Abstract:
Exemplary methods, apparatuses, and systems include a client virtual machine processing a system call for a device driver to instruct a physical device to perform a function and transmitting the system call to an appliance virtual machine to execute the system call. The client virtual machine determines, in response to the system call, that an established connection with the appliance virtual machine has switched from a first protocol to a second protocol, the first and second protocols including a high-performance transmission protocol and Transmission Control Protocol and Internet Protocol (TCP/IP). The client virtual machine transmits the system call to the appliance virtual machine according to the second protocol. For example, the established connection may switch to the second protocol in response to the client virtual machine migrating to the first host device from a second host device.
Abstract:
Disclosed are aspects of memory-aware placement in systems that include graphics processing units (GPUs) that are virtual GPU (vGPU) enabled. In some examples, graphics processing units (GPU) are identified in a computing environment. Graphics processing requests are received. A graphics processing request includes a GPU memory requirement. The graphics processing requests are processed using a graphics processing request placement model that minimizes a number of utilized GPUs that are utilized to accommodate the requests. Virtual GPUs (vGPUs) are created to accommodate the graphics processing requests according to the graphics processing request placement model. The utilized GPUs divide their GPU memories to provide a subset of the plurality of vGPUs.
Abstract:
Disclosed are aspects of memory-aware placement in systems that include graphics processing units (GPUs) that are virtual GPU (vGPU) enabled. In some embodiments, a computing environment is monitored to identify graphics processing unit (GPU) data for a plurality of virtual GPU (vGPU) enabled GPUs of the computing environment, a plurality of vGPU requests are received. A respective vGPU request includes a GPU memory requirement. GPU configurations are determined in order to accommodate vGPU requests. The GPU configurations are determined based on an integer linear programming (ILP) vGPU request placement model. Configured vGPU profiles are applied for vGPU enabled GPUs, and vGPUs are created based on the configured vGPU profiles. The vGPU requests are assigned to the vGPUs.
Abstract:
Methods, systems, and computer programs, for estimating think times. One of the methods includes receiving a request to perform a test of one or more computing resources. The test of the one or more computing resources is performed by simulating an interaction of one or more simulated users with the one or more computing resources. Requests are submitted from the simulated user for execution by the one or more computing resources. Respective response times of the one or more computing resources to each of the requests are measured. An estimated think time of the simulated user is computed, wherein the estimated think time is computed based on at least one preceding response time.
Abstract:
Systems and techniques are described for modifying an executable file of an application and executing the application using the modified executable file. A described technique includes receiving, by a virtual machine, a request to perform an initial function of an application and an executable file for the application. The virtual machine modifies the executable file by redirecting the executable file to a custom runtime library that includes a custom function configured to initialize the application and to place the application in a paused state. A custom function call is added to the custom function in the executable file. The virtual machine initializes the application by executing the modified executable file, the executing causing the custom function to initialize the application and place the application in a paused state.
Abstract:
This document describes techniques for identifying trusted websites. In one embodiment, a computer system can receive a request from user to access a website and a private image and a public image wherein the public image and the private image are associated with a user account that enables the user to access the website. The computer system then embeds the private image in the public image to create a combined image and transmits the combined image to a client device for processing. The computer system can then receive a confirmation from the user that at least the private image embedded in the combined image is verified.
Abstract:
Methods, systems, and computer programs are provided for measuring the performance of display images received on a remote computer display. One method includes an operation for detecting calls from an application to an application programming interface (API), which is provided for rendering images on a display image, each call causing an update of the display image. Further, the method includes an operation for embedding data for measuring performance in display frames of the display image based on the detecting. The embedding results in modified displayed frames with respective data for measuring performance. The modified displayed frames are transmitted to a remote client, which results in received modified display frames having respective received data for measuring the performance. In addition, the method includes an operation for calculating the remote display quality for the given application based on the received modified display frames and the respective received data for measuring performance.
Abstract:
Disclosed are aspects of workload selection and placement in systems that include graphics processing units (GPUs) that are virtual GPU (vGPU) enabled. In some aspects, workloads are assigned to virtual graphics processing unit (vGPU)-enabled graphics processing units (GPUs) based on a variety of vGPU placement models. A number of vGPU placement neural networks are trained to maximize a composite efficiency metric based on workload data and GPU data for the plurality of vGPU placement models. A combined neural network selector is generated using the vGPU placement neural networks, and utilized to assign a workload to a vGPU-enabled GPU.
Abstract:
Disclosed are aspects of virtual graphics processing unit (vGPU) scheduling-aware virtual machine migration. Graphics processing units (GPUs) that are compatible with a current virtual GPU (vGPU) profile for a virtual machine are identified. A scheduling policy matching order for a migration of the virtual machine is determined based on a current vGPU scheduling policy for the virtual machine. A destination GPU is selected based on a vGPU scheduling policy of the destination GPU being identified as a best available vGPU scheduling policy according to the scheduling policy matching order. The virtual machine is migrated to the destination GPU.
Abstract:
Disclosed are aspects of network function placement in virtual graphics processing unit (vGPU)-enabled environments. In one example a network function request is associated with a network function. A scheduler selects a vGPU-enabled GPU to handle the network function request. The vGPU-enabled GPU is selected in consideration of a network function memory requirement or a network function IO requirement. The network function request is processed using an instance of the network function within a virtual machine that is executed using the selected vGPU-enabled GPU.