Nutanix, Inc.

Lightbeam.ai, Inc.

Aditya Ramesh

Gaurav Poothia

Robert William Schwenz

Hemanth Kumar Mantri

Himanshu Shukla

Abhinay Nagpal

Fabien Hermenier

Kamalneet Singh

Ashwin Thennaram Vakkayil

Miao Cui

Rahul Singh

Gokul Kannan

Manosiz Bhattacharyya

Tabrez Parvez Memon

Felipe Franciosi

Saish Sali

Malcolm Gordon Crossley

Swathi Koundinya

A system and method include classifying and assigning virtual disks accessed from compute only nodes. The method determines, by a management processor of a virtual computing system, characteristics for a plurality of virtual disks hosted on a plurality of hyper converged nodes in a cluster of nodes in the virtual computing system. The method further classifies, by the management processor, each of the plurality of virtual disks based on the determined characteristics and identifies, by the management processor, one of the plurality of virtual disks to host data for a virtual machine on a compute only node based on the classification to spread out input-output demand in the cluster, reducing probability of input-output bottlenecks and increasing cluster-wide storage throughput. The method also assigns, by the management processor, the identified virtual disk to host data for the virtual machine located on the compute only node.

Assigning a virtual disk to a virtual machine hosted on a compute node for improved network performance

Datasource processors may communicate with an artificial intelligence (AI) engine in order to generate, in parallel, object summaries from datasource objects received from datasources. Each object summary may include an object identifier, one or more local entities, and a mapping from each of the one or more local entities to one or more attributes. A global entity resolver may augment the object summaries by mapping each of the local entities to a global entity. Policy engines may evaluate, in parallel, the object summaries with respect to a security and/or privacy policy. If a security and/or privacy violation is recognized, a remediation measure may be applied in connection with the datasource object for which the security and/or privacy violation exists.

Method for maintaining privacy and security of data

Methods, systems and computer program products for computer cluster management. Multiple components are operatively interconnected to carry out operations for placing virtual machines onto a multi-tenant computing cluster, where the placement achieves adherence to a set of security requirements. Initially, data characterizing logical CPU resources of the multi-tenant computing cluster are gathered. Upon receipt of a request to place a virtual machine onto a node of the multi-tenant computing cluster, a set of security rules that are used to achieve the set of security requirements associated with the multi-tenant computing cluster are accessed. In accordance with the security rules the virtual machine is assigned to execute in a portion of the logical CPU resources. The virtual machine does not share logical CPU resources with any other tenant.

Security-aware scheduling of virtual machines in a multi-tenant infrastructure

Virtual disk management for virtual disks accessed from compute only nodes

Communication between VMs running in a clustered virtualization environment and a distributed storage system is managed. The distributed storage system consists of multiple storage/compute nodes. A hypervisor communicates with a controller in a local node on behalf of a VM. The controller will redirect a connection to a second controller on different node. The controller to which the connection is redirected is selected so as to distribute the load evenly across the cluster.

Hypervisor attached volume group load balancing

A system and method include migrating virtual machines (VMs) between compute only (CO) and hyper converged (HC) nodes. The method includes identifying, by a management processor of a virtual computing system, a plurality of virtual machines hosted on compute only (CO) nodes in the virtual computing system. The management processor then identifies hyper converged (HC) nodes having virtual disks hosting data for the plurality of virtual machines hosted on CO nodes. When a virtual machine (VM) in the plurality of virtual machines is migrating, the management processor biases the VM to migrate to a first HC node hosting a virtual disk assigned to host data for the VM.

Live migration of virtual machines between compute only nodes and hyperconverged nodes

A system and method include assigning virtual machines (VMs) to any one of compute only (CO) and hyper converged (HC) nodes. The method includes identifying, by a management processor of a virtual computing system, input-output patterns for a plurality of virtual disks in the virtual computing system; ranking, by the management processor, the plurality of virtual machines according to the identified input-output patterns; assigning, by the management processor, the plurality of virtual machines to any one of CO and HC nodes that optimize performance of the virtual computing system; monitoring, by the management processor, performance of the plurality of virtual machines; and determining, by the management processor, a change in behavior of one of the plurality of virtual machines.

Intelligent assignment of virtual machines to compute only or hyper converged nodes

Systems for distributed resource system management. A first computing system operates in a first computing environment. A predictive model is trained in the first computing environment to form a trained resource performance predictive model that comprises a set of trained model parameters to capture at least computing and storage IO parameters that are responsive to execution of one or more workloads that consume computing and storage resources in the first computing environment. When the trained resource performance predictive model is deployed to a second computing environment, various computing system configuration differences, and/or workload differences and/or other differences between the first computing environment and the second computing environment are detected and measured. Responsive to the detected differences and/or measurements, some of the trained resource performance predictive model parameters are modified to adapt the trained resource performance predictive model to any of the detected and/or measured characteristics of the second computing environment.

Adapting a pre-trained distributed resource predictive model to a target distributed computing environment

Resource allocation techniques for distributed data storage. A set of distributed storage system historical resource usage measurements are collected and stored using distributed storage system measurement techniques. The resource usage metrics are associated with and/or derived from processing entities in the distributed storage computing system. An analysis module determines a training window time period corresponding to a portion of the collected distributed storage system historical resource usage measurements. The training window time period is determined so as to provide an earlier time boundary and a later time boundary that defines a periodically recurring portion of the distributed storage system historical resource usage measurements. A latest cycle of those periodically recurring measurements are then used to train a predictive model, which in turn is used to produce distributed storage system predicted resource usage characteristics. Resource allocation decisions are made based at least in part on predictions from the trained predictive model.

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San Jose, CA, United States of America

Aditya Ramesh