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Workshop Outline

Observability automation in your Hybrid Multi-Cloud environment

Data collection

  • Sources of measurement data

  • Collection of data in production, DevOp and Benchmarking environments   

  • BEZNext methodology and software agents extracting measurement data

Workload characterization

  • Workload Aggregation

  • Workload Characterization

  • Workload Forecasting

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Problem determination tuning and development recommendations

  • Cost and performance anomaly detection

    • Determine the frequency and severity of performance and financial anomalies and root causes for each business workload

    • Cost and Performance Anomaly Detection

    • Identify the critical SQL queries as well as the most frequently accessed databases and tables

    • Analyze changes in the patterns of performance and resource utilization

    • Develop performance-tuning recommendations specific to your environment.

Use modeling to optimize cost and control performance in your environment

Applying modeling and optimization for appropriate cloud data platform selection for each business workload

  • BEZNext modeling basics, including the use of ChatGPT, Machine Learning algorithms, Iterative queueing network models, and gradient optimization

  • Evaluate the impact of expected changes, workload, and volume of data growth and new applications deployment

  • Select the appropriate cloud platform and set realistic performance and cost expectations for each business workload

  • Verify results

Applying modeling and optimization for cloud migration decisions optimization

  • Determine the minimum configuration and budget needed to meet SLGs for ETL process in a cloud

  • Predict the overhead of Tokenization/Detokenization in the cloud

Organizing dynamic capacity management in the hybrid multi-cloud agentic AI environment

  • Use cost and performance observability results for each application on each cloud platform

  • Apply modeling and optimization to determine the minimum configuration, resource allocation, and workload management changes needed to meet SLGs for all workloads

  • Set realistic performance and financial expectations for all business workloads on all platforms.

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Sizing new applications before deployment into the cloud

  • Use measurement data collected in the development and test environment

  • Apply modeling and optimization to determine the minimum configuration, resource allocation, and workload management changes needed to meet SLGs for new workload after deployment in the production cloud environment

  • Develop recommendations for developers and operations before new applications deployment

Results verification and organizing continuous cost and performance control

Build summary report with findings and recommendations

We will guide you in developing a cost performance and cost optimization report 

  • Each lecture of the workshop incorporates exercises to reinforce your learning experience.

  • After the workshop, we will guide you in generating a report highlighting key findings and generating performance and cost optimization recommendations.​

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For further inquiries or additional information, please don't hesitate to contact us at inquiry@beznext.com. Our team will be delighted to assist you.

  • Compare cost and performance measurement results with expected

  • Modify resource allocation and workload management results to continuously meet SLGs for all applications on all platforms

Taking Notes
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