Abstract: A technique for data de-duplication is applied to suspend and resume operations for virtual machines (VMs) to reduce the time needed to suspend or resume a virtual machine and store the suspended VMs in a space-efficient and time-efficient manner. A VM's state is serialized and written to disk in the form of key values that are used to retrieve data from a key-data map. The key-data map stores unique data blocks for suspended VM images. The key-data map can be stored in a solid-state drive to further reduce latency and access time.

Abstract: A technique for data de-duplication is applied to suspend and resume operations for virtual machines (VMs) to reduce the time needed to suspend or resume a virtual machine and store the suspended VMs in a space-efficient and time-efficient manner. A VM's state is serialized and written to disk in the form of key values that are used to retrieve data from a key-data map. The key-data map stores unique data blocks for suspended VM images. The key-data map can be stored in a solid-state drive to further reduce latency and access time.

Abstract: A robust method for solving in a computer, optimization problems under uncertainty including the steps of: specifying the uncertainty as a hierarchical series of sets of constraints on parameters, with the parameters restricted to each constraint set forming an ensemble, and the hierarchy of constraints, represented as mathematical sets forming a hierarchy of ensembles, said hierarchy being based on subset, intersection or disjoint relationships amongst them; utilizing optimization techniques to create effective identify minimum and maximum bounds on the each objective function, said bounds depending on the constraints comprising each ensemble of parameters and being computed for each of the assumptions about the future; estimating a volume of candidate ensembles and relating the volume to one or more information theoretic measures; and utilizing information theoretic measures to analyze and improve the candidate iteratively refine the ensembles and associated by changing a specificity of the bounds on the ob

Abstract: A robust method for solving in a computer, optimization problems under uncertainty including the steps of: specifying the uncertainty as a hierarchical series of sets of constraints on parameters, with the parameters restricted to each constraint set forming an ensemble, and the hierarchy of constraints, represented as mathematical sets forming a hierarchy of ensembles, said hierarchy being based on subset, intersection or disjoint relationships amongst them; utilizing optimization techniques to create effective identify minimum and maximum bounds on the each objective function, said bounds depending on the constraints comprising each ensemble of parameters and being computed for each of the assumptions about the future; estimating a volume of candidate ensembles and relating the volume to one or more information theoretic measures; and utilizing information theoretic measures to analyze and improve the candidate iteratively refine the ensembles and associated by changing a specificity of the bounds on the ob