Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Rajashekar Dasari

Sagi Vasavi

Hari Dhanalakoti

Ganesh Kolli

Srinivas Sanapala

Thavamaniraja Sakthivel

Sudheer Yetukuri

Raju Kalidindi

Reddy Challa Narsimha

Dhanalakoti Hari

Bezawada Suresh

Srivathsan P Polla

Examples disclosed herein relate to creation of a storage area network (SAN) zone based on a service level agreement (SLA). In an example, provisioning of a server in a cloud may be determined. Further, a Service Level Agreement (SLA) for the provisioned server may be determined. The SLA may define desired states of SAN configuration parameters to be met in configuring a SAN zone for the provisioned server. Network components and storage components in the cloud that meet the desired states of SAN configuration parameters defined in the SLA may be identified. A SAN zone for the provisioned server may be created based on the identified network components and storage components.

Creating a storage area network zone based on a service level agreement

A method for prioritizing data backup requests includes assigning priority values to a plurality of parameters, each parameter being associated with a plurality of data backup requests. The data backup requests can be sorted according to a primary parameter based on the priority values to prioritize the data backup requests. The data backup requests can also be sorted according to a secondary parameter when at least two of the data backup requests have a same primary parameter priority value.

Data backup prioritization

Scheduling of backup jobs is optimized in accordance with a characteristic of the backup jobs. The characteristic of the backup jobs can take into account a number of the backup jobs that are stale. A stale backup job is a backup job in which no backed up data changed during a most recent performance of the backup job.

Backup jobs scheduling optimization

Systems, computer readable media, and methods are provided. An example method can include classifying a plurality of storage mapping systems as a plurality of storage tiers in a datacenter, assigning a chargeback level to each of the plurality of storage tiers, analyzing a plurality of storage volumes of a plurality of servers in the datacenter to obtain characteristics of each of the plurality of storage volumes where the characteristics include one of the plurality of storage mapping systems, assigning the chargeback level to each of the plurality of storage volumes based on the storage mapping system for each of the plurality of storage volumes, and determining a storage recommendation for a number of configuration item (CIs) based on a criticality of the number of CIs where the criticality corresponds to at least one of the chargeback levels assigned to each of the plurality of storage tiers.

Chargeback based storage recommendations for datacenters

Methods, systems, and computer-readable media with executable instructions stored thereon for backup storage management are provided. A utilization threshold can be defined for each of a number of virtual tape libraries (VTLs). A number of slipped backup jobs can be identified, wherein each of the number of slipped backup jobs is associated with one of the number of VTLs. A number of storage statistics for each of the number of VTLs can be collected and storage can be allocated for each of the number of slipped backup jobs via a VTL backup storage manager that analyzes the utilization threshold, the number of slipped backup jobs, and the number of storage statistics for each of the number of VTLs.

Backup storage management

A non-transitory machine-readable storage device includes executable instructions that, when executed, cause one or more processors to calculate an effective application density for switches in fabrics. The processors are further caused to calculate an effective application density for the fabrics using the switch application densities. The processors are further caused to recommend deployment of an application on a first switch in a first fabric if the effective application density of the first switch is below a first threshold and the effective application density of the first fabric is below a second threshold.

Effective application densities for fabrics

Systems, methods, and computer-readable and executable instructions are provided for automatic cloud template approval. Automatic cloud template approval can include mapping a request to a chargeback package of a user, the request being for cloud service including a plurality of cloud service components. The automatic cloud template approval can include determining if the request is within the chargeback package of the user based on each of the plurality of cloud service components and, in response to the request being within the chargeback package of the user, automatically approving a cloud template to allocate the cloud service requested by the user. After approval resources will be allocated based on the availability, else resource analysis will be done and allocation will be done appropriately.

Automatic cloud template approval

In one embodiment, a virtualization analysis tool defines a classification weight value for each physical computing device from a plurality of physical computing devices, defines an effective utilization value for that physical computing device, and associates a virtual migration priority with that physical computing device based on the effective utilization value. The virtualization analysis tool defines the effective utilization value based on the classification weight value, a first utilization weight value, and a second utilization weight value. The first utilization weight value is associated with a first resource of that physical computing device, and the second utilization weight value associated with a second resource of that physical computing device. The virtual migration priority has a first value if the effective utilization value is within a first range of utilization weight values and a second value if the effective utilization value is within a second range of utilization weight values.

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Hyderabad Andhra Pradesh, India

Rajashekar Dasari