Infrastructure as a Service (IaaS) clouds have become the predominant underlying infrastructure for the operation of modern and smart technology. IaaS clouds have proven to be useful for multiple reasons such as reduced costs, increased speed and efficiency, and better reliability and scalability. Compute services offered by such clouds are heterogeneous -- they offer a set of architecturally diverse machines that fit efficiently executing different workloads. However, there has been little study to shed light on the performance of popular application types on these heterogeneous compute servers across different clouds. Such a study can help organizations to optimally (in terms of cost, latency, throughput, consumed energy, carbon footprint, etc.) employ cloud compute services. At HPCC lab, we have focused on such benchmarks in different research projects and, in this report, we curate those benchmarks in a single document to help other researchers in the community using them. Specifically, we introduce our benchmarks datasets for three application types in three different domains, namely: Deep Neural Networks (DNN) Inference for industrial applications, Machine Learning (ML) Inference for assistive technology applications, and video transcoding for multimedia use cases.

翻译：基础设施即服务（IaaS）云已成为现代智能技术运行的主要底层基础设施。IaaS 云已被证明具有诸多优势，例如降低成本、提升速度与效率，以及更好的可靠性与可扩展性。此类云平台提供的计算服务具有异构性——它们提供一系列架构多样化的机器，能够高效执行不同的工作负载。然而，目前鲜有研究揭示不同云平台上这些异构计算服务器运行常见应用类型的性能表现。此类研究可帮助组织以最优方式（在成本、延迟、吞吐量、能耗、碳足迹等方面）利用云计算服务。在高性能计算与云计算实验室，我们已在多个研究项目中聚焦此类基准测试，并在本报告中将这些基准测试成果整理成单一文档，以协助领域内的其他研究者使用。具体而言，我们介绍了针对三个不同领域应用类型的基准测试数据集，即：面向工业应用的深度神经网络（DNN）推理、面向辅助技术应用的机器学习（ML）推理，以及面向多媒体用例的视频转码。