BEZNext Keeps You A Step Ahead

Than we use Iterative Modeling and Gradient Optimization to determine the type of instances, minimum number of instances which will be required in each Cloud to satisfy Service Level Goals for each of the growing workloads and increasing volume of data during night time, day time and evening time during the next 12 months and predict cost.

A typical Cloud selection project takes 2 - 4 weeks. It includes Data Collection, Workload Characterization, Modeling and Optimization and delivery report with findings and recommendations. BEZNext Modeling and Optimization solutions reduce uncertainty and risk of performance and financial surprises.

Value of our Cloud Selection Solutions

• Reduce Risk of Performance and Financial Surprises
• Reduce Uncertainty and Provide Realistic Expectations
• Significantly reduce time for POC process
• Enable verification by comparing actual results with expected/predicted

2. Help Customers Implement Performance Assurance of Production Environment

After selection of the Cloud platform we offer Dynamic Capacity Management solutions during entire Application Life Cycle:

Performance Engineering During DevOps Process

Our Performance Engineering Solutions Incorporating Modeling and Optimization complement results of Load and Stress testing during the DevOps process. It provides value to both Application Developers and Operations.

Value of our Performance Engineering Solutions for Application developers:

• Predict new applications implementation impact
• Predict how new applications will perform in production environment
• Identify Anomalies and their Root Causes during performance testing of new applications
• Develop performance tuning recommendations for Application Developers
• Predict how new applications will affect performance of existing production applications
• Develop proactive performance tuning, workload management, and capacity planning recommendations
• Set up realistic expectations

Value of our Performance Engineering Solutions for Operations

• Selection of appropriate Cloud platform for new application

• Develop Proactive Performance Management, Workload Management and Capacity Planning Recommendations

• Compare performance measurement results with expected

• Develop proactive performance tuning recommendations

Dynamic Capacity Management

• We automate Data Collection, Workload Characterization and discovery of the most frequent and severe anomalies for Data Warehouse and Big Data applications, determination of their root causes, changes in pattern of resource utilization to narrow down performance tuning and workload management optimization efforts.

• We apply iterative modeling and gradient performance optimization to compare options and develop proactive performance tuning, resource allocation and workload management recommendations.

• We recommend the most appropriate platforms and configurations required to meet SLGs for growing Data Warehouse and Big Data workloads in production On Prem and Cloud environments. It addresses the following challenges:

Value of Our Dynamic Capacity Management Optimization

• Automaton of problem prediction, most frequent and server Anomalies and Root Cause determination, Pattern change recognition, Analysis of Availability, and balance of resource utilization narrows down the scope of performance management and workload management optimization

• Alerting and active dashboards

• Modeling and optimization compare alternatives and develop proactive Performance Management and Workload Management Optimization recommendations.

• Automatic verification by comparing actual results with expected

Customers say our solutions reduce uncertainty, provide realistic expectations and lower the risk of performance and financial surprises. The performance and cost predictions enable verification of results by comparing the actual results with expected.