Abstract: Mechanisms are provided for identifying risky user entitlements in an identity and access management (IAM) computing system. A self-learning peer group analysis (SLPGA) engine receives an IAM data set which specifies user attributes of users of computing resources and entitlements allocated to the users for accessing the computing resources. The SLPGA engine generates a user-entitlement matrix, performs a machine learning matrix decomposition operation on the user-entitlement matrix to identify excessive entitlement allocations, and performs a conditional entropy analysis of the user attributes and entitlements in the IAM data set to identify a set of user attributes for defining peer groups. The SLPGA engine performs a commonality analysis of user attributes and entitlements for each of one or more peer groups defined based on the set of user attributes, and identifies outlier entitlements based on the identification of the excessive entitlement allocations and results of the commonality analysis.

Abstract: Mechanisms are provided for identifying risky user entitlements in an identity and access management (IAM) computing system. A self-learning peer group analysis (SLPGA) engine receives an IAM data set which specifies user attributes of users of computing resources and entitlements allocated to the users for accessing the computing resources. The SLPGA engine generates a user-entitlement matrix, performs a machine learning matrix decomposition operation on the user-entitlement matrix to identify excessive entitlement allocations, and performs a conditional entropy analysis of the user attributes and entitlements in the IAM data set to identify a set of user attributes for defining peer groups. The SLPGA engine performs a commonality analysis of user attributes and entitlements for each of one or more peer groups defined based on the set of user attributes, and identifies outlier entitlements based on the identification of the excessive entitlement allocations and results of the commonality analysis.

Abstract: In response to a performance parameter of a virtual machine (VM) exceeding a threshold, an operation to be performed relative to the VM is identified. A resource requirement of performing the operation is determined using a resource requirement thresholds hierarchy. The resource requirement thresholds hierarchy is usable for computing the resource requirement of the operation when the operation is performed at a specified level in a hierarchy within a virtualized environment where the VM is executing. A violation value resulting from performing the operation is determined using a violation value thresholds hierarchy. If the resource requirement and the violation value do not exceed a resource requirement threshold and a violation value threshold respectively, a first configuration of the VM is revised to a second configuration of the VM. The second configuration includes a result of performing the operation relative to the VM at the specified level in the hierarchy.

Abstract: In response to a performance parameter of a virtual machine (VM) exceeding a threshold, an operation to be performed relative to the VM is identified. A resource requirement of performing the operation is determined using a resource requirement thresholds hierarchy. The resource requirement thresholds hierarchy is usable for computing the resource requirement of the operation when the operation is performed at a specified level in a hierarchy within a virtualized environment where the VM is executing. A violation value resulting from performing the operation is determined using a violation value thresholds hierarchy. If the resource requirement and the violation value do not exceed a resource requirement threshold and a violation value threshold respectively, a first configuration of the VM is revised to a second configuration of the VM. The second configuration includes a result of performing the operation relative to the VM at the specified level in the hierarchy.

Abstract: A virtual machine placement framework is described to enable a data center operator to develop a placement scheme to satisfy its particular constraints while simultaneously optimizing resource utilization. To generate a placement solution, the virtual machine placement problem is first characterized as a “bin packing” problem. The framework provides simple interface tools and processing modules, and a pluggable architecture for receiving placement algorithms. To generate a solution, an administrator creates an XML representation that abstracts physical entities (e.g., data center, subnet, rack, physical server, and the like) into a hierarchical tree of bins. The administrator also defines a set of “rules” that govern (direct) the placement of the virtual machines by placing constraints on the placement scheme. Using the hierarchical tree and the rules, the framework is executed to generate a placement as a solution to a bin packing problem, preferably on a layer-by-layer basis.

Abstract: In response to a performance parameter of a virtual machine (VM) exceeding a threshold, an operation to be performed relative to the VM is identified. A resource requirement of performing the operation is determined using a resource requirement thresholds hierarchy. The resource requirement thresholds hierarchy is usable for computing the resource requirement of the operation when the operation is performed at a specified level in a hierarchy within a virtualized environment where the VM is executing. A violation value resulting from performing the operation is determined using a violation value thresholds hierarchy. If the resource requirement and the violation value do not exceed a resource requirement threshold and a violation value threshold respectively, a first configuration of the VM is revised to a second configuration of the VM. The second configuration includes a result of performing the operation relative to the VM at the specified level in the hierarchy.

Abstract: In response to a performance parameter of a virtual machine (VM) exceeding a threshold, an operation to be performed relative to the VM is identified. A resource requirement of performing the operation is determined using a resource requirement thresholds hierarchy. The resource requirement thresholds hierarchy is usable for computing the resource requirement of the operation when the operation is performed at a specified level in a hierarchy within a virtualized environment where the VM is executing. A violation value resulting from performing the operation is determined using a violation value thresholds hierarchy. If the resource requirement and the violation value do not exceed a resource requirement threshold and a violation value threshold respectively, a first configuration of the VM is revised to a second configuration of the VM. The second configuration includes a result of performing the operation relative to the VM at the specified level in the hierarchy.

Abstract: A virtual machine placement framework is described to enable a data center operator to develop a placement scheme to satisfy its particular constraints while simultaneously optimizing resource utilization. To generate a placement solution, the virtual machine placement problem is first characterized as a “bin packing” problem. The framework provides simple interface tools and processing modules, and a pluggable architecture for receiving placement algorithms. To generate a solution, an administrator creates an XML representation that abstracts physical entities (e.g., data center, subnet, rack, physical server, and the like) into a hierarchical tree of bins. The administrator also defines a set of “rules” that govern (direct) the placement of the virtual machines by placing constraints on the placement scheme. Using the hierarchical tree and the rules, the framework is executed to generate a placement as a solution to a bin packing problem, preferably on a layer-by-layer basis.

Abstract: A method, system, and computer program product for user interface for dynamic workflow state management are provided in the illustrative embodiments. A set of steps of a workflow is presented as a set of tabs in a graphical user interface (UI). Each tab includes a visual indicator indicating a status of a corresponding step associated with that tab. Tor a step in the set of steps, a visual indicator in a tab is used to depict a status of the step in the workflow, the visual indicator visually changing with a change in the status of the step. A status update is received for the step. A determination is made whether the status update also includes a status update for a related step in the workflow, A visual indicator corresponding to the related step is changed in accordance with the status update for the related step.