Abstract: The present disclosure relates generally to virtualization, and more particularly to techniques for deploying containers in a virtual environment. The container scheduling can be based on information determined by a virtual machine scheduler. For example, a container scheduler can receive a request to deploy a container. The container scheduler can send container information to the virtual machine scheduler. The virtual machine scheduler can use the container information along with resource utilization of one or more virtual machines to determine an optimal virtual machine for the container. The virtual machine scheduler can send an identification of the optimal virtual machine back to the container scheduler so that the container scheduler can deploy the container on the optimal virtual machine.

Abstract: The present disclosure relates generally to virtualization, and more particularly to techniques for deploying containers in a virtual environment. The container scheduling can be based on information determined by a virtual machine scheduler. For example, a container scheduler can receive a request to deploy a container. The container scheduler can send container information to the virtual machine scheduler. The virtual machine scheduler can use the container information along with resource utilization of one or more virtual machines to determine an optimal virtual machine for the container. The virtual machine scheduler can send an identification of the optimal virtual machine back to the container scheduler so that the container scheduler can deploy the container on the optimal virtual machine.

Abstract: The present disclosure relates generally to virtualization, and more particularly to techniques for deploying containers in a virtual environment. The container scheduling can be based on information determined by a virtual machine scheduler. For example, a container scheduler can receive a request to deploy a container. The container scheduler can send container information to the virtual machine scheduler. The virtual machine scheduler can use the container information along with resource utilization of one or more virtual machines to determine an optimal virtual machine for the container. The virtual machine scheduler can send an identification of the optimal virtual machine back to the container scheduler so that the container scheduler can deploy the container on the optimal virtual machine.

Abstract: The present disclosure relates generally to virtualization, and more particularly to techniques for deploying containers in a virtual environment. The container scheduling can be based on information determined by a virtual machine scheduler. For example, a container scheduler can receive a request to deploy a container. The container scheduler can send container information to the virtual machine scheduler. The virtual machine scheduler can use the container information along with resource utilization of one or more virtual machines to determine an optimal virtual machine for the container. The virtual machine scheduler can send an identification of the optimal virtual machine back to the container scheduler so that the container scheduler can deploy the container on the optimal virtual machine.

Abstract: Techniques for scheduling resources on a managed computer system are provided herein. A generative adversarial network generates predicted resource utilization. An orchestrator trains the generative adversarial network and provides the predicted resource utilization from the generative adversarial network to a resource scheduler for usage when the quality of the predicted resource utilization is above a threshold. The quality is measured as the ability of a generator component of the generative adversarial network to “fool” a discriminator component of the generative adversarial network into misclassifying the predicted resource utilization as being real (i.e., being of the type that is actually measured from the computer system).

Abstract: A computer processing system having a first memory with a first set of memory pages resident therein and a second memory coupled to the first memory. A resource tracker provides information to instances of a long short-term memory (LSTM) recurrent neural network (RNN). A predictor identifies memory pages from the first set of memory pages for prediction by the one or more LSTM RNN instances. The system groups the memory pages of the identified plurality of memory pages into a number of patterns based on a number of memory accesses per time. An LSTM RNN instance predicts a number of page accesses for each pattern. A second set of memory pages is selected for moving from the first memory to the second memory.

Abstract: The present disclosure relates generally to virtualization, and more particularly to techniques for deploying containers in a virtual environment. The container scheduling can be based on information determined by a virtual machine scheduler. For example, a container scheduler can receive a request to deploy a container. The container scheduler can send container information to the virtual machine scheduler. The virtual machine scheduler can use the container information along with resource utilization of one or more virtual machines to determine an optimal virtual machine for the container. The virtual machine scheduler can send an identification of the optimal virtual machine back to the container scheduler so that the container scheduler can deploy the container on the optimal virtual machine.