{"id":939681,"date":"2023-05-08T12:02:33","date_gmt":"2023-05-08T19:02:33","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2025-05-18T23:21:33","modified_gmt":"2025-05-19T06:21:33","slug":"how-different-are-the-cloud-workloads-characterizing-large-scale-private-and-public-cloud-workloads","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/how-different-are-the-cloud-workloads-characterizing-large-scale-private-and-public-cloud-workloads\/","title":{"rendered":"How Different are the Cloud Workloads? Characterizing Large-Scale Private and Public Cloud Workloads"},"content":{"rendered":"<p><span dir=\"ltr\" role=\"presentation\">With the rapid development of cloud systems, an <\/span><span dir=\"ltr\" role=\"presentation\">increasing number of service workloads are deployed in the <\/span><span dir=\"ltr\" role=\"presentation\">private cloud and\/or public cloud. Although large cloud providers <\/span><span dir=\"ltr\" role=\"presentation\">such as Azure and Google have published workload traces in the <\/span><span dir=\"ltr\" role=\"presentation\">past, prior work has not focused on analyzing and characterizing <\/span><span dir=\"ltr\" role=\"presentation\">the differences between private and public cloud workloads in <\/span><span dir=\"ltr\" role=\"presentation\">detail. Based on our experience working with Azure, one of the <\/span><span dir=\"ltr\" role=\"presentation\">most widely used cloud platforms in the world, we find that the <\/span><span dir=\"ltr\" role=\"presentation\">workload characteristics are different between the private and<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">public cloud workloads. Specifically, compared with the public <\/span><span dir=\"ltr\" role=\"presentation\">cloud workloads, the private cloud workloads tend to be more <\/span><span dir=\"ltr\" role=\"presentation\">homogeneous in both deployment sizes and utilization patterns, <\/span><span dir=\"ltr\" role=\"presentation\">more static with occasional bursts in deployment characteristics, <\/span><span dir=\"ltr\" role=\"presentation\">and more region-agnostic regarding the sensitivity to deployed <\/span><span dir=\"ltr\" role=\"presentation\">regions.<\/span> <span dir=\"ltr\" role=\"presentation\">Our<\/span> <span dir=\"ltr\" role=\"presentation\">findings<\/span> <span dir=\"ltr\" role=\"presentation\">gain<\/span> <span dir=\"ltr\" role=\"presentation\">several<\/span> <span dir=\"ltr\" role=\"presentation\">insights<\/span> <span dir=\"ltr\" role=\"presentation\">and<\/span> <span dir=\"ltr\" role=\"presentation\">implications <\/span><span dir=\"ltr\" role=\"presentation\">on cloud management and motivate us to build a centralized <\/span><span dir=\"ltr\" role=\"presentation\">workload knowledge base.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>With the rapid development of cloud systems, an increasing number of service workloads are deployed in the private cloud and\/or public cloud. Although large cloud providers such as Azure and Google have published workload traces in the past, prior work has not focused on analyzing and characterizing the differences between private and public cloud workloads 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