Abstract: 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.

Abstract: The present disclosure provides a method in a data processing system that includes at least one processor and at least one memory. The at least one memory includes instructions executed by the at least one processor to implement a driving encounter recognition system. The method includes receiving information, from one or more sensors coupled to a first vehicle, determining first trajectory information associated with the first vehicle and second trajectory information associated with a second vehicle, extracting a feature vector, providing the feature vector to a trained classifier, the classifier trained using unsupervised learning based on a plurality of feature vectors, and receiving, from the trained classifier, a classification of the current driving encounter in order to facilitate the first vehicle to perform a maneuver based on the current driving encounter.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Examples of systems described herein include a computing node configured to execute a hypervisor and a hypervisor independent interface software layer configured to execute on the computing node. The interface software layer may be configured to determine configuration information and an operating system for a virtual machine to be created, receive an instruction to create the virtual machine through the hypervisor independent interface software layer, convert the instruction to create the virtual machine into a hypervisor specific command, create a virtual machine instance responsive to the hypervisor specific command, generate an image file by accessing a customization tool library from a plurality of customization tool libraries based, at least in part, on the customization information operating system for the virtual machine, attach the image file to the virtual machine, and power on the virtual machine instance.

Abstract: In one embodiment, a system for placing virtual machines in a virtualization environment receives instructions to place a virtual machine within the virtualization environment, wherein the virtual environment includes a plurality of host machines that include a hypervisor, at least one user virtual machine, and an input/output (I/O) controller and a virtual disk that includes a plurality of storage devices and is accessible by all of the I/O controllers, wherein the I/O controllers conduct I/O transactions with the virtual disk based on I/O requests received from the UVMs. The system determines a predicted resource usage profile for the virtual machine. The system selects, based on the predicted resource usage profile, one of the host machines for placement of the virtual machine. The system places the virtual machine on the selected one of the host machines.

Abstract: In one embodiment, a system for placing virtual machines in a virtualization environment receives instructions to place a virtual machine within the virtualization environment, wherein the virtual environment includes a plurality of host machines that include a hypervisor, at least one user virtual machine, and an input/output (I/O) controller and a virtual disk that includes a plurality of storage devices and is accessible by all of the I/O controllers, wherein the I/O controllers conduct I/O transactions with the virtual disk based on I/O requests received from the UVMs. The system determines a predicted resource usage profile for the virtual machine. The system selects, based on the predicted resource usage profile, one of the host machines for placement of the virtual machine. The system places the virtual machine on the selected one of the host machines.