Abstract: In an approach to improve the field of architecture generation by dynamically sizing computer system architecture requirements for virtual machines. Embodiments define static parameters and dynamic parameters for customer consuming resources of the computer system architecture and store data of the static parameters and dynamic parameters in data warehouse database (DWDB) tables. Further, embodiments compute, using the data of the DWDB tables, calculate the minimum, maximum, mean and standard deviation (SD) for the user count and the CPU and memory usage, and update the DWDB tables based on the minimum, maximum, and SD values per customer.

Abstract: At least one data file for backup can be received. The data file can be divided into a plurality of data blocks. A first portion of the plurality of data blocks can be allocated to a first data processing system for backup by the first data processing system. A second portion of the plurality of data blocks can be allocated to a second data processing system for backup by the second data processing system.

Abstract: At least one data file for backup can be received. The data file can be divided into a plurality of data blocks. A first portion of the plurality of data blocks can be allocated to a first data processing system for backup by the first data processing system. A second portion of the plurality of data blocks can be allocated to a second data processing system for backup by the second data processing system.

Abstract: In an approach to improve the field of architecture generation by dynamically sizing computer system architecture requirements for virtual machines. Embodiments define static parameters and dynamic parameters for customer consuming resources of the computer system architecture and store data of the static parameters and dynamic parameters in data warehouse database (DWDB) tables. Further, embodiments compute, using the data of the DWDB tables, calculate the minimum, maximum, mean and standard deviation (SD) for the user count and the CPU and memory usage, and update the DWDB tables based on the minimum, maximum, and SD values per customer.

Abstract: According to a computer-implemented method, a first virtual machine (VM) is deployed on a first hypervisor from a non-clustered server pool to run a workload of one or more applications. A dummy VM is configured on a second hypervisor from the non-clustered server pool to reserve same resources as the first VM without powering the dummy VM. The first VM is powered with a cold start on the second hypervisor using the resources on the dummy VM. Also, the first VM is provided with a same VM configuration on the second hypervisor that was on the first hypervisor.

Abstract: According to a computer-implemented method, a first virtual machine (VM) is deployed on a first hvpervisor from a non-clustered server pool to run a workload of one or more applications. A dummy VM is configured on a second hypervisor from the non-clustered server pool to reserve same resources as the first VM without powering the dummy VM. The first VM is powered with a cold start on the second hypervisor using the resources on the dummy VM. Also, the first VM is provided with a same VM configuration on the second hypervisor that was on the first hypervisor.