Abnormal KPI Detection Service - HUAWEI CLOUD...NAIE V200R021C20 Abnormal KPI Detection Service Issue 01 Date 2020-11-30 HUAWEI TECHNOLOGIES CO., LTD

NAIEV200R021C30

Abnormal KPI Detection Service

Issue 01

Date 2021-01-30

HUAWEI TECHNOLOGIES CO., LTD.

Copyright © Huawei Technologies Co., Ltd. 2021. All rights reserved.

No part of this document may be reproduced or transmitted in any form or by any means without priorwritten consent of Huawei Technologies Co., Ltd. Trademarks and Permissions

and other Huawei trademarks are trademarks of Huawei Technologies Co., Ltd.All other trademarks and trade names mentioned in this document are the property of their respectiveholders. NoticeThe purchased products, services and features are stipulated by the contract made between Huawei andthe customer. All or part of the products, services and features described in this document may not bewithin the purchase scope or the usage scope. Unless otherwise specified in the contract, all statements,information, and recommendations in this document are provided "AS IS" without warranties, guaranteesor representations of any kind, either express or implied.

The information in this document is subject to change without notice. Every effort has been made in thepreparation of this document to ensure accuracy of the contents, but all statements, information, andrecommendations in this document do not constitute a warranty of any kind, express or implied.

Huawei Technologies Co., Ltd.Address: Huawei Industrial Base

Bantian, LonggangShenzhen 518129People's Republic of China

Website: https://www.huawei.com

Email: [email protected]

Issue 01 (2021-01-30) Copyright © Huawei Technologies Co., Ltd. i

https://www.huawei.commailto:[email protected]

Contents

1 Documentation Guide............................................................................................................ 1

2 Product Overview.................................................................................................................... 22.1 What Is KPI Anomaly Detection Model Service............................................................................................................ 22.2 Application Scenarios............................................................................................................................................................. 22.3 Functions.................................................................................................................................................................................... 32.4 Benefits....................................................................................................................................................................................... 32.5 Restrictions................................................................................................................................................................................ 32.6 Basic Concepts.......................................................................................................................................................................... 52.7 Service Dependencies.............................................................................................................................................................62.8 Billing Description................................................................................................................................................................... 62.9 Accessing the KPI Anomaly Detection Model Service................................................................................................ 72.10 Change History...................................................................................................................................................................... 8

3 Quick Start................................................................................................................................ 93.1 Prerequisites.............................................................................................................................................................................. 93.2 Subscribing to the KPI Anomaly Detection Model Service....................................................................................... 93.3 Accessing the KPI Anomaly Detection Model Service.............................................................................................. 103.4 Operation Process................................................................................................................................................................. 103.5 Detection Object Configuration....................................................................................................................................... 113.5.1 Configuring KPI Attributes..............................................................................................................................................113.5.2 Importing Time Series Data........................................................................................................................................... 153.6 Advanced Settings................................................................................................................................................................ 163.6.1 Detection Policy................................................................................................................................................................. 163.6.2 Event Management.......................................................................................................................................................... 183.6.3 Task Management.............................................................................................................................................................203.7 Anomaly Dashboard............................................................................................................................................................ 213.7.1 Visualized Dashboard.......................................................................................................................................................213.7.2 Anomaly List........................................................................................................................................................................233.8 Change History...................................................................................................................................................................... 25

4 API Reference......................................................................................................................... 264.1 Environment Preparation................................................................................................................................................... 264.1.1 Obtaining Request Authentication .............................................................................................................................264.1.2 Obtaining the Project ID and Tenant ID.................................................................................................................... 27

NAIEAbnormal KPI Detection Service Contents

Issue 01 (2021-01-30) Copyright © Huawei Technologies Co., Ltd. ii

4.2 Change History...................................................................................................................................................................... 28

5 FAQs..........................................................................................................................................295.1 What Are the Service Scenarios of Time Series KPI Anomaly Detection...........................................................295.2 Can Time Series KPI Anomaly Detection Be Applied to All Scenarios................................................................295.3 What Are the Types of Time Series KPI Data.............................................................................................................. 295.4 Change History...................................................................................................................................................................... 30

6 Glossary................................................................................................................................... 31

NAIEAbnormal KPI Detection Service Contents

Issue 01 (2021-01-30) Copyright © Huawei Technologies Co., Ltd. iii

1 Documentation GuideDocuments including Introduction, Quick Start, FAQs, and Glossary are given tohelp customers learn and use the abnormal KPI detection service in order tocustomize site-oriented models.

Table 1-1 Documentation guide

Document Description

Introduction This document describes the positioning, application scenarios,functions, benefits, and restrictions of the abnormal KPIdetection service.

Quick Start This document describes how to use the abnormal KPI detectionservice to quickly generate KPI anomaly models, helping usersquickly get familiar with and use the abnormal KPI detectionservice.

FAQs This document provides answers to frequently asked questions(FAQs) for users of the abnormal KPI detection service.

Glossary This document describes the product terms related to theabnormal KPI detection service.

APIReference

This document describes the APIs of the abnormal KPI detectionservice, including the description, syntax, parameter description,examples, and other information.

NAIEAbnormal KPI Detection Service 1 Documentation Guide

Issue 01 (2021-01-30) Copyright © Huawei Technologies Co., Ltd. 1

2 Product Overview2.1 What Is KPI Anomaly Detection Model Service

This service identifies KPI anomalies from a large amount of KPI input data,identifies the KPI input mode based on the service configurations and data type,automatically optimizes algorithms, and predicts system faults or quickly locatefaults.

2.2 Application Scenarios

Call Drop Rate DetectionThe call drop rate is a core network KPI in the telecom domain. The traditionalfixed-threshold alarm mode cannot be used to identify normal KPI fluctuation andcannot predict KPI deterioration. The KPI anomaly detection service can rapidlypredict KPI trends to identify anomalies and report alarms.

Call Quality DetectionCall quality KPIs include call center KPIs of telecom operators. These KPIs are usedto improve target management. Call quality KPIs, such as the total number of callsand call abandonment rate, are monitored to detect sudden traffic increases anddecreases.

Website KPI DetectionThe KPIs of online mall websites of telecom operators reflect the quality andperformance of the websites. Website KPIs, such as the number of users, numberof advertisement clicks, and page traffic, are monitored to detect the impacts ofspecific factors on the websites and website changes in a timely manner.

NAIEAbnormal KPI Detection Service 2 Product Overview


2.3 Functions

Intelligent Anomaly DetectionAPIs are provided to detect time series. The following detection types aresupported: discrete, white noise, periodic, periodic+trend, periodic+abrupt changepoint, and aperiodic.

Automatic ModelingBased on an input data flow, the service automatically generates the algorithmmodel for a service.

LabelingSamples can be labeled as positive or negative samples and stored in the samplelibrary.

Sample ManagementTime series data samples can be managed. The storage duration is configurable.

2.4 Benefits

Diverse Application FieldsThis service supports multiple KPI modes (such as period, abrupt change, andtrend) and can detect KPI anomalies in multiple telecom fields, such as VoLTE corenetwork, EPC, and radio access network.

Accurate Anomaly LocatingMultiple built-in intelligent algorithms, such as GMM, XGBoost, and Holt-Winters,can be used to perform automatic optimization based on user data features. Theaccuracy of KPI anomaly detection is up to 95%.

High Efficiency and Easy IntegrationThe KPI anomaly detection service supports cloud-based deployment, elasticscaling, and on-demand system resource allocation. Detection can be performedfor more than 100,000 KPIs concurrently at millisecond-level. RESTful APIs aresupported to facilitate integration and accelerate service rollout.

2.5 Restrictions1. Time series data is continuous. The data loss rate is less than 10%.2. Data within at least four periods must be provided. For aperiodic data, it is

recommended that data within one week be provided.



3. The collection points for multi-KPI correlated detection must be the same.4. The following time series waveforms are supported:

– Discrete data

– White noise

– Periodic data

– Periodic+trend data



– Periodic+abrupt change data

– Aperiodic data

2.6 Basic Concepts

API

An Application Programming Interface (API) is a set of predefined functions usedby applications or developers to access a group of routines based on certain



software or hardware without the need to access the source code or understandthe internal mechanism.

KPI

A key performance indicator (KPI) reflects the performance of an object at acertain time point. A KPI can be aggregated.

2.7 Service Dependencies

ModelArts Service

The NAIE platform uses the ModelArts service provided by the Huawei publiccloud system to implement data preprocessing and large-scale distributed modeltraining.

IAM Service

The NAIE platform uses the Identity and Access Management (IAM) serviceprovided by the Huawei public cloud system to implement unified identityauthentication and permission management.

API Gateway

The NAIE platform must interconnect with the unified API gateway provided bythe Huawei public cloud system. The API gateway provides a unified entrance forusers to invoke NAIE cloud service APIs. APIs provided by the NAIE cloud servicefor tenants must be registered with the API gateway before being released.

Relationship with the OBS

The NAIE platform uses the Object Storage Service (OBS) to store data and modelbackup and snapshots, achieving secure, reliable, and low-cost storage.

Relationship with the CCE

The NAIE platform uses the Cloud Container Engine (CCE) to deploy models asonline services, satisfying requirements for high concurrency and elastic scaling.

2.8 Billing Description

Billing Items

The KPI anomaly detection model service is charged based on the number of KPIsand service subscription duration set during the subscription. The billing itemsinclude the number of KPIs, as described in Table 2-1.



Table 2-1 Billing items

Billing Item Description

Number ofKPIs

The KPI anomaly detection model service is charged based onthe number of KPIs and service subscription duration set duringthe subscription.You will be charged for the service after the subscription. If youdo not use the service, unsubscribe from it in a timely mannerto avoid unnecessary fees.

Billing ModePay-per-use mode is used. Fees are charged based on the number of KPIs andservice subscription duration set during the subscription.

Number of KPIs = Number of KPIs on a single node x Number of nodes

Billing formula: Unit price x Number of KPI steps x Service subscription duration.The billing step is 1000 KPIs.

Changing Billing ModeFees are charged for the KPI anomaly detection model service after thesubscription. Users can unsubscribe from or re-subscribe to the service as required.No service change configuration is involved.

RenewalUsers can recharge their accounts in time as required to ensure that the KPIanomaly detection model service can be used properly.

Expiration and Overdue PaymentIf you do not renew your subscription on time, the cloud platform provides a graceperiod and a retention period. The grace period and retention period depend onthe customer level. For details, see Grace Period and Retention Period.

If the account is not recharged after the retention period expires, the resources arecleared.

2.9 Accessing the KPI Anomaly Detection Model ServiceStep 1 Enter https://console-intl.huaweicloud.com/naie/ in the address box of a

browser on a user PC and press Enter to access the NAIE service official website.

Step 2 Click Sign In in the upper right corner to access the login page.

Step 3 Select IAM User Login and enter the tenant name, user name, and password.

You can also log in using an account. Change the password after the firstsuccessful login and change the password periodically.



https://support.huaweicloud.com/intl/en-us/usermanual-billing/en-us_topic_0083039587.htmlhttps://console-intl.huaweicloud.com/naie/

Step 4 Click Log In to access the NAIE service official website.

Step 5 Choose AI Services > Model and Training Service > Communication ModelService > Abnormal KPI Detection Service. The introduction page of the KPIanomaly detection model service is displayed.

Step 6 Click Enter Service. The KPI anomaly detection model service page is displayed.

----End

2.10 Change HistoryDate Change Description

2020-06-30 Added section "Billing Description."

2019-12-30 Optimized the outline of Product Overview and rewrote theentire document.

2019-04-30 Released this document officially for the first time.



3 Quick Start3.1 Prerequisites

● You have registered a HUAWEI CLOUD account.● The administrator tenant and IAM user of the NAIE platform have been

registered.● You have subscribed to the KPI anomaly detection model service of the NAIE.

3.2 Subscribing to the KPI Anomaly Detection ModelService

Step 1 Enter https://console-intl.huaweicloud.com/naie/ in the address box of abrowser on a user PC and press Enter to access the NAIE service official website.

When you access the NAIE service official website for the first time, the AccessAuthorization page is displayed. Click Authorize.

Step 2 Click Sign In in the upper right corner of the page. The login page is displayed.

Step 3 Enter the tenant name and password, and click Log In to access the NAIE serviceofficial website.

Change the password after the first successful login and change the passwordperiodically.


Step 5 Click Buy Now. The page shown in Figure 3-1 is displayed.

You can click Learn about billing details to better understand the resources,specifications, and price information provided by the KPI anomaly detection modelservice. In addition, when you use a specific resource, the service displays an eye-catching charging prompt on the page.

The parameters are described as follows:

NAIEAbnormal KPI Detection Service 3 Quick Start


https://console-intl.huaweicloud.com/naie/

● Region: HUAWEI CLOUD region that provides services.● KPI Indicators Number: Number of KPIs to be detected. Set this parameter

based on live network requirements.

Figure 3-1 Subscribing to the KPI anomaly detection model service

Step 6 Click Use Immediately. The service subscription is complete.

----End

3.3 Accessing the KPI Anomaly Detection Model ServiceStep 1 Enter https://console-intl.huaweicloud.com/naie/ in the address box of a

browser on a user PC and press Enter to access the NAIE service official website.

Step 2 Click Sign In in the upper right corner to access the login page.

Step 3 Select IAM User Login and enter the tenant name, user name, and password.

You can also log in using an account. Change the password after the firstsuccessful login and change the password periodically.

Step 4 Click Log In to access the NAIE service official website.


Step 6 Click Enter Service. The KPI anomaly detection model service page is displayed.

----End

3.4 Operation ProcessFigure 3-2 shows how to use the KPI anomaly detection model service.



https://console-intl.huaweicloud.com/naie/

Figure 3-2 Operation process

3.5 Detection Object ConfigurationConfigure the KPI attributes of the network or system to be detected and importthe corresponding time series data.

3.5.1 Configuring KPI AttributesBefore performing KPI anomaly detection, you need to configure the KPIs to bedetected. You can import KPIs in batches.



Importing KPIs in Batches

Step 1 On the menu bar, choose Detection Object Configuration > KPI AttributeConfiguration. The KPI Attribute Configuration page is displayed, as shown inFigure 3-3.

Figure 3-3 Configuring KPI attributes

Step 2 Click Batch. The batch KPI attribute import page is displayed, as shown in Figure3-4.

Figure 3-4 Importing KPIs in batches

Step 3 Click Download Template to download the import file.

Step 4 Click to select a template file from the local PC and click OK to import KPIsin batches.

Import succeeded. is displayed, indicating that the import is successful.

----End



Modifying KPIs

Step 1 On the menu bar, choose Detection Object Configuration > KPI AttributeConfiguration. The KPI Attribute Configuration page is displayed, as shown inFigure 3-5.

Figure 3-5 Configuring KPI attributes

Step 2 Select a KPI and click Modify. The Modify KPI dialog box is displayed, as shown inFigure 3-6.

Figure 3-6 Modifying a KPI

Table 3-1 describes the parameters.

Table 3-1 Parameter settings

Parameter Description

DetectionKPI ID

KPI IDThe value is specified in the import template file and cannot bechanged after the import.An example is 4_117491624.




KPI Name KPI nameAn example is USN_kpi7_sample_ratio.

KPI Type KPI typeThe values are as follows:● ratio: ratio● failureratio: failure rate● attemps: number of requests● success: number of successful operations● failure: number of failures

Group You can add different KPIs to the same group based on servicerequirements.Set the parameter based on site conditions.

NE Type Type of the NE to which a KPI belongs

NE ID ID of the NE to which a KPI belongs

DetectionCategory

Detected KPI typeThe value is specified in the import template file and cannot bechanged after the import.The values are as follows:● Detect all without differentiation● Routine detection: Perform detection during routine

operations.● Major operation detection: Perform detection during

upgrade and migration.

DetectionDirection

A KPI has a value range and a threshold. Detection needs to beperformed based on KPI trends. Label the values exceeding thethreshold as abnormal.The values are as follows:● UP: Detection is performed when the KPI is increasing. If a KPI

value exceeds the maximum threshold, the value is labeled asabnormal.

● DOWN: Detection is performed when the KPI is decreasing. Ifa KPI value is less than the minimum threshold, the value islabeled as abnormal.

● BOTH: Detection is performed regardless of the KPI trend. If aKPI value is greater than the upper threshold or less than thelower threshold, the value is labeled as abnormal.

For example, if this parameter is set to DOWN for the callanswer success rate and the value of this KPI is less than theminimum threshold, the value is labled as abnormal.




Sensitivity KPI detection sensitivityThe value range is [1,10].An example is 3.

StaticThresholdParameters

Static KPI threshold. If a KPI value exceeds this threshold, analarm is reported.● Detection Type: KPI threshold source The value can be AI,

Static Threshold, or AI+Static Threshold.● Maximum value: maximum value of the static threshold● Minimum value: minimum value of the static threshold● Offset: offset to the static thresholdFor example, if this parameter is set to Static Threshold,Maximum value is set to 90, and Minimum value is set to 1, analarm is reported when the KPI value exceeds 91.

Step 3 Click OK. The KPI attributes are modified.

----End

Other Operations for Configuring KPI Attributes● You can quickly query KPIs by KPI ID, group, or name.

● Select a KPI and click Delete to delete the KPI.

● Select a KPI and click Modify to modify KPI information.

3.5.2 Importing Time Series DataStep 1 On the menu bar, choose Detection Object Configuration > Time Series Data

Import. The Time Series Data Import page is displayed, as shown in Figure 3-7.

By default, the dataset list, data source type, file name, and total number of datarecords that have been imported to the platform are displayed under Data Source

Information. You can also click and in the Operation column to viewdetails about the dataset or delete the current dataset.

Figure 3-7 Importing time series data

Step 2 Click Add. The data source information page is displayed, as shown in Figure 3-8.



Figure 3-8 Importing data

Step 3 Click Download Template to download the dataset import template to the localPC.

Step 4 Set the dataset source to Local upload. In the Select a file area, click toimport the downloaded template file from the local PC.

Step 5 Click Data Import to import the template file.

----End

3.6 Advanced SettingsConfigure detection policies, events, and tasks based on site requirements.

3.6.1 Detection PolicyStep 1 On the menu bar, choose Advanced Settings > Detection Policy. The System

Parameter Configuration page is displayed, as shown in Figure 3-9.

Figure 3-9 System parameter configurations

Set detection parameters by referring to Table 3-2.



Table 3-2 Detection parameters

Area Parameter

Description

TrainingParameters

Trainingtask startinterval(s)

Interval for starting a training task.For example, the value 30 indicates that the task isstarted every 30s. Only integers are supported.

Start Timefor FirstTraining

Start time for the first training based on the importeddata source. The latest training start time can be set tothe earliest start time of the time series data.Default value: Jan 1, 2019 00:00:00

DetectionParameter

Detectiontask startinterval(s)

Interval for starting a detection task.Default value: 20, indicating that the task is startedevery 20s

Start Timefor FirstDetection

Start time for the first detection based on the importeddata source. The earliest detection start time can beset to the eighth day after the earliest start time of thetime series data.Default value: Jan 1, 2019 00:00:00

Percentage of lostdata

Data loss threshold. If the data loss exceeds thethreshold, no detection is performed.Default value: 20Only digits from 0 to 100 are supported.

Detectiontype

KPI threshold source.The values are as follows:● AI● Static Threshold● AI+Static ThresholdDefault value: AI

Numberofconsecutivereportedabnormalpoints

Thresholdfor theNumberofConsecutivelyReportedAbnormalPoints

If the number of consecutive abnormal values exceedsthe threshold, an alarm is reported.Default value: 3Only integers are supported.

Policy forreportingdiscontinuous

Jitterobservation window

Used to determine the length of the discontinuousexception window. For example, the length can be 50points. The default value is 50 (only integers aresupported).



Area Parameter

Description

abnormalpoints

Percentage ofDiscontinuouslyReportedAnomalies(%)

When the percentage of abnormal points exceeds thespecified value in the discontinuous exception window,an alarm is reported. The default value is 70 (onlydigits from 0 to 100 are supported).

Data lossreportingpolicy

Data lossreporting

Whether to report an alarm when data is missing.The values are as follows:● yes● noDefault value: yes

CommonParameters

Dataretentionperiod(days)

Retention period for the historical time series data. Thedata after the time obtained by substracting the dataretention period from the next detection time.Default value: empty, indicating that the data isretained for 30 days

Defaultdashboarddisplaytime (day)

Default dashboard waveform display period.Default value: 1 (day)

Step 2 Click Save to save the settings.

Save Successfully is displayed.

----End

3.6.2 Event ManagementIf a KPI anomaly is detected, related event information is displayed for analysis.For example, if the migration is performed between 22:00:00 August 11, 2019 to04:00:00 August 12, 2019, the network is expected to be abnormal during thisperiod. No special processing is required.

Adding Events

Step 1 On the menu bar, choose Advanced Settings > Event Management. The EventManagement page is displayed, as shown in Figure 3-10.

The event list and event information, such as the event ID, name, type, start time,and end time, are displayed.



Figure 3-10 Event management page

Step 2 Click Add. The Create Event dialog box is displayed, as shown in Figure 3-11.

Figure 3-11 Adding events

Table 3-3 describes the parameters.

Table 3-3 Adding events

Parameter

Description

EventID

Unique ID of an event

Eventname

Event nameExample: spring festival



Parameter

Description

Eventtype

Event typeThe values are as follows:● Gray upgrade● Common upgrade● Patch installation● Instantiation upgrade● Segmentation● Holiday● Social/Natural event

NE ID NE node

NEType

NE type

Starttime

Start time of an event

Endtime

End time of an event

Step 3 Click OK to add an event.

----End

Other Operations for Configuring Event Information● Quickly query an event by event ID, name, or type.● Select an event and click Delete to delete the event.● Select an event and click Modify to modify the event configurations.

3.6.3 Task ManagementBy default, a KPI anomaly detection task is started after KPI attributes areconfigured. For how to configure KPI attributes, see Configuring KPI Attributes.

You can view the list of KPI anomaly detection tasks on the Task Managementpage, as shown in Figure 3-12.



Figure 3-12 Task management page

The main fields in the task list are described as follows:● Task name: The value of this parameter is determined by KPI group

information.● Node ID: NE node● Node type: NE type● Detection KPI ID: The value includes all KPI IDs in a group.● Task status: status of a task, which can be Detecting, Not started,

Abnormal, or Discarded

Users can quickly query detection tasks by task name, task status, NE ID, or NEtype, and can stop or start a detection task in the Operation column of the tasklist.

3.7 Anomaly Dashboard

3.7.1 Visualized DashboardThe visualized dashboard uses a chart to display the trend of a specified KPI in aspecified period.

Viewing KPI Information in the Default View

For example, to view the KPI group10000001_USN_USN_1000_15,USN_kpi1_sample_ratio&10000002_USN_USN_1000_15,USN_kpi1_sample_attempts&10000003_USN_USN_1000_15,USN_kpi1_sample_failure in the default view, perform the following operations:

Step 1 On the menu bar, choose Anomaly Dashboard > Visualized Dashboard. TheVisualized Dashboard page is displayed.

On the top of the Visualized Dashboard page, you can view the KPI subscriptioninformation, as shown in Figure 3-13.

Figure 3-13 Subscription information



Step 2 In the navigation pane, choose EPC(multi) > USN > USN_1000 >10000001_USN_USN_1000_15,USN_kpi1_sample_ratio&10000002_USN_USN_1000_15,USN_kpi1_sample_attempts&10000003_USN_USN_1000_15,USN_kpi1_sample_failure.

Step 3 In the time range area on the right, set the detection time range, for example: Aug7, 2019 00:00:00 - Aug 30, 2019 00:00:00, and click OK. Retain the default refreshinterval, which is 30s.

Step 4 Click to view the corresponding KPI trend, as shown in Figure 3-14.

Figure 3-14 Visualized dashboard

----End

Adding Views

Step 1 On the menu bar, choose Anomaly Dashboard > Visualized Dashboard. TheVisualized Dashboard page is displayed.

Step 2 Click Add View in the upper right corner of the page. The Add View dialog box isdisplayed.

Step 3 Set the view name to USN0000145 and click OK.

The USN0000145 page is displayed.

Step 4 Click . The Create Chart dialog box is displayed, as shown in Figure3-15.

Set the following parameters:

● Chart name: Set this parameter to USN0000145.

● KPI Selection: Select KPI groups and click to add the selected groupsto the Selected KPIs area on the right.

● Chart Display Layout: Set this parameter based on the site requirements.



Figure 3-15 Creating a chart

Step 5 Click OK.

The USN0000145 chart is added. The USN0000145 page is displayed.

Step 6 In the right pane, set the time range to Aug 7, 2019 00:00:00 - Aug 30, 2019

00:00:00, click OK, and retain the default refresh interval, which is 30s. Click toview the KPI trend chart, as shown in Figure 3-16.

Figure 3-16 USN0000145 chart

----End

3.7.2 Anomaly ListThe anomaly list displays details about the detected KPI anomalies, as shown inFigure 3-17.

Users can quickly query anomaly detection results based on the anomaly ID,detection KPI ID, and anomaly status. Detection results can be exported inbatches.



Figure 3-17 KPI anomaly detection results

You can provide feedback on the anomaly results and delete anomaly records. Theprocedure is as follows:

Step 1 On the menu bar, choose Anomaly Dashboard > Anomaly List.

Step 2 On the Anomaly List page, click in the Operation column corresponding toan anomaly ID.

The anomaly result feedback page is displayed, as shown in Figure 3-18. Red partsin the chart indicate abnormal values.

Figure 3-18 Feeding back on anomaly results

Step 3 Check whether the value of this KPI is abnormal based on services on the livenetwork.

Select Hit or False alarm and enter the remarks.

Step 4 Click Feedback to submit the feedback.

Step 5 You can select the check box before an anomaly record and click Delete to deletethe anomaly record. Multiple records can be deleted at a time.



NO TE

If a detection KPI has been deleted, buttons in the Operation column are unavailable,including the feedback button.

----End


2020-03-30 Optimized the KPI anomaly detection model service UI andupdated all documents.

2019-12-30 Changed the service UI, optimized service functions, andupdated all documents.

2019-04-30 Released this document officially for the first time.



4 API Reference4.1 Environment Preparation

4.1.1 Obtaining Request AuthenticationThere are two authentication methods for interface invocation. You can use eitherof the methods to perform the authentication. AK/SK authentication isrecommended because it is more secure than token authentication.

Token authentication: Requests are authenticated using tokens.

Token AuthenticationIf you use a token for authentication, you must obtain the user's token and add X-Auth-Token to the request message header of the service API when invoking anAPI.

Step 1 Send POST https://IAM Endpoint/v3/auth/tokens to obtain the endpoint of IAMand the region name in the body. For details, see http://developer.huaweicloud.com/endpoint.html.

If the service area name is All, select the IAM cn-north-1 endpoint.

The following is a request example:

NO TE

Replace contents in italic in the sample codes with actual contents. For details, see theIdentity and Access Management API Reference.

{ "auth": { "identity": { "methods": [ "password" ], "password": { "user": { "name": "username", "password": "password", "domain": {

NAIEAbnormal KPI Detection Service 4 API Reference


http://developer.huaweicloud.com/endpoint.htmlhttp://developer.huaweicloud.com/endpoint.html

"name": "domainname" } } } }, "scope": { "project": { "name": "cn-north-1aaa" //Assume that the area name is cn-north-1aaa. } } } }

Step 2 Obtain the token. For details, see section "Obtaining User Token" in Identity andAccess Management API Reference. The value of X-Subject-Token in the responseheader is the token value.

Step 3 Invoke a service API, add X-Auth-Token to the message header, and set the valueof X-Auth-Token to the token obtained in step 2.

----End

4.1.2 Obtaining the Project ID and Tenant IDA project ID (project_id or tenant_id because both of them have the samemeaning in this document) is required for some URLs when invoking an API.Therefore, you need to obtain a project ID on the console before invoking an API.To obtain the project ID, perform the following steps:

1. Register and log in to the management console.2. Click the username and select Basic Information from the drop-down list.3. On the Basic Information page, click Manage.

On the My Credential page, view project IDs in the project list.

Figure 4-1 Viewing the project ID




2020-12-30 This is the first official release.



5 FAQs5.1 What Are the Service Scenarios of Time Series KPIAnomaly Detection

Anomaly detection is performed on time series data of KPIs in the O&M domainto detect anomalies, such as CPU usage and call completion rate anomalies. Thepurpose is to detect KPI anomalies based on the status changes of time seriesdata.

5.2 Can Time Series KPI Anomaly Detection Be Appliedto All Scenarios

Anomalies are classified into the following types:

● Data anomalies: We can detect data anomalies by checking whether thesample value is greatly different from existing values. For example, thenormal CPU usage is 1%. Through mathematical analysis, it is regarded as ananomaly if the CPU usage suddenly rises to 10%.

● Service anomalies: Data anomalies are different from service anomalies. Inthe preceding example, the 10% CPU usage is an anomaly in terms of math.However, the 10% CPU usage is normal in terms of services. Therefore, weneed to identify anomalies based on related service experience.

Currently, the time series KPI anomaly detection service supports data anomalydetection and user-defined service rules, so that the detection accuracy of serviceanomalies can be improved.

5.3 What Are the Types of Time Series KPI DataKPI curves are classified into the following types: periodic trend and non-periodictrend. Modeling is required for different trend types.

NAIEAbnormal KPI Detection Service 5 FAQs



2020-03-30 This issue does not include any changes.

2019-04-30 This is the first official release.

NAIEAbnormal KPI Detection Service 5 FAQs


6 GlossaryA

API

An Application Programming Interface (API) is a set of predefined functions usedby applications or developers to access a group of routines based on certainsoftware or hardware without the need to access the source code or understandthe internal mechanism.

KKPI

A key performance indicator (KPI) reflects the performance of an object at acertain time point. A KPI can be aggregated.

NAIEAbnormal KPI Detection Service 6 Glossary


Contents1 Documentation Guide2 Product Overview2.1 What Is KPI Anomaly Detection Model Service2.2 Application Scenarios2.3 Functions2.4 Benefits2.5 Restrictions2.6 Basic Concepts2.7 Service Dependencies2.8 Billing Description2.9 Accessing the KPI Anomaly Detection Model Service2.10 Change History

3 Quick Start3.1 Prerequisites3.2 Subscribing to the KPI Anomaly Detection Model Service3.3 Accessing the KPI Anomaly Detection Model Service3.4 Operation Process3.5 Detection Object Configuration3.5.1 Configuring KPI Attributes3.5.2 Importing Time Series Data

3.6 Advanced Settings3.6.1 Detection Policy3.6.2 Event Management3.6.3 Task Management

3.7 Anomaly Dashboard3.7.1 Visualized Dashboard3.7.2 Anomaly List

3.8 Change History

4 API Reference4.1 Environment Preparation4.1.1 Obtaining Request Authentication4.1.2 Obtaining the Project ID and Tenant ID

4.2 Change History

5 FAQs5.1 What Are the Service Scenarios of Time Series KPI Anomaly Detection5.2 Can Time Series KPI Anomaly Detection Be Applied to All Scenarios5.3 What Are the Types of Time Series KPI Data5.4 Change History

6 Glossary