Solutions
BEZNext offers software and services optimizing Hybrid Multi-Cloud FinOps decisions during DevOps and Dynamic Capacity Management
We are experts in Modeling and Performance Optimization
BEZNext offers Dynamic Capacity Management Performance Assurance software and services for Data Warehouse and Big Data applications working in Hybrid On Prem and Cloud environments. We are experts in modeling and performance optimization and our unique software incorporates ML, AI, Iterative Queueing Network Models and Gradient Optimization technology to assist customers with Cloud Selection and determining Dynamic Capacity Management actions necessary to meet applications Service Level Goals with lowest cost. We apply our technology during entire application life cycle, starting with Performance Engineering during DevOps for new applications, Performance and Workload Management in production environment and Strategic Capacity planning, helping clients compare options, predict cost and select the most appropriate Cloud and On Prem solutions.
BEZNext Dynamic Capacity Management and Performance Assurance Solutions
Our History
- We’ve offered software and services for decades to optimize cost and performance for large data warehouses on Teradata, DB2, Oracle, and SQL Server.
- We added Big Data (Hadoop) in 2014 and Cloud support in 2017
The Need
- Customers feel uncertainty about cost and performance while making strategic decisions about cloud selection and management of multi-cloud environments
Current Focus
- We enhanced our software and services to optimize cloud data platform selection decisions
- Typical project takes 2-3 weeks instead of 6-9 months POC studies
- We also offer DevOps, FinOps and dynamic capacity management software continuously optimizing hybrid multi-cloud decisions
- Our goal is to reduce the uncertainty and risk of performance and financial surprises
BEZNext Technology automates and optimizes cloud data platform selection and dynamic capacity management decisions
- Data collection from different cloud and on-prem platforms
- Data Transformation
- Workload aggregation by Line of business / business workloads
- Workload characterization
- Applying ML algorithms for performance and financial Anomaly detection and Root Cause determination
- Workloads pattern determination (seasonality)
- Applying iterative Queuing Network Modeling and Gradient Optimization for determining:
- The minimum configuration and budget required to meet SLGs for all workloads on different cloud data platforms
- Dynamic capacity management measures necessary to continuously support SLGs changing workloads, number of users, deployment of new workloads and increasing the volume of data
- Automatic verification of results by comparing performance and financial measurement data with expected
- Close loop feedback control through the model modification and development corrective recommendation
Typical Projects
- Optimization of FinOps and Dynamic Capacity Management in Hybrid Multi-Cloud environment
- Determination of Performance and Financial Anomalies and Root causes for on-prem and cloud data platforms
- Determination of the Pattern of performance and resource consumption by individual VWs
- Determining the minimum configuration and budget required to meet business performance goals during different hours of the day and different months of the year
- Automatic result verification by comparing the actual measurement data with expected (predicted)
- Analysis of the historical measurement data to determine the optimum size of VW to reduce cost.
- Optimization of DevOps decisions for Application Developers and Operations
Cost/Performance Optimization During Dev/Ops
Recommendations to Application Developers during DevOps process prior to new Application deployment
- Identify anomalies and root causes during testing of new applications prior to deployment
- Develop tuning recommendations
Recommendations to Operations during DevOps prior to deployment of new Application
Select cloud data platform for new application prior to deployment Predict the minimum configuration and budget required for deployment of the new application on different cloud data platforms, taking into consideration
- Expected increase in number of concurrent users and volume of data after deployment comparing with the test environment
- Response Time SLG
- Expected increase in number of users and volume of data during next 12 month
Set up realistic performance and financial expectations
Organize process of verification of results and development proactive recommendations
Predicted Minimum Configuration Required to provide satisfactory RT for new application after deployment on cloud data platform
Optimization FinOps decisions during Hybrid Multi-Cloud Dynamic Capacity Management
- Objective is to determine Performance and Financial Anomalies and Root Causes
- Allocate sufficient resources on each Cloud to meet SLGs for all business workloads with lowest cost
- Create realistic Budget
- Verify results
- Modify resource allocation and workload management rules to control performance and Cost
- BEZNext software optimizes the resource allocation and workload management rules to meet SLGs for each workload on each platform with the lowest cost
- We automate Data Collection, Workload Characterization and discovery of the most frequent and severe performance and financial anomalies and root causes on each platform for each workload, changes in pattern of resource utilization
- We apply iterative modeling and gradient performance optimization to compare options and develop proactive performance tuning, resource allocation and workload management recommendations
- Automatic results verification achieved by comparing the actual measurement data with expected / predicted results
- Feedback control enables continuous Dynamic Capacity Management
BEZNext Hands-On Workshop on Cloud Data Platform Selection and Dynamic Capacity Management for Hybrid Multi-Cloud Environment
Summary
- Cloud platform selection and dynamic capacity management of hybrid multi-cloud environments hands-on workshop
- Nine hours of instruction and hands-on learning delivered over three weeks (three hours per week).
Description
- Workshop includes instructor-led lectures, videos, quizzes and lab exercises
- The lab exercises will give students first-hand knowledge of the skills needed to effectively select and manage cloud platforms
- Participants will have access to BEZNext modeling and optimization software
- Participants will run and modify Python program to compare several ML algorithms of anomaly detection.
Workshop participants will learn how to:
- Collect measurement data on-prem and cloud platforms.
- Perform workload characterization
- Detect anomalies and their root causes
- Determine seasonality for each workload
- Predict the minimum configuration and cost required to meet SLAs for each workload on each platform
- Analyze Workload Patterns and Resource Management
- Organize continuous Dynamic Capacity Management to meet SLAs for each workload on each platform.
Prerequisites
- This workshop designed for participants who understand Cloud capabilities and have had exposure to Data Warehouse performance management.
Audience, Who should attend:
- This workshop is for managers, architect, analysts, DBAs, etc. who participate in Cloud Data platform selection and management of the Hybrid Multi-Cloud environments. For the complete program description, please download the workshop brochure here.