In this study, the focus is on analyzing data-center workloads on AWS. The investigation encompasses both Intel and ARM architectures, with a detailed exploration of performance metrics. These metrics include thread count, memory bandwidth, and the number of vCPUs. The benchmarks used for this analysis are STREAM, Multiload, and Sysbench. This comprehensive examination provides valuable insights into the performance and efficiency of different architectures under various workloads.
The increasing popularity of cloud computing has introduced a new dimension to data-center workloads, with multiple cloud providers offering scalable and flexible resources for a wide range of applications. Furthermore, the landscape of data-center hardware has diversified with the emergence of different processor architectures, including those from Intel, ARM, AMD and more. To assess the effectiveness of these architectures and their associated workloads, various performance metrics and benchmarks are employed.
Benchmarks play a crucial role in comparing and measuring performance in data-center workloads. They provide standardized tests that enable fair and objective comparisons across different hardware and software configurations. By using benchmarks, we can assess the efficiency and effectiveness of various components and systems within a data center, including network performance, input/output (IO) capabilities, computational power, and memory usage. These task-specific benchmarks help us identify bottlenecks and optimize the overall performance of data-center workloads.
In this study, the focus is on analyzing data-center workloads on AWS. The investigation encompasses both Intel and ARM architectures, with a detailed exploration of performance metrics. These metrics include thread count, memory bandwidth, and the number of vCPUs. The benchmarks used for this analysis are STREAM, Multiload, and Sysbench. This comprehensive examination provides valuable insights into the performance and efficiency of different architectures under various workloads.
