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.