------|:------------------------|:----|--------:|----------:|-------:|-------:| |5 |LOAD |TermAppISO |CPU Usage |Percent single processor |Bias | 0.7803| 0.0945| 8.2564| 0| |8 |MODEL |TermAppISO |CPU Usage |Percent single processor |Bias | 2.3382| 0.2487| 9.3998| 0| |11 |NETWORK |TermAppISO |CPU Usage |Percent single processor |Bias | 98.5533| 1.0779| 91.4298| 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 @ 30.00/sis the expected Network usage at 30.00 requests per second. The columnEstimateshould 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 columnStd. Errorshould be used to determine the confidence interval around the estimate:
| |Class |Test |Measure |Units |Coef | Estimate| Std. Error| t value| p.value| Est Use @ 30.00/s| |:--|:-------|:----------|:------------------|:---------------|:----|------------:|-----------:|-------:|-------:|-----------------:| |1 |LOAD |TermAppISO |Network Usage recv |bits per second |Rate | 6663052.131| 215589.2848| 30.9062| 0| 199891563.9| |2 |LOAD |TermAppISO |Network Usage sent |bits per second |Rate | 9975775.822| 278065.1130| 35.8757| 0| 299273274.7| |3 |MODEL |TermAppISO |Network Usage recv |bits per second |Rate | 12115566.626| 462562.1313| 26.1923| 0| 363466998.8| |4 |MODEL |TermAppISO |Network Usage sent |bits per second |Rate | 13895344.467| 488665.5018| 28.4353| 0| 416860334.0| |6 |LOAD |TermAppISO |Network Usage recv |bits per second |Rate | 24126.854| 645.3996| 37.3828| 0| 723805.6| |7 |LOAD |TermAppISO |Network Usage sent |bits per second |Rate | 30426.772| 695.1658| 43.7691| 0| 912803.2| |9 |MODEL |TermAppISO |Network Usage recv |bits per second |Rate | 26257.266| 768.0186| 34.1883| 0| 787718.0| |10 |MODEL |TermAppISO |Network Usage sent |bits per second |Rate | 28421.664| 1192.1485| 23.8407| 0| 852649.9| |12 |NETWORK |TermAppISO |Network Usage recv |bits per second |Rate | 10072.766| 1048.6558| 9.6054| 0| 302183.0| |13 |NETWORK |TermAppISO |Network Usage sent |bits per second |Rate | 4643.404| 878.5013| 5.2856| 0| 139302.1|
- 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
Estimateindicates this. The columnStd. Errorshould 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 | 3158375092.8| 1937282.114| 1630.3124| 0| |2 |LOAD |TermAppISO |Network Usage sent |bits per second |Bias | 5853510229.8| 2498688.979| 2342.6326| 0| |3 |MODEL |TermAppISO |Network Usage recv |bits per second |Bias | 7935778688.1| 4156576.447| 1909.2103| 0| |4 |MODEL |TermAppISO |Network Usage sent |bits per second |Bias | 7180650596.3| 4391140.947| 1635.2585| 0| |6 |LOAD |TermAppISO |Network Usage recv |bits per second |Bias | 186938.6| 5799.551| 32.2333| 0| |7 |LOAD |TermAppISO |Network Usage sent |bits per second |Bias | 377667.5| 6246.749| 60.4583| 0| |9 |MODEL |TermAppISO |Network Usage recv |bits per second |Bias | 191159.3| 6901.403| 27.6986| 0| |10 |MODEL |TermAppISO |Network Usage sent |bits per second |Bias | 303591.5| 10712.628| 28.3396| 0| |12 |NETWORK |TermAppISO |Network Usage recv |bits per second |Bias | 697370.7| 9423.205| 74.0057| 0|
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 2022-08-23 12:04:01 and ended at 2022-08-23 17:26:00.
Resource usage for platform Appliance
Server class LOAD CPU usage
CPU Usage - ssCPUSystemUser
Call:
lm(formula = Value.sum ~ Rate, data = summ.class.include)
Residuals:
Min 1Q Median 3Q Max
0 0 0 0 0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0 0 NA NA
Rate 0 0 NA NA
Residual standard error: 0 on 31 degrees of freedom
Multiple R-squared: NaN, Adjusted R-squared: NaN
F-statistic: NaN on 1 and 31 DF, p-value: NA
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
-8295104 -3210035 -580457 3134317 10702471
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.158e+09 1.937e+06 1630.31 <2e-16 ***
Rate 6.663e+06 2.156e+05 30.91 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5179000 on 31 degrees of freedom
Multiple R-squared: 0.9686, Adjusted R-squared: 0.9676
F-statistic: 955.2 on 1 and 31 DF, p-value: < 2.2e-16
Network Usage sent - sent
Call:
lm(formula = Value.sum ~ Rate, data = summ.class.include)
Residuals:
Min 1Q Median 3Q Max
-11844253 -4327603 241651 3332146 14218589
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.854e+09 2.499e+06 2342.63 <2e-16 ***
Rate 9.976e+06 2.781e+05 35.88 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6680000 on 31 degrees of freedom
Multiple R-squared: 0.9765, Adjusted R-squared: 0.9757
F-statistic: 1287 on 1 and 31 DF, p-value: < 2.2e-16
Server class MODEL CPU usage
CPU Usage - ssCPUSystemUser
Call:
lm(formula = Value.sum ~ Rate, data = summ.class.include)
Residuals:
Min 1Q Median 3Q Max
0 0 0 0 0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0 0 NA NA
Rate 0 0 NA NA
Residual standard error: 0 on 31 degrees of freedom
Multiple R-squared: NaN, Adjusted R-squared: NaN
F-statistic: NaN on 1 and 31 DF, p-value: NA
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
-16109639 -8075317 -2591944 5680936 22027323
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.936e+09 4.157e+06 1909.21 <2e-16 ***
Rate 1.212e+07 4.626e+05 26.19 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 11110000 on 31 degrees of freedom
Multiple R-squared: 0.9568, Adjusted R-squared: 0.9554
F-statistic: 686 on 1 and 31 DF, p-value: < 2.2e-16
Network Usage sent - sent
Call:
lm(formula = Value.sum ~ Rate, data = summ.class.include)
Residuals:
Min 1Q Median 3Q Max
-18032408 -8025717 -2190444 6694795 23778225
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.181e+09 4.391e+06 1635.26 <2e-16 ***
Rate 1.390e+07 4.887e+05 28.43 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 11740000 on 31 degrees of freedom
Multiple R-squared: 0.9631, Adjusted R-squared: 0.9619
F-statistic: 808.6 on 1 and 31 DF, p-value: < 2.2e-16
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
-0.64261 -0.14168 -0.02961 0.14401 0.50521
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.78027 0.09450 8.256 2.52e-09 ***
Rate 0.26391 0.01052 25.094 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2527 on 31 degrees of freedom
Multiple R-squared: 0.9531, Adjusted R-squared: 0.9516
F-statistic: 629.7 on 1 and 31 DF, p-value: < 2.2e-16
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
-29441 -10648 -2023 10067 32282
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 186938.6 5799.6 32.23 <2e-16 ***
Rate 24126.9 645.4 37.38 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 15500 on 31 degrees of freedom
Multiple R-squared: 0.9783, Adjusted R-squared: 0.9776
F-statistic: 1397 on 1 and 31 DF, p-value: < 2.2e-16
Network Usage sent - sent
Call:
lm(formula = Value.sum ~ Rate, data = summ.class.include)
Residuals:
Min 1Q Median 3Q Max
-44462 -8603 558 7361 35525
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 377667.5 6246.7 60.46 <2e-16 ***
Rate 30426.8 695.2 43.77 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 16700 on 31 degrees of freedom
Multiple R-squared: 0.9841, Adjusted R-squared: 0.9836
F-statistic: 1916 on 1 and 31 DF, p-value: < 2.2e-16
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
-1.20920 -0.50251 0.09593 0.48240 1.25388
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.33816 0.24875 9.40 1.38e-10 ***
Rate 0.49533 0.02768 17.89 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.665 on 31 degrees of freedom
Multiple R-squared: 0.9117, Adjusted R-squared: 0.9089
F-statistic: 320.2 on 1 and 31 DF, p-value: < 2.2e-16
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
-35959 -12926 2843 12690 39583
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 191159 6901 27.70 <2e-16 ***
Rate 26257 768 34.19 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 18450 on 31 degrees of freedom
Multiple R-squared: 0.9742, Adjusted R-squared: 0.9733
F-statistic: 1169 on 1 and 31 DF, p-value: < 2.2e-16
Network Usage sent - sent
Call:
lm(formula = Value.sum ~ Rate, data = summ.class.include)
Residuals:
Min 1Q Median 3Q Max
-48697 -19672 -3903 18678 53214
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 303592 10713 28.34 <2e-16 ***
Rate 28422 1192 23.84 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 28640 on 31 degrees of freedom
Multiple R-squared: 0.9483, Adjusted R-squared: 0.9466
F-statistic: 568.4 on 1 and 31 DF, p-value: < 2.2e-16
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
-4.7062 -1.9086 0.0565 2.0354 5.3696
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 98.5533 1.0779 91.430 < 2e-16 ***
Rate 0.6055 0.1200 5.048 1.87e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.882 on 31 degrees of freedom
Multiple R-squared: 0.4511, Adjusted R-squared: 0.4334
F-statistic: 25.48 on 1 and 31 DF, p-value: 1.873e-05
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
-49419 -20269 348 23427 35998
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 697371 9423 74.006 < 2e-16 ***
Rate 10073 1049 9.605 8.31e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 25190 on 31 degrees of freedom
Multiple R-squared: 0.7485, Adjusted R-squared: 0.7404
F-statistic: 92.26 on 1 and 31 DF, p-value: 8.308e-11
Network Usage sent - sent
Call:
lm(formula = Value.sum ~ Rate, data = summ.class.include)
Residuals:
Min 1Q Median 3Q Max
-38339 -13208 -691 15566 39753
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4956.3 7894.2 -0.628 0.535
Rate 4643.4 878.5 5.286 9.47e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21100 on 31 degrees of freedom
Multiple R-squared: 0.474, Adjusted R-squared: 0.4571
F-statistic: 27.94 on 1 and 31 DF, p-value: 9.469e-06
Performance
- For test TermAppISO/TermAppISONFT (Test 1, from 2022-08-23 12:04:01 to 2022-08-23 17:26:00), 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 15.00 arrivals per second (this rate is as opposed to the projected/future peak rate). This cut-off time occurred at 2022-08-23 12:38:00. 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:
| |Basename |Outcome | Count| Percent| Resp| StdDev| |:--|:--------------------------|:------------------------------|-----:|-------:|-----:|------:| |1 |authorisation_request_1100 |AUTHORISATION_RESPONSE_1110_OK | 15744| 100| 0.203| 0.01|
- Distribution of response times where rate does not exceed the target cutoff target rate for outcome TRANSACTION_ADVICE_RESPONSE_1230_OK:
| |Basename |Outcome | Count| Percent| Resp| StdDev| |:--|:--------------------------------|:-----------------------------------|-----:|-------:|-----:|------:| |2 |transaction_advice_response_1230 |TRANSACTION_ADVICE_RESPONSE_1230_OK | 15696| 100| 0.103| 0.005|
Operation authorisation_request_1100
Operation transaction_advice_response_1230
Resource Usage
Linux Server CPU Usage
TermAppISO
Server LOAD0
Server LOAD1
Server MODELAPP0
Server MODELAPP1
Server NETWORK
Linux Server Disk Activity
Server LOAD0
Server MODELAPP0
Server MODELAPP1
Server NETWORK
Server LOAD1
Linux Server Memory Usage
Server LOAD0
Server MODELAPP0
Server MODELAPP1
Server NETWORK
Server LOAD1
Linux Server Swap Usage
Server LOAD0
Server MODELAPP0
Server MODELAPP1
Server NETWORK
Server LOAD1
Linux Server Network Usage
Server LOAD0
Server MODELAPP0
Server MODELAPP1
Server NETWORK
Server LOAD1
Linux Server Usage from SNMP MIB Data
Resource Usage for Server LOAD0_SNMP
CPU Usage for Server LOAD0_SNMP
Memory Usage for Server LOAD0_SNMP
Network Usage for Server LOAD0_SNMP
Resource Usage for Server LOAD1_SNMP
CPU Usage for Server LOAD1_SNMP
Memory Usage for Server LOAD1_SNMP
Network Usage for Server LOAD1_SNMP
Resource Usage for Server MODELAPP0_SNMP
CPU Usage for Server MODELAPP0_SNMP
Memory Usage for Server MODELAPP0_SNMP
Network Usage for Server MODELAPP0_SNMP
Resource Usage for Server MODELAPP1_SNMP
CPU Usage for Server MODELAPP1_SNMP
Memory Usage for Server MODELAPP1_SNMP
Network Usage for Server MODELAPP1_SNMP
Wintel Server Resource Usage
Test TermAppISO
Wintel Server Resource Usage
Test TermAppISO
pSeries AIX Server Reource Usage
Test TermAppISO
