Abstract: A virtual machine memory management method and device are provided. The method includes: identifying memory of a non-operating system of a virtual machine from total memory allocated to the virtual machine, where the total memory includes memory of the virtual machine and management memory of a hypervisor, and the memory of the virtual machine includes memory of an operating system of the virtual machine and the memory of the non-operating system of the virtual machine; using the memory of the non-operating system of the virtual machine as swappable memory; and storing data in the swappable memory into a nonvolatile memory. This method helps avoid a problem that computer performance significantly deteriorates in a virtual machine over-submission scenario, and improve stability in running of a virtual machine and users' use experience.

Abstract: Techniques are provided for managing load distribution in a data storage system which comprises storage server nodes and storage nodes, wherein each storage server node comprises one or more distribution targets, wherein each distribution target comprises a logical processing unit that is configured to manage input/output operations for accessing data in the storage nodes. A process for managing the load distribution comprises: partitioning a logical address space into a plurality of distribution items; mapping the logical address space to a sequence of one or more distribution templates, wherein each distribution template comprises an ordered list of the distribution targets of the storage server nodes, which is uniquely different for each of the one or more distribution templates; and utilizing the one or more distribution templates to map the distribution items of the logical address space to the distribution targets of the storage server nodes.

Abstract: A computer implemented method controls in a core dimension packing the plurality of processing unit cores to reduce partial use of the plurality of processing unit cores by concurrently loading into the plurality of processing unit cores: all computational nodes of at least one member selected from the group consisting of the first number of whole groups and the first number of partial groups; and all computation nodes of at least one member selected from the group consisting of the second number of whole groups and the second number of partial groups. According to other illustrative embodiments, a computer system and a computer program product for packing the plurality of processing cores are provided. As a result, the illustrative embodiments can provide a technical effect of increasing the performance in running jobs in a computer system in which partial use of processing unit cores can be reduced.

Abstract: Automation methods and systems include identifying common parts of consecutive pairs of consecutive screen outputs. The consecutive screen outputs are clustered responsive to a determination that a distance between the common parts of the consecutive pairs is lower than a threshold distance value. Interactions with a remote computer system are automated based on the clustered consecutive screen outputs.

Abstract: Embodiments of the present disclosure a method for dynamically generating a generative operating system based on a hardware and software environment feature, the method is configured to generate the generative operating system, and an architecture of the generative operating system includes: a hardware architecture adaptation layer and a meta kernel layer and an external library of the operating system.

Abstract: A method for resource allocation in a disaggregated data center (DDC), comprising: a reliability model to determine an achievable reliability for a service request to the DDC; a integer linear programming (ILP) model to perform a resource allocation for the service request to the DDC such that maximizing total number of service requests received by the DDC accepted for execution is maximized, while the number of the accepted service requests allocated with backup computing resources is minimized; and a heuristic process to perform a resource allocation for the service request to the DDC such that the least reliable node of each needed computing resource type is allocated but still meeting the reliability requirement of the service request.