Vmware, Inc.

Zscaler, Inc.

Conopco, Inc.

Wmware, Inc.

Unilever Plc

Unilever, N.v.

Kumar Gaurav

Hemanth Kumar Pannem

Amarnath Palavalli

Chandrashekhar Jha

Jobin Raju George

Jusvinder Singh

Chakkaravarthy Periyasamy Balaiah

Charu Duggal

Piyush Bharat Masrani

Aditya Gopisetti

Ajay Gautam

Anil Kv Kumar

Prateek Sahu

Vaibhav Kohli

Shruti Sharma

Rajdeep Dua

Janhavi Sanjay Raut

Kailash Kailash

Anne Holler

Vishal I Gupta

Sushil Pangeni

Dattathreya Sathyamurthy

Akhil Sadashiv Hingane

Sushil Verma

Servesh Pratap Singh

Shrisha Chandrashekar

Gurusreekanth Chagalakondu

Abhishek Bathla

Raman Madaan

Shweta Gupta

Mrityunjoy Saha

Bhaskardas Kambivelu

Ashish Anant Vaidya

Arun Narayanaswamy

Swarnalatha Pasupuleti

Mohan Vijaykumar Chavan

Punam Bandyopadhyay

Satish Kalipatnapu

Shilpa Atul Vora

Akshay Prabhakar Mirajkar

Lakshmaiah Regoti

Naveen Mudnal

Jayalekshmy Nair

Mridula Kini

T N Ravi Kumar

Vijay Potluri

Gurusreekanth C

Chintesh Pulavarthi

Nikhil Dhankani

Nupur Agrawal

Bharath Palanisamy

Praveen Kumar

Sujeetkumar Jha

The detection of utilized virtual machines through usage pattern analysis is described. In one example, a computing device can collect utilization metrics from a virtual machine over time. The utilization metrics can be related to one or more processing usage, disk usage, network usage, and memory usage metrics, among others. The utilization metrics can be used to determine a number of clusters, and the clusters can be used to organize the utilization metrics into groups. Depending upon the number or overall percentage of the utilization metrics assigned to individual ones of the plurality of clusters, it is possible to determine whether or not the virtual machine is a utilized or an idle virtual machine. Once identified, utilized virtual machines can be migrated in some cases. Idle virtual machines can be shut down to conserve processing resources and costs in some cases.

Usage pattern virtual machine idle detection

Methods and systems predict parameters in a dataset of an identified piece of ('information technology') IT equipment. An automated method identifies datasets IT equipment in a same category of IT equipment as a piece of IT equipment identified as having incomplete dataset information. Each dataset of IT equipment parameters is used to construct generalized linear models of different classes of IT equipment within the category of IT equipment. The class of the identified IT equipment is determined. A predicted equipment parameter of incomplete information of the identified piece of IT equipment is computed using the generalized linear model associated with the class. The predicted equipment parameter can be used to complete the dataset of the identified piece of IT equipment.

Methods and systems to predict parameters in a database of information technology equipment

Systems and methods include obtaining statistics based on monitoring in a cloud-based system for a given time period; and, responsive to determining an arrangement of counters for N counters, storing each of M counters for the given time period as a plurality of records with each record including a record type, a possible offset to a next record in terms of a counter identifier (ID), and a counter value, wherein N and M are integers and M&#x3c;&#x3c;N, and wherein the arrangement is determined such that most frequently used counters occupy lower counter IDs. The systems and methods can further include updating the arrangement of the counters for the N counters, to perform an optimization such that the most frequently used counters occupy lower counter IDs.

Systems and methods for efficiently maintaining records in a cloud-based system

Systems and methods include receiving a record associated with an incident that was detected by the CASB system in a Software-as-a-Service (SaaS) application; determining a hash based on a plurality of levels for the record; determining if the record exists in a data store based on the hash, and if the record exists, deleting an old record; and inserting the record in the data store based on the hash, wherein the data store is maintained in-memory and includes records at leaf nodes in a multi-level hash based on the plurality of levels.

Cloud access security broker systems and methods with an in-memory data store

A system can provide a visual representation of an inventory of data entities for a distributed computing system. Inventory data including cost and operational data for data entities such as data centers, servers, and virtual machines, can be converted into a format file. The format file can be used to create a tree of nodes and node summaries corresponding to the data entities. A user interface can display hierarchical and isolated views of the tree revealing parent child relationships between data entities within a computing system infrastructure. Node summaries including cost and utilization data can be displayed to reveal how specific sub-costs such as labor and licensing, are driven by data entities in one level of the infrastructure and pushed to respective parent or child data entities in other levels. Views of the tree can be used to determine areas of inefficiency or reduced value within the computing system.

Visualizing data center inventory and entity relationships

Systems and methods include determining log data for a time period at a plurality of senders, wherein each sender is a node in the cloud-based system, and the log data is associated with one or more cloud services; providing the log data to one or more storage clusters, via one or more distributors, for the time period; responsive to all of the plurality of senders performing the providing, moving to a next time period and repeating the determining and the providing; detecting a given sender is a faulty data source or a slow data source; and moving the given sender to a deferred processing list where the given sender does not hold up the moving to the next time period.

Detecting lagging nodes in a time-synchronized distributed environment

Methods and systems assist data center customer to plan virtual data center ('VDC') configurations, create purchase recommendations to achieve either an expansion or contraction of a VDC, and optimize the data center cost. Methods generate recommendations on lower cost combinations of virtual machine ('VM') guest OS licenses, server computer hardware and VM software to optimize the costs are generated, generate data center customer plans for additional VMs with Quest OS for a projected period of time, provide recommendations on lower cost combination of guest OS licenses, server hardware, and VM software to optimize the cost. Methods also report any underutilized licensed servers and provide recommendations for cost savings when volume licenses can be replaced by instance based software licenses. Methods may generate VM placement recommendations to data center customers while the customers attempt to manually migrate VMs to different server computers.

Methods and systems to optimize server utilization for a virtual data center

The detection of idle virtual machines through usage pattern analysis is described. In one example, a computing device can collect utilization metrics from a virtual machine over time. The utilization metrics can be related to one or more processing usage, disk usage, network usage, and memory usage metrics, among others. The utilization metrics can be separated into a set of training metrics and a set of validation metrics, and a number of clusters can be determined based on the set of training metrics. The clusters can be used to organize the set of validation metrics into groups. Depending upon the number or overall percentage of the utilization metrics assigned to individual ones of the plurality of clusters, it is possible to determine whether or not the virtual machine is an idle virtual machine. Once identified, idle virtual machines can be shut down to conserve processing resources and costs.

Methods and systems to optimize operating system license costs in a virtual data center

Methods and systems assist data center customer to plan virtual data center ('VDC') configurations, create purchase recommendations to achieve either an expansion or contraction of a VDC, and optimize the data center cost. Methods generate recommendations on lower cost combinations of virtual machine ('VM') guest OS licenses, server computer hardware and VM software to optimize the costs are generated, generate data center customer plans for additional VMs with guest OS for a projected period of time, provide recommendations on lower cost combination of guest OS licenses, server hardware, and VM software to optimize the cost. Methods also report any underutilized licensed servers and provide recommendations for cost savings when volume licenses can be replaced by instance based software licenses. Methods may generate VM placement recommendations to data center customers while the customers attempt to manually migrate VMs to different server computers.

Various examples are disclosed for using clustering routines to extrapolate metrics to other computing resources in a cluster. One or more computing devices can classify computing resources, such as servers, based on various characteristics of the computing resources. For each class of computing resource, a clustering routine can be applied to generate clusters of the computing resources. A minimal number of metrics required to be obtained from an end user can be determined as a function of a number of the clusters. If one or more of the metrics are obtained from the end user, the metrics can be extrapolated to other computing resources in the same cluster.

Clustering routines for extrapolating computing resource metrics

Methods and systems to compute application license costs of a number of applications run on virtual machines of a virtualized data center are described. In one aspect, one or more of the virtual machines ('VMs') that form the virtual data center are determined. Each VM is created from hardware components specifications of one or more application blueprints stored in a data-storage devices. The one or more blueprints are searched to determine the one more applications that run in each VM. For each VM, a total VM application licensing cost of the one or more applications is computed based on one or more of an application instance license cost, application socket license cost, and application core license of each of the one or more applications associated with each application.

Methods and systems to determine application license costs in a virtualized data center for use in virtual machine consolidation

Methods and apparatus to select virtualization environments are disclosed. An example apparatus includes a logic circuit, a workload analyzer to determine characteristics of a virtualized application, a score generator to compare the characteristics of the virtualized application to a plurality of virtualization environment types to determine scores for each of the plurality of virtualization environment types, the scores based on rules that identify different scores for combinations of characteristics and virtualization environment types, and a workload deployer to deploy the virtualized application using one of the plurality of virtualization environment types based on the scores.

Methods and apparatus to select virtualization environments during deployment

Growing community of inventors

Bengaluru, India

Kumar Gaurav