Benchmarking Different Application Types across Heterogeneous Cloud Compute Services
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913643739742208 |
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| author | Duggi, Nivedhitha Rafiei, Masoud Salehi, Mohsen Amini |
| author_facet | Duggi, Nivedhitha Rafiei, Masoud Salehi, Mohsen Amini |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06128 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmarking Different Application Types across Heterogeneous Cloud Compute Services Duggi, Nivedhitha Rafiei, Masoud Salehi, Mohsen Amini Distributed, Parallel, and Cluster Computing 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. |
| title | Benchmarking Different Application Types across Heterogeneous Cloud Compute Services |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2501.06128 |