Using gala-anteater
gala-anteater is an AI-based operating system exception detection platform. It provides functions such as time series data preprocessing, exception detection, and exception reporting. Based on offline pre-training, online model incremental learning and model update, it can be well adapted to multi-dimensional and multi-modal data fault diagnosis.
This chapter describes how to deploy and use the gala-anteater service.
Installation
Mount the repositories.
basic
[everything]
name=everything
baseurl=http://121.36.84.172/dailybuild/EBS-openEuler-22.03-LTS-SP4/EBS-openEuler-22.03-LTS-SP4/everything/$basearch/
enabled=1
gpgcheck=0
priority=1
[EPOL]
name=EPOL
baseurl=http://repo.openeuler.org/EBS-openEuler-22.03-LTS-SP4/EPOL/main/$basearch/
enabled=1
gpgcheck=0
priority=1Install gala-anteater.
bash
# yum install gala-anteaterConfiguration
Note: Some gala-anteater parameters can be configured in /etc/gala-anteater/config/gala-anteater.yaml.
Startup Parameters
| Parameter | Parameter Full Name | Type | Mandatory (Yes/No) | Default Value | Name | Description |
|---|---|---|---|---|---|---|
| -ks | --kafka_server | string | True | KAFKA_SERVER | IP address of the Kafka server, for example, localhost / xxx.xxx.xxx.xxx. | |
| -kp | --kafka_port | string | True | KAFKA_PORT | Port number of the Kafka server, for example, 9092. | |
| -ps | --prometheus_server | string | True | PROMETHEUS_SERVER | IP address of the Prometheus server, for example, localhost / xxx.xxx.xxx.xxx. | |
| -pp | --prometheus_port | string | True | PROMETHEUS_PORT | Port number of the Prometheus server, for example, 9090. | |
| -m | --model | string | False | vae | MODEL | Exception detection model. Currently, two exception detection models are supported: random_forest and vae. random_forest: random forest model, which does not support online learning vae: Variational Atuoencoder (VAE), which is an unsupervised model and supports model update based on historical data during the first startup. |
| -d | --duration | int | False | 1 | DURATION | Frequency of executing the exception detection model. The unit is minute, which means that the detection is performed every x minutes. |
| -r | --retrain | bool | False | False | RETRAIN | Whether to use historical data to update and iterate the model during startup. Currently, only the VAE model is supported. |
| -l | --look_back | int | False | 4 | LOOK_BACK | Whether to update the model based on the historical data of the last x days. |
| -t | --threshold | float | False | 0.8 | THRESHOLD | Threshold of the exception detection model, ranging from 0 to 1. A larger value can reduce the false positive rate of the model. It is recommended that the value be greater than or equal to 0.5. |
| -sli | --sli_time | int | False | 400 | SLI_TIME | Application performance metric. The unit is ms. A larger value can reduce the false positive rate of the model. It is recommended that the value be greater than or equal to 200. For scenarios with a high false positive rate, it is recommended that the value be greater than 1000. |
Start
Start gala-anteater.
Note: gala-anteater can be started and run in command line mode, but cannot be started and run in systemd mode.
- Running in online training mode (recommended)
bash
gala-anteater -ks {ip} -kp {port} -ps {ip} -pp {port} -m vae -r True -l 7 -t 0.6 -sli 400- Running in common mode
bash
gala-anteater -ks {ip} -kp {port} -ps {ip} -pp {port} -m vae -t 0.6 -sli 400Query the gala-anteater service status.
If the following information is displayed, the service is started successfully. The startup log is saved to the logs/anteater.log file in the current running directory.
log
2022-09-01 17:52:54,435 - root - INFO - Run gala_anteater main function...
2022-09-01 17:52:54,436 - root - INFO - Start to try updating global configurations by querying data from Kafka!
2022-09-01 17:52:54,994 - root - INFO - Loads metric and operators from file: xxx\metrics.csv
2022-09-01 17:52:54,997 - root - INFO - Loads metric and operators from file: xxx\metrics.csv
2022-09-01 17:52:54,998 - root - INFO - Start to re-train the model based on last day metrics dataset!
2022-09-01 17:52:54,998 - root - INFO - Get training data during 2022-08-31 17:52:00+08:00 to 2022-09-01 17:52:00+08:00!
2022-09-01 17:53:06,994 - root - INFO - Spends: 11.995422840118408 seconds to get unique machine_ids!
2022-09-01 17:53:06,995 - root - INFO - The number of unique machine ids is: 1!
2022-09-01 17:53:06,996 - root - INFO - Fetch metric values from machine: xxxx.
2022-09-01 17:53:38,385 - root - INFO - Spends: 31.3896164894104 seconds to get get all metric values!
2022-09-01 17:53:38,392 - root - INFO - The shape of training data: (17281, 136)
2022-09-01 17:53:38,444 - root - INFO - Start to execute vae model training...
2022-09-01 17:53:38,456 - root - INFO - Using cpu device
2022-09-01 17:53:38,658 - root - INFO - Epoch(s): 0 train Loss: 136.68 validate Loss: 117.00
2022-09-01 17:53:38,852 - root - INFO - Epoch(s): 1 train Loss: 113.73 validate Loss: 110.05
2022-09-01 17:53:39,044 - root - INFO - Epoch(s): 2 train Loss: 110.60 validate Loss: 108.76
2022-09-01 17:53:39,235 - root - INFO - Epoch(s): 3 train Loss: 109.39 validate Loss: 106.93
2022-09-01 17:53:39,419 - root - INFO - Epoch(s): 4 train Loss: 106.48 validate Loss: 103.37
...
2022-09-01 17:53:57,744 - root - INFO - Epoch(s): 98 train Loss: 97.63 validate Loss: 96.76
2022-09-01 17:53:57,945 - root - INFO - Epoch(s): 99 train Loss: 97.75 validate Loss: 96.58
2022-09-01 17:53:57,969 - root - INFO - Schedule recurrent job with time interval 1 minute(s).
2022-09-01 17:53:57,973 - apscheduler.scheduler - INFO - Adding job tentatively -- it will be properly scheduled when the scheduler starts
2022-09-01 17:53:57,974 - apscheduler.scheduler - INFO - Added job "partial" to job store "default"
2022-09-01 17:53:57,974 - apscheduler.scheduler - INFO - Scheduler started
2022-09-01 17:53:57,975 - apscheduler.scheduler - DEBUG - Looking for jobs to run
2022-09-01 17:53:57,975 - apscheduler.scheduler - DEBUG - Next wakeup is due at 2022-09-01 17:54:57.973533+08:00 (in 59.998006 seconds)Output Data
If gala-anteater detects an exception, it sends the result to Kafka. The output data format is as follows:
json
{
"Timestamp":1659075600000,
"Attributes":{
"entity_id":"xxxxxx_sli_1513_18",
"event_id":"1659075600000_1fd37742xxxx_sli_1513_18",
"event_type":"app"
},
"Resource":{
"anomaly_score":1.0,
"anomaly_count":13,
"total_count":13,
"duration":60,
"anomaly_ratio":1.0,
"metric_label":{
"machine_id":"1fd37742xxxx",
"tgid":"1513",
"conn_fd":"18"
},
"recommend_metrics":{
"gala_gopher_tcp_link_notack_bytes":{
"label":{
"__name__":"gala_gopher_tcp_link_notack_bytes",
"client_ip":"x.x.x.165",
"client_port":"51352",
"hostname":"localhost.localdomain",
"instance":"x.x.x.172:8888",
"job":"prometheus-x.x.x.172",
"machine_id":"xxxxxx",
"protocol":"2",
"role":"0",
"server_ip":"x.x.x.172",
"server_port":"8888",
"tgid":"3381701"
},
"score":0.24421279500639545
},
...
},
"metrics":"gala_gopher_ksliprobe_recent_rtt_nsec"
},
"SeverityText":"WARN",
"SeverityNumber":14,
"Body":"TimeStamp, WARN, APP may be impacting sli performance issues."
}Licensed under the MulanPSL2
Copyright © 2026 openEuler. All rights reserved.J. ICP B. No. 2020036654-1
J.G.W.A.B. No. 11030102011597
J.G.W.A.B. No. 11030102011597Licensed underthe MulanPSL2
Copyright © 2026 openEuler. All rights reserved.
Bug