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: 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: A system and method for supporting a structural foundation supported by a subterranean earth strata. A plurality of support piling segments are disposed in a columnar array beneath the foundation. Each segment has a central channel for receiving a tension or reinforcing member. The distal end of the tension member is anchored beneath the first or lowermost segment in the array. The anchor is sized such that it may not be withdrawn or pulled up through the channels in the segments. After the desired depth of the column is reached, a tension adjustment mechanism is affixed to the proximal end of the tension or reinforcing member and tightened to provide additional compressive force and strength to the columnar array.