Vmware, Inc.

Swarnalatha Pasupuleti

Dattathreya Sathyamurthy

Sivaraj M

Akhil Sadashiv Hingane

Gurusreekanth Chagalakondu

Sathyamurthy Dattathreya Hassan

Jeevan Prabhu

Kumar Gaurav

Chandrashekhar Jha

Ritesh Ranjan Jha

Yash Bhatnagar

Hemanth Kumar Pannem

Shrisha Chandrashekar

Soumya Panigrahi

Mrityunjoy Saha

Naveen Mudnal

Gurusreekanth C

Disclosed are various embodiments for distributed resource scheduling. An eviction request from a first host is received. The eviction request comprises data regarding a virtual machine to be migrated from the first host. The eviction request is then broadcast to a plurality of hosts. A plurality of responses are received from the plurality of hosts, each response comprising a score representing an ability of a respective one of the plurality of hosts to act as a new host for the virtual machine. A second host is selected from the plurality of hosts to act as the new host for the virtual machine based at least in part on the score in each of the plurality of responses. Then, a response is sent to the first host, the response containing an identifier of the second host.

Decentralized resource scheduling

Various examples are disclosed for generating a prediction of server requirements needed to deploy an application. The application can be deployed in virtualized environment in which virtual machines can execute the application. The predicted server requirements can be generated based upon data from other deployments of the application in other virtualized environments.

Generating predictive metrics for virtualized deployments

Techniques for optimizing resource claims for containers is described. In one example, resource utilization data associated with at least one container may be obtained for a period. A set of forecasting models may be trained based on the resource utilization data associated with a portion of the period. Resource utilization of the at least one container may be predicted for a remaining portion of the period using the set of trained forecasting models. The predicted resource utilization may be compared with the obtained resource utilization data for the remaining portion of the period. A forecasting model may be determined from the set of trained forecasting models based on the comparison to optimize resource claims for the at least one container.

Resource claim optimization for containers

Methods and systems are directed to allocating costs of storage arrays to logical disks. A fully loaded cost of storage of a storage area network ('SAN') is calculated based on capital and operational expenditures of the SAN. Data is stored in the SAN in logical groups partitioned into logical disks. The unit rates of the logical groups are calculated from the capacities and utilization of the storage arrays that form the SAN. The unit rate of a logical disk is the unit rate the logical group to which the logical disk belongs. The storage cost of each logical disk is calculated from the unit rate of the logical disk and the utilized capacity of the logical disk. Methods also calculate cost of unallocated capacities of the logical disks, storage arrays, and the SAN, in order to evaluate efficient use of the SAN storage resources.

Methods and systems to allocate physical data-storage costs to logical disks

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Bengaluru, India

Swarnalatha Pasupuleti