Cloud benchmarks aren’t enough: The use of modeling to address the limitations of benchmark tests
This white paper explores the value and constraints of industry-standard and customized benchmark tests in making informed cloud decisions. We present the BEZNext approach to overcoming these limitations by leveraging modeling and optimization technology. Our method adds value to results of the benchmark tests by evaluating options, optimizing cloud performance, reducing cost, and refining decisions related to platform selection, cloud migration, dynamic capacity management, DevOps choices, and carbon footprint estimation within a Hybrid Multi-Cloud environment.
Standard benchmarks like TPC-DS and TPC-H, along with custom benchmarks, help companies assess resource utilization and scalability of cloud data platforms. However, such benchmarks have limitations. Cloud platform selection based solely on benchmark tests doesn’t guarantee optimal real-world performance.
Relying solely on benchmark results for cloud decisions risks unexpected financial and performance challenges.
The BEZNext approach includes organizing the observability of the customer cloud environment, modeling and optimization to compare options, and assisting with cloud data platform selection, the migration of workloads to the cloud, dynamic capacity management of the Hybrid Multi-Cloud environment, DevOps, carbon footprint estimation, and, finally, continuous performance and cost control.
In this paper we will review the value and limitations of benchmarks and present BEZNext cloud performance optimization and cost control technology based on measurement data collected at the customer environment. We incorporate results of the benchmark tests done on different cloud platforms.