A virtual machine that includes a plurality of processes executes on a computer processor. A record-replay file, trace annotations, and an application program interface request are received into the computer processor. The trace annotations and application program interface request are translated into record-replay commands. The record-replay commands capture data from the record-replay file, and the captured data can be accessed via a programmatic interface.

In one embodiment, a processor includes a memory hierarchy and a core coupled to the memory hierarchy. The memory hierarchy stores encrypted data, and the core includes circuitry to access the encrypted data stored in the memory hierarchy, decrypt the encrypted data to yield decrypted data, perform an entropy test on the decrypted data, and update a processor state based on a result of the entropy test. The entropy test may include determining a number of data entities in the decrypted data whose values are equal to one another, determining a number of adjacent data entities in the decrypted data whose values are equal to one another, determining a number of data entities in the decrypted data whose values are equal to at least one special value from a set of special values, or determining a sum of n highest data entity value frequencies.

A method for containerized workload scheduling can include determining a network state for a first hypervisor in a virtual computing cluster (VCC). The method can further include determining a network state for a second hypervisor. Containerized workload scheduling can further include deploying a container to run a containerized workload on a virtual computing instance (VCI) deployed on the first hypervisor or the second hypervisor based, at least in part, on the determined network state for the first hypervisor and the second hypervisor.

The disclosure provides a method for managing multiple operating systems in a terminal. The terminal includes multiple operating systems and a management system. The management system is configured to manage the multiple operating systems. The management system includes a cross-system application database. The method includes: when a first operating system in the multiple operating systems runs in a foreground, and a second operating system in the multiple operating systems runs in a background, if the second operating system receives a first message of a first application in the second operating system, sending, by the second operating system, a notification message to the management system; storing, by the management system, the notification message into the cross-system application database; and listening, by the first operating system, on the cross-system application database, and outputting a prompt of the first message when listening and obtaining the notification message.

A system and method for backing up workloads for multiple tenants of a cloud computing system are disclosed. A method of backing up workloads for multiple tenants of a computing system includes triggering an archival process according to an archival policy set by a tenant, and executing the archival process by reading backup data of the tenant stored in a backup storage device of the computer system and transmitting the backup data to an archival store designated in the archival policy, and then deleting or invalidating the backup data stored in the backup storage device.

A virtualized computing system includes: a host cluster including hosts executing a virtualization layer on hardware platforms thereof, the virtualization layer configured to support execution of virtual machines (VMs), the VMs including a pod VM, the pod VM including a container engine configured to support execution of containers in the pod VM, the pod VM including a first virtual disk attached thereto; and an orchestration control plane integrated with the virtualization layer, the orchestration control plane including a master server in communication with a pod VM controller, the pod VM controller configured to execute in the virtualization layer external to the VMs and cooperate with a pod VM agent in the pod VM, the pod VM agent generating root directories for the containers in the pod VM, each of the root directories comprising a union a read/write ephemeral layer stored on the first virtual disk and a read-only layer.

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.

Methods and apparatuses in a cloud-based database management system are described. Data in a database are stored in a plurality of pages in a page store of the database. A plurality of redo log records are received to be applied to the database. The redo log records within a predefined boundary are parsed to determine, for each given redo log record, a corresponding page to which the given log record is to be applied. The redo log records are reordered by corresponding page. The reordered redo log records are stored to be applied to the page store of the database.

A system for container migration includes containers running instances of an application running on a cluster, an orchestrator with a controller, a memory, and a processor in communication with the memory. The processor executes to monitor a vitality metric of the application. The vitality metric indicates that the application is in either a live state or a dead state. Additionally, horizontal scaling for the application is disabled and the application is scaled-down until the vitality metric indicates that the application is in the dead state. Responsive to the vitality metric indicating that the application is in the dead state, the application is scaled-up until the vitality metric indicates that the application is in the live state. Also, responsive to the vitality metric indication transitioning from the dead state to the live state, the application is migrated to a different cluster while the horizontal scaling of the application is disabled.

Examples of systems are described herein which may dynamically allocate compute resources to recovery clusters. Accordingly, a recovery site may utilize fewer compute resources in maintaining recovery clusters for multiple associate clusters, while ensuring that, during use, compute resources are allocated to a particular cluster. This may reduce and/or avoid vulnerabilities arising from a use of shared resources in a virtualized and/or cloud environment.