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shanshishi authored 2020-01-11 12:24 . Documentation: optimize UserGuide

A-Tune User Guide

English | 简体中文

Legal Statement

Copyright © Huawei Technologies Co., Ltd. 2020. All rights reserved.

Your replication, use, modification, and distribution of this document are governed by the Creative Commons License Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0). You can visit https://creativecommons.org/licenses/by-sa/4.0/ to view a human-readable summary of (and not a substitute for) CC BY-SA 4.0. For the complete CC BY-SA 4.0, visit https://creativecommons.org/licenses/by-sa/4.0/legalcode.

Trademarks and Permissions

A-Tune 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 respective holders.

Disclaimer

This document is used only as a guide. Unless otherwise specified by applicable laws or agreed by both parties in written form, all statements, information, and recommendations in this document are provided "AS IS" without warranties, guarantees or representations of any kind, including but not limited to non-infringement, timeliness, and specific purposes.

Preface

Overview

This document describes how to install and use A-Tune, which is a performance self-optimization software for openEuler.

Intended Audience

This document is intended for developers, open-source enthusiasts, and partners who use the openEuler system and want to know and use A-Tune. You need to have basic knowledge of the Linux OS.

1 Getting to Know A-Tune

1.1 Introduction

An operating system (OS) is basic software that connects applications and hardware. It is critical for users to adjust OS and application configurations and make full use of software and hardware capabilities to achieve optimal service performance. However, numerous workload types and varied applications run on the OS, and the requirements on resources are different. Currently, the application environment composed of hardware and software involves more than 7000 configuration objects. As the service complexity and optimization objects increase, the time cost for optimization increases exponentially. As a result, optimization efficiency decreases sharply. Optimization becomes complex and brings great challenges to users.

Second, as infrastructure software, the OS provides a large number of software and hardware management capabilities. Each capability applies to different scenarios. Therefore, different capabilities need to be enabled or disabled in different scenarios to combine various capabilities provided by the system, maximizing the optimal performance of applications.

In addition, thousands of actual service scenarios exist, and hardware configurations for computing, network, and storage emerge. The lab cannot list all applications, service scenarios, and different hardware combinations.

To address the preceding challenges, openEuler launches A-Tune.

A-Tune is AI-based software that optimizes system performance. It uses AI technologies to create precise system profiles for service scenarios, aware and infer service characteristics, make intelligent decisions, and match and recommend the optimal system parameter configuration combination, ensuring the optimal running status of services.

1.2 Architecture

The following figure shows the A-Tune core technical architecture, which consists of intelligent decision-making, system profile, and interaction system.

  • Intelligent decision-making layer: consists of the awareness and decision-making subsystems, which implements intelligent awareness of applications and system optimization decision-making, respectively.
  • System profile layer: consists of the labeling and learning subsystems. The labeling subsystem is used to cluster service models, and the learning subsystem is used to learn and classify service models.
  • Interaction system layer: monitors and configures various system resources and executes optimization policies.

1.3 Supported Features and Service Models

Supported Features

Table 1 describes the main features supported by A-Tune, feature maturity, and usage suggestions.

Table 1 Feature maturity

Feature

Maturity

Usage Suggestion

Auto optimization of 11 applications in seven workload types

Tested

Pilot

User-defined workload types and service models

Tested

Pilot

Automatic parameter optimization

Tested

Pilot

Supported Service Models

Based on the workload characteristics of applications, A-Tune classifies services into seven types. For details about the workload characteristics of each type and the applications supported by A-Tune, see Table 2.

Table 2 Supported workload types and applications

Workload Type

Description

Workload Characteristic

Supported Application

default

Default type

The usage of CPU, memory bandwidth, network, and I/O resources is low.

N/A

webserver

HTTPS application

The CPU usage is high.

Nginx

big_database

Database

  • Relational database

    Read: The usage of CPU, memory bandwidth, and network is high.

    Write: The usage of I/O is high.

  • Non-relational database

    The usage of CPU and I/O is high.

MongoDB, MySQL, PostgreSQL, and MariaDB

big_data

Big data

The usage of CPU and I/O is high.

Hadoop and Spark

in-memory_computing

Memory-intensive application

The usage of CPU and memory bandwidth is high.

SPECjbb2015

in-memory_database

Computing- and network-intensive application

The usage of a single-core CPU is high, and the network usage is high in multi-instance scenarios.

Redis

single_computer_intensive_jobs

Computing-intensive application

The usage of a single-core CPU is high, and the usage of memory bandwidth of some subitems is high.

SPECCPU2006

communication

Network-intensive application

The usage of CPU and network is high.

Dubbo

idle

System in idle state

The system is in idle state and no applications are running.

N/A

2 Installation and Deployment

This chapter describes how to install and deploy A-Tune.

2.1 Software and Hardware Requirements

Hardware Requirement

  • Huawei Kunpeng 920 processor

Software Requirement

  • OS: openEuler 1.0

2.2 Environment Preparation

Install an openEuler OS. For details, see openEuler 1.0 Installation Guide.

2.3 A-Tune Installation

This chapter describes the installation modes and methods of the A-Tune.

2.3.1 Installation Modes

A-Tune can be installed in single-node or distributed mode.

  • Single-node mode

    The client and server are installed on the same system.

  • Distributed mode

    The client and server are installed on different systems.

2.3.2 Installation Procedure

To install the A-Tune, perform the following steps:

  1. Mount an openEuler ISO file.

    # mount openEuler-1.0-aarch64-dvd.iso /mnt
  2. Configure the local yum source.

    # vim /etc/yum.repos.d/local.repo

    The configured contents are as follows:

    [local]
    name=local
    baseurl=file:///mnt
    gpgcheck=0
    enabled=1
  3. Install an A-Tune server.

    NOTE:
    In this step, both the server and client software packages are installed. For the single-node deployment, skip Step 4.

    # yum install atune -y
  4. Install an A-Tune client.

    # yum install atune-client -y
  5. Check whether the installation is successful.

    # rpm -qa | grep atune
    atune-client-xxx
    atune-db-xxx
    atune-xxx

    If the preceding information is displayed, the installation is successful.

2.4 A-Tune Deployment

This chapter describes how to deploy A-Tune.

2.4.1 Overview

The configuration items in the A-Tune configuration file /etc/atuned/atuned.cnf are described as follows:

  • A-Tune service startup configuration

    You can modify the parameter value as required.

    • address: Listening IP address of the gRPC server. The default value is 127.0.0.1. Modify the value for distributed deployment.
    • port: Listening port of the gRPC server. The value ranges from 0 to 65535. The port is not in use.
    • rest_port: Listening port of the system REST service. The value ranges from 0 to 65535. The port is not in use.
    • sample_num: Number of samples collected when the system executes the analysis process.
  • System information

    System is the parameter information required for system optimization. You must modify the parameter information according to the actual situation.

    • disk: Disk information to be collected during the analysis process or specified disk during disk optimization.

    • network: NIC information to be collected during the analysis process or specified NIC during NIC optimization.

    • user: User name used for ulimit optimization. Currently, only the user root is supported.

    • tls: SSL/TLS certificate verification for the gRPC and HTTP services of A-Tune. This is disabled by default. After TLS is enabled, you need to set the following environment variables before running the atune-adm command to communicate with the server:

      • export ATUNE_TLS=yes
      • export ATUNE_CLICERT=<Client certificate path>
    • tlsservercertfile: path of the gPRC server certificate.

    • tlsserverkeyfile: gPRC server key path.

    • tlshttpcertfile: HTTP server certificate path.

    • tlshttpkeyfile: HTTP server key path.

    • tlshttpcacertfile: CA certificate path of the HTTP server.

  • Log information

    Change the log path and level based on the site requirements. By default, the log information is stored in /var/log/message.

  • Monitor information

    The hardware information that is collected by default when the system is started.

Example

#################################### server ###############################
# atuned config
[server]
# the address that the grpc server to bind to, default is 127.0.0.1
address = 127.0.0.1

# the atuned grpc listening port, default is 60001
# the port can be set between 0 to 65535 which not be used
port = 60001

# the rest service listening port, default is 8383
# the port can be set between 0 to 65535 which not be used
rest_port = 8383

# when run analysis command, the numbers of collected data.
# default is 20
sample_num = 20

# enable gRPC and http server authentication SSL/TLS
# default is false
# tls = true
# tlsservercertfile = /etc/atuned/server.pem
# tlsserverkeyfile = /etc/atuned/server.key
# tlshttpcertfile = /etc/atuned/http/server.pem
# tlshttpkeyfile = /etc/atuned/http/server.key
# tlshttpcacertfile = /etc/atuned/http/cacert.pem

#################################### log ###############################
# either "debug", "info", "warn", "error", "critical", default is "info"
level = info

#################################### monitor ###############################
[monitor]
# with the module and format of the MPI, the format is {module}_{purpose}
# the module is Either "mem", "net", "cpu", "storage"
# the purpose is "topo"
module = mem_topo, cpu_topo

#################################### system ###############################
# you can add arbitrary key-value here, just like key = value
# you can use the key in the profile
[system]
# the disk to be analysis
disk = sda

# the network to be analysis
network = enp189s0f0

user = root

2.5 Starting A-Tune

After the A-Tune is installed, you need to start the A-Tune service.

  • Start the atuned service.

    $ systemctl start atuned
  • To query the status of the atuned service, run the following command:

    $ systemctl status atuned

    If the following information is displayed, the service is started successfully:

3 Application Scenarios

You can invoke the command line interface (CLI) provided by A-Tune to use A-Tune functions. This chapter describes the functions and usage of the A-Tune CLI.

3.1 Querying Workload Types

list

Function

Query the supported workload types, profiles, and the values of Active.

Format

atune-adm list

Example

$ atune-adm list

Support WorkloadTypes:
+-----------------------------------+------------------------+-----------+
| WorkloadType                      | ProfileName            | Active    |
+===================================+========================+===========+
| default                           | default                | true      |
+-----------------------------------+------------------------+-----------+
| webserver                         | ssl_webserver          | false     |
+-----------------------------------+------------------------+-----------+
| big_database                      | database               | false     |
+-----------------------------------+------------------------+-----------+
| big_data                          | big_data               | false     |
+-----------------------------------+------------------------+-----------+
| in-memory_computing               | in-memory_computing    | false     |
+-----------------------------------+------------------------+-----------+
| in-memory_database                | in-memory_database     | false     |
+-----------------------------------+------------------------+-----------+
| single_computer_intensive_jobs    | compute-intensive      | false     |
+-----------------------------------+------------------------+-----------+
| communication                     | rpc_communication      | false     |
+-----------------------------------+------------------------+-----------+
| idle                              | default                | false     |
+-----------------------------------+------------------------+-----------+

NOTE:
If the value of Active is true, the profile is activated. In the example, the profile of the default type is activated.

3.2 User-defined Workload Types

In addition to the workload types defined in the system, A-Tune also supports user-defined workload types and corresponding profiles, and you can update or delete these workload types.

You can also add the user-defined model to A-Tune. For details about how to train a model, see User-defined Model.

define

Function

Add a user-defined workload type and the corresponding profile optimization item.

Format

atune-adm define <WORKLOAD_TYPE> <PROFILE_NAME> <PROFILE_PATH>

Example

Add a workload type. Set workload type to test_type, profile name to test_name, and configuration file of an optimization item to example.conf.

$ atune-adm define test_type test_name ./example.conf

The example.conf file can be written as follows (the following optimization items are optional and are for reference only). You can also run the atune-adm info command to view how the existing profile is written.

[main]
# list it's parent profile
[tip]
# the recommended optimization, which should be performed manunaly
[check]
# check the environment
[affinity.irq]
# to change the affinity of irqs
[affinity.task]
# to change the affinity of tasks
[bios]
# to change the bios config
[bootloader.grub2]
# to change the grub2 config
[kernel_config]
# to change the kernel config
[script]
# the script extention of cpi
[sysctl]
# to change the /proc/sys/* config
[sysfs]
# to change the /sys/* config
[systemctl]
# to change the system service config
[ulimit]
# to change the resources limit of user

update

Function

Update an optimization item of a workload type to the content in the new.conf file.

Format

atune-adm update <WORKLOAD_TYPE> <PROFILE_NAME> <PROFILE_FILE>

Example

Update the workload type to test_type and the optimization item of test_name to new.conf.

$ atune-adm update test_type test_name ./new.conf

undefine

Function

Delete a user-defined workload type.

Format

atune-adm undefine <WORKLOAD_TYPE>

Example

Delete the test_type workload type.

$ atune-adm undefine test_type 

3.3 User-defined Model

You can train a new workload type model by running the collection and train commands.

collection

Function

Collect the global resource usage and OS status information during service running, and save the collected information to a CSV output file as the input dataset for model training.

NOTE:
This command depends on the sampling tools such as perf, mpstat, vmstat, iostat, and sar. Currently, only the Kunpeng 920 CPU is supported. You can run the dmidecode -t processor command to check the CPU model.

Format

atune-adm collection <OPTIONS>

Parameter Description

  • OPTIONS

    Parameter

    Description

    --filename, -f

    Name of the generated CSV file used for training: name-timestamp.csv

    --output_path, -o

    Path for storing the generated CSV file. The absolute path is required.

    --disk, -b

    Disk used during service running, for example, /dev/sda.

    --network, -n

    Network port used during service running, for example, eth0.

    --workload_type, -t

    Workload type, which is used as a label for subsequent training.

    --duration, -d

    Data collection time during service running, in seconds. The default collection time is 1200 seconds.

    --interval, -i

    Interval for collecting data, in seconds. The default interval is 5 seconds.

Example

$ atune-adm collection --filename name --interval 5 --duration 1200 --output_path /home --disk sda --network eth0 --workload_type test_type 

train

Function

Use the collected data to train the model. Collect data of at least two workload types during training. Otherwise, an error is reported.

Format

atune-adm train <OPTIONS>

Parameter Description

  • OPTIONS

    Parameter

    Description

    --data_path, -d

    Path for storing CSV files required for model training

    --output_file, -o

    Model generated through training

Example

Use the CSV file in the data directory as the training input. The generated model new-model.m is stored in the model directory.

$ atune-adm train --data_path ./data --output_file ./model/new-model.m 

3.4 Workload Type Analysis and Auto Optimization

analysis

Function

Collect real-time statistics from the system to identify and automatically optimize workload types.

Format

atune-adm analysis [OPTIONS]

Parameter Description

Parameter

Description

--model, -m

Model generated by user-defined training

Example

  • Use the default model for classification and identification.

    $ atune-adm analysis
  • Use the user-defined training model for recognition.

    $ atune-adm analysis --model ./model/new-model.m

3.5 Querying Profiles

info

Function

View the profile content of a workload type.

Format

atune-adm info <WORKLOAD_TYPE>

Example

View the profile content of webserver.

$ atune-adm info webserver

*** ssl_webserver:

#
# webserver tuned configuration
#
[main]
#TODO CONFIG

[kernel_config]
#TODO CONFIG

[bios]
#TODO CONFIG

[sysfs]
#TODO CONFIG

[sysctl]
fs.file-max=6553600
fs.suid_dumpable = 1
fs.aio-max-nr = 1048576
kernel.shmmax = 68719476736
kernel.shmall = 4294967296
kernel.shmmni = 4096
kernel.sem = 250 32000 100 128
net.ipv4.tcp_tw_reuse = 1
net.ipv4.tcp_syncookies = 1
net.ipv4.ip_local_port_range = 1024     65500
net.ipv4.tcp_max_tw_buckets = 5000
net.core.somaxconn = 65535
net.core.netdev_max_backlog = 262144
net.ipv4.tcp_max_orphans = 262144
net.ipv4.tcp_max_syn_backlog = 262144
net.ipv4.tcp_timestamps = 0
net.ipv4.tcp_synack_retries = 1
net.ipv4.tcp_syn_retries = 1
net.ipv4.tcp_fin_timeout = 1
net.ipv4.tcp_keepalive_time = 60
net.ipv4.tcp_mem =  362619      483495   725238
net.ipv4.tcp_rmem = 4096         87380   6291456
net.ipv4.tcp_wmem = 4096         16384   4194304
net.core.wmem_default = 8388608
net.core.rmem_default = 8388608
net.core.rmem_max = 16777216
net.core.wmem_max = 16777216

[systemctl]
sysmonitor=stop
irqbalance=stop

[bootloader.grub2]
selinux=0
iommu.passthrough=1

[tip]
bind your master process to the CPU near the network = affinity
bind your network interrupt to the CPU that has this network = affinity
relogin into the system to enable limits setting = OS

[script]
openssl_hpre = 0
prefetch = off

[ulimit]
{user}.hard.nofile = 102400
{user}.soft.nofile = 102400

[affinity.task]
#TODO CONFIG

[affinity.irq]
#TODO CONFIG

[check]
#TODO CONFIG

3.6 Activing Profiles

profile

Function

Manually activate a profile of a workload type.

Format

atune-adm profile <WORKLOAD_TYPE>

Parameter Description

You can run the list command to query the supported workload types.

Example

Activate the profile configuration of webserver.

$ atune-adm profile webserver

3.7 Rolling Back Profiles

rollback

Function

Roll back the current configuration to the initial configuration of the system.

Format

atune-adm rollback

Example

$ atune-adm rollback

3.8 Updating Database

upgrade

Function

Update the system database.

Format

atune-adm upgrade <DB_FILE>

Example

The database is updated to new_sqlite.db.

$ atune-adm upgrade ./new_sqlite.db

3.9 Querying System Information

check

Function

Check the CPU, BIOS, OS, and NIC information.

Format

atune-adm check

Example

$ atune-adm check
 cpu information:
     cpu:0   version: Kunpeng 920-6426  speed: 2600000000 HZ   cores: 64
     cpu:1   version: Kunpeng 920-6426  speed: 2600000000 HZ   cores: 64
 system information:
     DMIBIOSVersion: 0.59
     OSRelease: 4.19.36-vhulk1906.3.0.h356.eulerosv2r8.aarch64
 network information:
     name: eth0              product: HNS GE/10GE/25GE RDMA Network Controller
     name: eth1              product: HNS GE/10GE/25GE Network Controller
     name: eth2              product: HNS GE/10GE/25GE RDMA Network Controller
     name: eth3              product: HNS GE/10GE/25GE Network Controller
     name: eth4              product: HNS GE/10GE/25GE RDMA Network Controller
     name: eth5              product: HNS GE/10GE/25GE Network Controller
     name: eth6              product: HNS GE/10GE/25GE RDMA Network Controller
     name: eth7              product: HNS GE/10GE/25GE Network Controller
     name: docker0           product:

3.10 Automatic Parameter Optimization

A-Tune provides the automatic search capability for optimal configurations, eliminating the need for repeated manual parameter adjustment and performance evaluation. This greatly improves the search efficiency of optimal configurations.

tuning

Function

Use the specified project file to search the dynamic space for parameters to find the optimal solution under the current environment configuration.

Format

atune-adm tuning [OPTIONS] <PROJECT_YAML>

NOTE:
Before running the command, ensure that the following conditions are met:

  1. The YAML configuration file of the server has been edited and placed in the /etc/atuned/tuning/ directory on the server by the server administrator.
  2. The YAML configuration file of the client has been edited and placed in any directory on the client.

Parameter Description

  • PROJECT_YAML

YAML configuration file of the client.

  • OPTIONS

Parameter

Description

--restore, -r

restore pre-optimized initial configuration.

--project value, -p value

The project name of the yaml file.

Configuration Description

The configuration items of a YAML file on a server are as follows:

Name

Description

Type

Value Range

project

Project name.

Character string

-

startworkload

Startup script of the service to be optimized.

Character string

-

stopworkload

Stopping script of the service to be optimized.

Character string

-

maxiterations

Maximum number of optimization iterations, which is used to limit the number of iterations on the client.

Integer

>=10

object

Parameters to be optimized and related information.

-

-

Table 1 Description of object configuration item

Name

Description

Type

Value Range

name

Parameter to be optimized.

Character string

-

desc

Description of parameters to be optimized.

Character string

-

get

Script for querying parameter values.

-

-

set

Script for setting parameter values.

-

-

needrestart

Specifies whether to restart the service for the parameter to take effect.

Enumeration

"true", "false"

type

Parameter type. Currently, the discrete and continuous types are supported.

Enumeration

"discrete", "continuous"

dtype

Parameter value type when type is set to discrete. Currently, int and string are supported.

Enumeration

int, string

scope

Parameter value range, which is used when dtype is set to int.

Integer

The value is user-defined and must be within the valid range of this parameter.

step

Parameter value step, which is used when dtype is set to int.

Integer

This value is user-defined.

items

Enumerated value of which the parameter value is not within the selected range. This is used when dtype is set to int.

Integer

The value is user-defined and must be within the valid range of this parameter.

options

Enumerated value range of the parameter value, which is used when dtype is set to string.

Character string

The value is user-defined and must be within the valid range of this parameter.

ref

Recommended initial value of the parameter

Integer or character string

The value is user-defined and must be within the valid range of this parameter.

The configuration items of a YAML file on a client are as follows:

Name

Description

Type

Value Range

project

Project name, which must be the same as that in the configuration file on the server.

Character string

-

iterations

Number of optimization iterations.

Integer

≥ 10

benchmark

Performance test script.

-

-

evaluations

Performance test evaluation index.

-

-

Table 2 Description of evaluations configuration item

Name

Description

Type

Value Range

name

Evaluation index name.

Character string

-

get

Script for obtaining performance evaluation results.

-

-

type

Specifies a positive or negative type of the evaluation result. The value positive indicates that the performance value is minimized, and the value negative indicates that the performance value is maximized.

Enumeration

"positive","negative"

weight

Weight of the index. The value ranges from 0 to 100.

Integer

0-100

threshold

Minimum performance requirement of the index.

Integer

User-specified

Configuration Example

The following is an example of the YAML file configuration on a server:

project: "example"
maxiterations: 10
startworkload: ""
stopworkload: ""
object :
  -
    name : "vm.swappiness"
    info :
        desc : "the vm.swappiness"
        get : "sysctl -a | grep vm.swappiness"
        set : "sysctl -w vm.swappiness=$value"
        needrestart: "false"
        type : "continuous"
        scope :
          - 0
          - 10
        ref : 1
  -
    name : "irqbalance"
    info :
        desc : "system irqbalance"
        get : "systemctl status irqbalance"
        set : "systemctl $value sysmonitor;systemctl $value irqbalance"
        needrestart: "false"
        type : "discrete"
        options:
          - "start"
          - "stop"
        dtype : "string"
        ref : "start"
  -
    name : "net.tcp_min_tso_segs"
    info :
        desc : "the minimum tso number"
        get : "cat /proc/sys/net/ipv4/tcp_min_tso_segs"
        set : "echo $value > /proc/sys/net/ipv4/tcp_min_tso_segs"
        needrestart: "false"
        type : "continuous"
        scope:
          - 1
          - 16
        ref : 2
  -
    name : "prefetcher"
    info :
        desc : ""
        get : "cat /sys/class/misc/prefetch/policy"
        set : "echo $value > /sys/class/misc/prefetch/policy"
        needrestart: "false"
        type : "discrete"
        options:
          - "0"
          - "15"
        dtype : "string"
        ref : "15"
  -
    name : "kernel.sched_min_granularity_ns"
    info :
        desc : "Minimal preemption granularity for CPU-bound tasks"
        get : "sysctl kernel.sched_min_granularity_ns"
        set : "sysctl -w kernel.sched_min_granularity_ns=$value"
        needrestart: "false"
        type : "continuous"
        scope:
          - 5000000
          - 50000000
        ref : 10000000
  -
    name : "kernel.sched_latency_ns"
    info :
        desc : ""
        get : "sysctl kernel.sched_latency_ns"
        set : "sysctl -w kernel.sched_latency_ns=$value"
        needrestart: "false"
        type : "continuous"
        scope:
          - 10000000
          - 100000000
        ref : 16000000

The following is an example of the YAML file configuration on a client:

project: "example"
iterations : 10
benchmark : "sh /home/Benchmarks/mysql/tunning_mysql.sh"
evaluations :
  -
    name: "tps"
    info:
        get: "echo -e '$out' |grep 'transactions:' |awk '{print $3}' | cut -c 2-"
        type: "negative"
        weight: 100
        threshold: 100

Example

  • Tuning
$ atune-adm tuning example-client.yaml
  • Restore the initial configuration before tuning
$ atune-adm tuning --restore --project example

4 Appendixes

Acronyms and Abbreviations

Table 1 Terminology

Term

Description

workload_type

Workload type, which is used to identify a type of service with the same characteristics.

profile

Set of optimization items and optimal parameter configuration.

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