Abstract: The invention provides a method and system for continuous optimization of a data center. The method includes monitoring loads of storage modules, server modules and switch modules in the data center, detecting an overload condition upon a load exceeding a load threshold, combining server and storage virtualization to address storage overloads by planning allocation migration between the storage modules, to address server overloads by planning allocation migration between the server modules, to address switch overloads by planning allocation migration mix between server modules and storage modules for overload reduction, and orchestrating the planned allocation migration to reduce the overload condition in the data center.

Abstract: A method and system for throttling a plurality of operations of a plurality of applications that share a plurality of resources. A difference between observed and predicted workloads is computed. If the difference does not exceed a threshold, a multi-strategy finder operates in normal mode and applies a recursive greedy pruning process with a look-back and look-forward optimization to select actions for a final schedule of actions that improve the utility of a data storage system. If the difference exceeds the threshold, the multi-strategy finder operates in unexpected mode and applies a defensive action selection process to select actions for the final schedule. The selected actions are performed according to the final schedule and include throttling of a CPU, network, and/or storage.

Abstract: An optimization method and system. The method includes receiving by a computing system a data footprint associated with data and a human resource model. The data footprint comprises a primary data section, a secondary data section, and an archive data section. A plurality of data storage strategies are associated with the primary data section, said secondary data section, and said archive data section. The plurality of data storage strategies are compared to each other. A data staging orchestrator software module is executed. The computing system determines based on results of executing the data staging orchestrator software module, an optimal migration time, an optimal migration speed, and an optimal migration cost for managing storage for portions of the data. The computing system executes a risk modulation software module and determines a risk associated with the managing.

Abstract: An optimization method and system. The method includes receiving by a computing system a data footprint associated with data and a human resource model. The data footprint comprises a primary data section, a secondary data section, and an archive data section. A plurality of data storage strategies are associated with the primary data section, said secondary data section, and said archive data section. The plurality of data storage strategies are compared to each other. A data staging orchestrator software module is executed. The computing system determines based on results of executing the data staging orchestrator software module, an optimal migration time, an optimal migration speed, and an optimal migration cost for managing storage for portions of the data. The computing system executes a risk modulation software module and determines a risk associated with the managing.

Abstract: An optimization method and system. The method includes receiving by a computing system a data footprint associated with data and a human resource model. The data footprint comprises a primary data section, a secondary data section, and an archive data section. A plurality of data storage strategies are associated with the primary data section, said secondary data section, and said archive data section. The plurality of data storage strategies are compared to each other. A data staging orchestrator software module is executed. The computing system determines based on results of executing the data staging orchestrator software module, an optimal migration time, an optimal migration speed, and an optimal migration cost for managing storage for portions of the data. The computing system executes a risk modulation software module and determines a risk associated with the managing.

Abstract: A method and system for throttling a plurality of operations of a plurality of applications that share a plurality of resources. A difference between observed and predicted workloads is computed. If the difference does not exceed a threshold, a multi-strategy finder operates in normal mode and applies a recursive greedy pruning process with a look-back and look-forward optimization to select actions for a final schedule of actions that improve the utility of a data storage system. If the difference exceeds the threshold, the multi-strategy finder operates in unexpected mode and applies a defensive action selection process to select actions for the final schedule. The selected actions are performed according to the final schedule and include throttling of a CPU, network, and/or storage.

Abstract: An optimization method and system. The method includes receiving by a computing system a data footprint associated with data and a human resource model. The data footprint comprises a primary data section, a secondary data section, and an archive data section. A plurality of data storage strategies are associated with the primary data section, said secondary data section, and said archive data section. The plurality of data storage strategies are compared to each other. A data staging orchestrator software module is executed. The computing system determines based on results of executing the data staging orchestrator software module, an optimal migration time, an optimal migration speed, and an optimal migration cost for managing storage for portions of the data. The computing system executes a risk modulation software module and determines a risk associated with the managing.

Abstract: The invention provides a method and system for continuous optimization of a data center. The method includes monitoring loads of storage modules, server modules and switch modules in the data center, detecting an overload condition upon a load exceeding a load threshold, combining server and storage virtualization to address storage overloads by planning allocation migration between the storage modules, to address server overloads by planning allocation migration between the server modules, to address switch overloads by planning allocation migration mix between server modules and storage modules for overload reduction, and orchestrating the planned allocation migration to reduce the overload condition in the data center.

Abstract: Prediction methods that anticipate the outcome of a detailed optimization step are used in lieu of or in conjunction with actual optimization to improve response time and reduce required computational resources for optimization problems having a hierarchical structure. Decomposition of the optimization problem into sub-problems and sub-sub-problems is, itself, an optimization process which is iteratively performed while preferably guided by prediction of the quality of solutions to the problems into which the “master” optimization problem may be decomposed. Prediction also reduces the requirements for computational resources and allows more decompositions to be examined within the available time in order to arrive at a more nearly optimal decomposition as well as a more nearly optimal solution. Prediction is selectively used when it is determined that such a benefit is probable.

Abstract: Prediction methods that anticipate the outcome of a detailed optimization step are used in lieu of or in conjunction with actual optimization to improve response time and reduce required computational resources for optimization problems having a hierarchical structure. Decomposition of the optimization problem into sub-problems and sub-sub-problems is, itself, an optimization process which is iteratively performed while preferably guided by prediction of the quality of solutions to the problems into which the “master” optimization problem may be decomposed. Prediction also reduces the requirements for computational resources and allows more decompositions to be examined within the available time in order to arrive at a more nearly optimal decomposition as well as a more nearly optimal solution. Prediction is selectively used when it is determined that such a benefit is probable.

Abstract: A method for effecting computer implemented decision support. The method can improve on a candidate solution by allowing problem solving methods to cooperate towards the creation of a more desirable solution. The method can realize an enhanced understanding of tradeoffs inherent in competing objectives, and can incorporate factors or special considerations not easily specified, by enabling the decision maker to actively participate in the creation of a more desirable solution.