BEZNext Cloud Performance and Financial Governance Optimization
Cloud Data Platform Selection
Build performance, resource consumption and data usage profile for each business workload
Predict the minimum configuration and budget needed to meet Service Level Goals for each business workload on each of cloud data platform
Cloud migration decisions optimization
Loading data on time and within the budget
Optimize security decisions
Set realistic performance and financial expectations
Dynamic Capacity Management
Predict the impact of expected changes and set performance and financial expectations
Verify results, determine performance and financial anomalies and their root causes
Evaluate options and predict measures necessary to continuously meet SLGs for all workloads on all platforms with the lowest cost
Organize continuous close loop control
Our History
We’ve offered capacity management software and services for large data warehouses on Teradata, DB2, Oracle, and SQL Server for several decades
We added Big Data (Hadoop) support in 2014 and Cloud Data platforms support in 2017
The Need
How to reduce the uncertainty and risk of performance and financial surprises while making cloud migration and dynamic capacity management decisions in the Hybrid Multi-Cloud environments?
Illustration of BEZNext Iterative Modeling and Gradient Optimization determining the minimum configuration required to meet SLGs for all business workloads
Workload characterization and forecasting results, configuration and cost options of the cloud data platforms targets are the inputs to our modeling and optimization technology.
The iterative queueing network modeling and gradient optimization performs multi step automatic evaluation of options to predict the minimum configuration and budget needed to meet SLGs of each business workload
The BEZNext modeling and optimization technology has been proven on numerous projects across multiple industries.
Applying BEZNext Technology for Cloud Data Platform Selection
Predict the minimum configuration and budget needed to meet SLGs for each workload on each cloud data platform
Results of data collection, workload characterization and workload forecasting
Cloud data platforms architecture, configuration and pricing models
The security implication and tokenization overhead
Carbon footprint
Review our white paper: Which Cloud Data Platform is Best for Your Data Warehouse?
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 and SLG
Set up realistic performance and financial expectations Organize process of verification of results and development proactive recommendations
Verify results
Organizing Dynamic Capacity Management for Hybrid Multi-Cloud
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 performance and financial anomalies and root causes for each workload
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