Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for using a virtual desktop. A method in one embodiment includes receiving, at a first edge node in a plurality of edge nodes, an instruction from a first set of input devices in a plurality of peripheral devices. The instruction is for use of a first virtual desktop deployed on the first edge node. The method further includes: using the first virtual desktop based on the instruction by using resources at the first edge node. The method further includes: sending data to an output device in the plurality of peripheral devices, wherein the data is associated with the use of the first virtual desktop. The solution for using a virtual desktop of the present application enables the use of a virtual desktop using resources at an edge node without requiring a client.

Described herein are systems, methods, and software to dynamically manage resources across software-defined data centers. In one implementation, a monitoring service obtains flow information associated with physical network interfaces (PNICs) and virtual networking interfaces (VNICs) across a plurality of software-defined data centers (SDDCs). The monitoring service further determines when the flow information associated with the one or more workloads satisfy criteria and, in response to satisfying criteria, generates an update to a configuration associated with at least one SDDC of the plurality of SDDCs based on the flow information.

An information handling system may include a processor; a network interface; and a physical storage resource having data stored thereon that is usable by a virtual resource that is executable on the processor. The network interface may accelerate migration of the data to a destination system by, in response to a command from a virtual machine manager: offloading, from the processor, a copying process configured to copy the data to the destination system; tracking portions of the data that are changed by the virtual resource during the copying process; notifying the virtual machine manager that a designated checkpoint has been reached in the copying process; causing the virtual resource to pause; completing the copying process; and causing the virtual resource to resume and use the copied data at the destination instead of the data on the physical storage resource.

A cloud computer system is provided that includes a plurality of computer devices and a database. The plurality of computer devices execute a plurality of virtual machines, with one of the virtual machines serving as a controller node and the remainder serving as worker instances. The controller node is programmed to accept a request to initiate a distributed process that includes a plurality of data jobs, determine a number of worker instances to create across the plurality of computer devices, and cause the number of worker instances to be created on the plurality of computer devices. The worker instances are programmed to create a unique message queue for the corresponding worker instance, and store a reference for the unique message queue that was created for the corresponding worker to the database. The controller node retrieves the reference to the unique message queues and posts jobs to the message queues for execution by the worker instances.

Described herein are systems, methods, and software to manage the migration of workloads from a first computing system to a second computing system. In one implementation, the first computing system identifies a request to migrate one or more workloads to a second computing system. In response to the request, the first computing system disables one or more services and disables all but one network interface on the first computing system. The first computing system then communicates configuration information to the second computing system and monitors for a cancel notification from the second computing system using the remining network interface. After receiving the cancel notification, the first computing system enables the other network interfaces may initiate the one or more services.

The technology provides for live migration from a first cluster to a second cluster. For instance, when requests to one or more cluster control planes are received, a predetermined fraction of the received requests may be allocated to a control plane of the second cluster, while a remaining fraction of the received requests may be allocated to a control plane of the first cluster. The predetermined fraction of requests are handled using the control plane of the second cluster. While handling the predetermined fraction of requests, it is detected whether there are failures in the second cluster. Based on not detecting failures in the second cluster, the predetermined fraction of requests allocated to the control plane of the second cluster may be increased in predetermined stages until all requests are allocated to the control plane of the second cluster.

Various techniques for managing heat and backwards-incompatible updates in cloud-based networks are described. In an example method, a virtualized resource, is identified. At least one first host may include an updated version of an element and at least one second host may include a previous version of the element. The updated version may be incompatible with the previous version. A first desirability index corresponding to the at least one first host may be less than a second desirability index corresponding to the at least one second host. The virtualized resource may be live-migrated from the source host to a target host among the at least one first host.

Performing container scaling and migration for container-based microservices is provided. A first set of features is extracted from each respective microservice of a plurality of different microservices. A number of containers required at a future point in time for each respective microservice of the plurality of different microservices is predicted using a trained forecasting model and the first set of features extracted from each respective microservice. A scaling label and a scaling value are assigned to each respective microservice of the plurality of different microservices based on a predicted change in a current number of containers corresponding to each respective microservice according to the number of containers required at the future point in time for each respective microservice. The current number of containers corresponding to each respective microservice of the plurality of different microservices is adjusted based on the scaling label and the scaling value assigned to each respective microservice.

At least one processor is configured to obtain measurement information comprising an indication of an amount of utilization of a hardware resource of a first server node by a plurality of processing groups and to determine that the amount of utilization of the hardware resource is above a threshold amount of utilization. The at least one processor is further configured to select a given processing group for redistribution based at least in part on the determination that the amount of utilization of the hardware resource is above the threshold amount and on an amount of utilization of the hardware resource by the given processing group. The at least one processor is further configured to determine that a second server node comprises enough available capacity of the hardware resource and to redistribute the given processing group to the second server node based at least in part on the determination.

Embodiments described herein are generally directed to improving performance of high-performance computing (HPC) or artificial intelligence (AI) workloads on cluster computer systems. According to one embodiment, a section of a high-performance computing (HPC) or artificial intelligence (AI) workload executing on a cluster computer system is identified as significant to a figure of merit (FOM) of the workload. An alternate placement among multiple heterogeneous compute resources of a node of the cluster computer system is determined for a portion of the section currently executing on a given compute resource of the multiple heterogeneous compute resources. After predicting an improvement to the FOM based on the alternate placement, the portion is relocated to the alternate placement.