Hewlett Packard Enterprise Development LP

Smitha Jayaram

Suhas Shivanna

Anand Andaneppa Ganjihal

Sagar Venkappa Nyamagouda

Srinivas Krishnappa Shapur

Manoj Thankappan Varadamma

Rachit Gupta

Sherine Jacob

Hiro Rameshlal Lalwani

Nagaraju K N

Vijay Ballal

In some examples, a system receives workload information of a workload collection, and applies a machine learning model on the workload information, the machine learning model trained using training information including features of different types of workloads. The system produces, by the machine learning model, an identification of a first file system from among different types of file systems, the machine learning model producing an output value corresponding to the first file system that is a candidate for use in storing files of the workload collection.

File system provisioning for workload

Example implementations relate to application-specific policies for failing over from an edge site to a cloud. When an application becomes operational within an edge site, a discovery phase is performed by a local disaster recovery (DR) agent. I/O associated with a workload of the application is monitored. An I/O rate for data replication that satisfies latency characteristics of the application is predicted based on the incoming I/O. Based on results of tests against multiple clouds indicative of their respective RTO/RPO values, information regarding a selected cloud to serve as a secondary system is stored in an application-specific policy. The application-specific policy is transferred to a remote DR agent running in the selected cloud. Responsive to a failover event, infrastructure within a virtualized environment of the selected cloud is enabled to support a failover workload for the application based on the application-specific policy.

Application-specific policies for failover from an edge site to a cloud

Growing community of inventors

Bengaluru, India

Smitha Jayaram