REPORT
Pepperdata Increases CPU Utilization by 157% When Compared to AWS Auto Scaling
The Pepperdata 2021 Benchmark Report demonstrates the efficacy of Pepperdata Capacity Optimizer compared to the AWS Custom Auto Scaling Policy. The benchmarking work in this report uses TPC-DS, an industry-standard big data benchmarking workload, and measures the following:
Key Findings:
This report highlights three groups of findings that demonstrate that Capacity Optimizer can automatically:
Optimize resource utilization.
Decrease instance duration.
Reduce costs by up to 50%.
Big Data Benchmarking in the Cloud
Benchmarking is the process of running a set of standard tests against some object to produce an assessment of that object’s relative performance. Imagine driving three different sports cars on the same course and measuring each car’s maximum speed, torque, and fuel consumption to compare the overall performance of the three cars.
This report covers our initial work with TPC-DS, the Decision Support framework from the Transaction Processing Performance Council. TPC-DS is a sophisticated, industry-standard big data analytics benchmark developed over decades and is a de facto standard for SQL including Hadoop. Our work is not an official audited benchmark as defined by TPC.
Resource Utilization Results
On average, Capacity Optimizer decreased both overall duration by 8% and instance hours by 38%, while increasing both CPU utilization by 157% and memory utilization by 38% when compared to AWS Custom Auto Scaling for the TPC-DS workload.
