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      Getting to Know A-Tune

      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. The capability required varies in different scenarios. Therefore, capabilities need to be enabled or disabled depending on scenarios, and a combination of capabilities will maximize the optimal performance of applications.

      In addition, the actual business embraces hundreds and thousands of scenarios, and each scenario involves a wide variety of hardware configurations for computing, network, and storage. The lab cannot list all applications, business scenarios, and hardware combinations.

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

      A-Tune is an AI-based engine that optimizes system performance. It uses AI technologies to precisely profile business scenarios, discover and infer business characteristics, so as to make intelligent decisions, match with the optimal system parameter configuration combination, and give recommendations, ensuring the optimal business running status.

      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 feature engineering and two-layer classification model. The feature engineering is used to automatically select service features, and the two-layer classification model is used to learn and classify service models.
      • Interaction system layer: monitors and configures various system resources and executes optimization policies.

      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

      FeatureMaturityUsage Suggestion
      Auto optimization of 15 applications in 11 workload typesTestedPilot
      User-defined profile and service modelsTestedPilot
      Automatic parameter optimizationTestedPilot

      Supported Service Models

      Based on the workload characteristics of applications, A-Tune classifies services into 11 types. For details about the bottleneck of each type and the applications supported by A-Tune, see Table 2.

      Table 2 Supported workload types and applications

      Service CategoryTypeBottleneckSupported ApplicationPlanned Application
      defaultDefault typeLow resource usage in terms of cpu, memory, network, and I/ON/AN/A
      webserverWeb applicationCPU and networkNginx, Apache Traffic ServerN/A
      databaseDatabaseCPU, memory, and I/OMongodb, Mysql, Postgresql, MariadbN/A
      big-dataBig dataCPU and memoryN/AHadoop-hdfs, Hadoop-spark
      middlewareMiddleware frameworkCPU and networkDubboN/A
      in-memory-databaseIn-memory databaseMemory and I/ORedisN/A
      basic-test-suiteBasic test suiteCPU and memorySPECCPU2006, SPECjbb2015N/A
      hpcHuman genomeCPU, meomry, and I/OGatk4N/A
      storageStorageNetwork and I/ON/ACeph
      virtualizationVirtualizationCPU, meomry, and I/OConsumer-cloud, MariaDBN/A
      dockerContainerCPU, meomry, and I/OMariaDBN/A

      Bug Catching

      Buggy Content

      Bug Description

      Submit As Issue

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      Bug Type
      Specifications and Common Mistakes

      ● Misspellings or punctuation mistakes;

      ● Incorrect links, empty cells, or wrong formats;

      ● Chinese characters in English context;

      ● Minor inconsistencies between the UI and descriptions;

      ● Low writing fluency that does not affect understanding;

      ● Incorrect version numbers, including software package names and version numbers on the UI.

      Usability

      ● Incorrect or missing key steps;

      ● Missing prerequisites or precautions;

      ● Ambiguous figures, tables, or texts;

      ● Unclear logic, such as missing classifications, items, and steps.

      Correctness

      ● Technical principles, function descriptions, or specifications inconsistent with those of the software;

      ● Incorrect schematic or architecture diagrams;

      ● Incorrect commands or command parameters;

      ● Incorrect code;

      ● Commands inconsistent with the functions;

      ● Wrong screenshots.

      Risk Warnings

      ● Lack of risk warnings for operations that may damage the system or important data.

      Content Compliance

      ● Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions;

      ● Copyright infringement.

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      Bug Catching
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