Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that may cause the processor to determine whether a request is stored in a message queue, in which the apparatus may be inside of a domain and the message queue may be outside of the domain. Based on a determination that a request is stored in the message queue, the processor may pull the request from the message queue through a domain boundary, fulfill the request to cause a response to the request to be generated, and forward the response to the message queue through the domain boundary.

Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that may cause the processor to determine whether a request is stored in a message queue, in which the apparatus may be inside of a domain and the message queue may be outside of the domain. Based on a determination that a request is stored in the message queue, the processor may pull the request from the message queue through a domain boundary, fulfill the request to cause a response to the request to be generated, and forward the response to the message queue through the domain boundary.

Abstract: This disclosure sets forth systems and methods for recommending candidate computing platforms for migration of data and data-related workload from an original computing platform. The systems and methods further describe determining recommendations of candidate computing platforms based on a comparison of key performance and utilization statistics of the original computing platform under a user-generated workload with candidate computing platforms under a synthetic workload. Key performance and utilization statistics may relate to CPU, memory, file I/O, network I/O, and database I/O operations on the respective computing platforms. The synthetic workload may be defined by parameters that simulate the key performance and utilization statistics of the original computing platform under the user-generated workload. Further, the synthetic workloads may be executed on individual candidate computing platforms to determine service level capabilities that are ultimately used to form the recommendation.

Abstract: This disclosure sets forth systems and methods for recommending candidate computing platforms for migration of data and data-related workload from an original computing platform. Recommendations of candidate computing platforms may be based on a comparison of key performance and utilization statistics of the original computing platform under a user-generated workload with candidate computing platforms under a synthetic workload. Key performance and utilization statistics may relate to CPU, memory, file I/O, network I/O, and database I/O operations on the respective computing platforms. The synthetic workload may be defined by parameters that simulate the key performance and utilization statistics of the original computing platform under the user-generated workload. Further, the synthetic workloads may be executed on individual candidate computing platforms to determine service level capabilities that are ultimately used to form the recommendation.

Abstract: This disclosure sets forth systems and methods for recommending candidate computing platforms for migration of data and data-related workload from an original computing platform. The systems and methods further describe determining recommendations of candidate computing platforms based on a comparison of key performance and utilization statistics of the original computing platform under a user-generated workload with candidate computing platforms under a synthetic workload. Key performance and utilization statistics may relate to CPU, memory, file I/O, network I/O, and database I/O operations on the respective computing platforms. The synthetic workload may be defined by parameters that simulate the key performance and utilization statistics of the original computing platform under the user-generated workload. Further, the synthetic workloads may be executed on individual candidate computing platforms to determine service level capabilities that are ultimately used to form the recommendation.