Rashmi R Gupta, 716986 Mimosa Dr, Carlsbad, CA 92011

2021022, Jul 22, 2021

Jul 30, 2020

16/942929

- San Jose CA, US
Hemanth Kumar Thummala - San Jose CA, US
Mausumi Ranasingh - San Jose CA, US
Rashmi Gupta - San Jose CA, US
Satyajit Deshmukh - Santa Clara CA, US
Shyamsunder Prayagchand Rathi - Sunnyvale CA, US

Nutanix, Inc. - San Jose CA

G06F 16/188
G06F 16/16
G06F 16/17
G06F 21/62
G06F 21/31
G06F 9/455

A virtualized file server (VFS) may include a plurality of file server virtual machines (FSVMs) that cooperatively manage a distributed file share of storage items. A data protocol server implemented by a first FSVM is configured to provide communication between a user device and the VFS responsive to a request from the user device to connect to the VFS. A common layer implemented by the first FSVM includes information management metadata for authorized users of the VFS. A web proxy at the first FSVM presents a user access list of storage items to the user device, where the user access list is based on user information received with the request from the user device to connect to the VFS.

2018015, Jun 7, 2018

Dec 1, 2017

15/829781

- San Jose CA, US
Manoj Naik - San Jose CA, US
Rashmi Gupta - Milpitas CA, US
Veerraju Tammineedi - San Jose CA, US
Yifeng Huang - Mountain View CA, US
Shyamsunder Prayagchand Rathi - Sunnyvale CA, US
Sravan Kumar Muthyala - Bangalore, IN
Mausumi Ranasingh - San Jose CA, US
Hemanth Kumar Thummala - San Jose CA, US
Manish Gupta - Bangalore, IN
Saji Kumar Vijaya Kumari Rajendran Nair - Cupertino CA, US

Nutanix, Inc. - San Jose CA

G06F 9/455
G06F 17/30

Examples described herein include virtualized file servers which may include load balancing. For example, a recommendation engine may estimate a load associated with each of a plurality of file server virtual machines in a cluster. Based on the load, the recommendation engine may recommend changing ownership of one or more volume groups, scaling up the virtualized file server, scaling down the virtualized file server, scaling in the virtualized file server, scaling out the virtualized file server, or combinations thereof.

2017023, Aug 17, 2017

Feb 1, 2017

15/422220

- San Jose CA, US
Kalpesh Ashok Bafna - Milpitas CA, US
Durga Mahesh Arikatla - San Jose CA, US
Shyamsunder Prayagchand Rathi - Sunnyvale CA, US
Satyajit Sanjeev Deshmukh - Milpitas CA, US
Vishal Sinha - Union City CA, US
Rashmi Gupta - Milpitas CA, US
Rishabh Sharma - Santa Clara CA, US
Yifeng Huang - Mountain View CA, US

G06F 17/30
G06F 9/455

In one embodiment, a system for managing communication connections in a virtualization environment includes a plurality of host machines implementing a virtualization environment, wherein each of the host machines includes a hypervisor, at least one user virtual machine (user VM), and a distributed file server that includes file server virtual machines (FSVMs) and associated local storage devices. Each FSVM and associated local storage device are local to a corresponding one of the host machines, and the FSVMs conduct I/O transactions with their associated local storage devices based on I/O requests received from the user VMs. Each of the user VMs on each host machine sends each of its respective I/O requests to an FSVM that is selected by one or more of the FSVMs for each I/O request based on a lookup table that maps a storage item referenced by the I/O request to the selected one of the FSVMs.

2016037, Dec 29, 2016

Jun 29, 2015

14/753341

Preetdeep KUMAR - Bhola Babu Colony, IN
Rashmi GUPTA - Sunnyvale CA, US
Shweta TIWARI - Vadodara, IN
Steven L. GREENSPAN - Scotch Plains NJ, US
Serguei MANKOVSKII - Morgan Hill CA, US

CA, INC. - New York NY

G06N 5/02

Systems and methods may include receiving, by an expert system, performance data for a monitored system. The systems and methods may include generating a prediction for the monitored system in response to determining that the performance data satisfies a condition. The prediction may identify an anomaly that is predicted to occur. The systems and methods may include receiving, by a filter system, the prediction, information identifying the condition, and user information. The user information may include user preference information and user feedback information. The systems and methods may include determining a filter criteria based on the user information. The filter criteria may be based on the preferences for predictions to be provided to the user and on the historical user feedback regarding the historical predictions. The systems and methods may include providing the prediction to the user in response to determining that the particular prediction satisfies the filter criteria.