发明名称 Proactive identification of hotspots in a cloud computing environment
摘要 The present invention proactively identifies hotspots in a cloud computing environment through cloud resource usage models that use workload parameters as inputs. In some embodiments the cloud resource usage models are based upon performance data from cloud resources and time series based workload trend models. Hotspots may occur and can be detected at any layer of the cloud computing environment, including the server, storage, and network level. In a typical embodiment, parameters for a workload are identified in the cloud computing environment and inputted into a cloud resource usage model. The model is run with the inputted workload parameters to identify potential hotspots, and resources are then provisioned for the workload so as to avoid these hotspots.
申请公布号 US9329908(B2) 申请公布日期 2016.05.03
申请号 US201012893302 申请日期 2010.09.29
申请人 International Business Machines Corporation 发明人 Gopisetty Sandeep;Murthy Seshashayee S.;Singh Aameek;Uttamchandani Sandeep M.;Weck David D.
分类号 G06F15/173;G06F9/50 主分类号 G06F15/173
代理机构 Keohane & D'Alessandro PLLC 代理人 Sharkan Noah A.;Barasch Maxine L.;Keohane & D'Alessandro PLLC
主权项 1. A method for proactively identifying hotspots in a particular cloud computing environment, comprising: identifying parameters associated with a workload, of a plurality of workloads, running in the particular cloud computing environment, the cloud computing environment comprising a plurality of resources; obtaining resource configuration, performance measures, and workload usage statistics information for the cloud computing environment; correlating server, storage, and network data to identify resources, of the plurality of resources, involved for each workload of the plurality of workloads; creating a regression based model based on the correlation of the server, storage, and network data; predicting an amount of load that the plurality of workloads will generate in the future through a time-series based workload trend model; forming a cloud resource usage model based on the workload trend model and the regression based model; providing the parameters to the cloud resource usage model; detecting, in response to a timed wake-up trigger, at least one potential hotspot in the particular cloud computing environment using the cloud resource usage model, the hotspot comprising one or more resources of the particular cloud computing environment, which become constrained such that at least one of application performance and throughput is limited; and provisioning at least one resource of the particular cloud computing environment for the workload in response to the detecting so as to minimize triggering the at least one potential hotspot; wherein the one or more resources comprises at least one of a compute node, a storage node, or a networking resource.
地址 Armonk NY US