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.

Abstract: Cloud computing benchmarking is performed wherein the resource usage of a measuring benchmarking application is compensated for as to not impact measurement. The measurements are of a cloud instance's benchmarking indicia which may include performance, functions and characteristics of the cloud instance. The benchmarking indicia use scalable measures as to allow the use of arithmetic operations such as those used in statistical functions. The benchmarking application is dispatched along with a configuration file and is controlled from a central controller to specified cloud instances. The dispatched benchmarking application takes measurements of the cloud instance based on the configuration file. The benchmarking application then stores the measurements in a results file for return back to the central controller. At the central controller, results files from one or more benchmarking applications are stored in a data store for comparative and statistical analysis.

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. 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: Cloud computing benchmarking is performed wherein the resource usage of a measuring benchmarking application is compensated for as to not impact measurement. The measurements are of a cloud instance's benchmarking indicia which may include performance, functions and characteristics of the cloud instance. The benchmarking indicia use scalable measures as to allow the use of arithmetic operations such as those used in statistical functions. The benchmarking application is dispatched along with a configuration file and is controlled from a central controller to specified cloud instances. The dispatched benchmarking application takes measurements of the cloud instance based on the configuration file. The benchmarking application then stores the measurements in a results file for return back to the central controller. At the central controller, results files from one or more benchmarking applications are stored in a data store for comparative and statistical analysis.