Abstract: Embodiments of the present invention provide an approach for optimizing a configuration of virtual machine (VM) instances. In a typical embodiment, such optimization comprises either the splitting of a single VM instance into multiple VM instances or the consolidation of multiple VM instances into fewer (e.g., a single) VM instance. Along these lines, it will first be determined which VM instances would be good candidates for reconfiguration. Under one approach, VM instances that are candidates for reconfiguration are identified based upon an analysis of applicable/associated service level agreement (SLA) terms versus the performance of the VM instances. For example, VM instances can be reconfigured if such reconfiguration will maximize a benefit provided by the applicable SLA terms (e.g., if the splitting of a single VM instance into multiple VM instances would cause a workload to be processed more efficiently, resulting in a more favorable cost/benefit ratio).

Abstract: Embodiments of the present invention provide an approach for optimizing a configuration of virtual machine (VM) instances. In a typical embodiment, such optimization comprises either the splitting of a single VM instance into multiple VM instances or the consolidation of multiple VM instances into fewer (e.g., a single) VM instance. Along these lines, it will first be determined which VM instances would be good candidates for reconfiguration. Under one approach, VM instances that are candidates for reconfiguration are identified based upon an analysis of applicable/associated service level agreement (SLA) terms versus the performance of the VM instances. For example, VM instances can be reconfigured if such reconfiguration will maximize a benefit provided by the applicable SLA terms (e.g., if the splitting of a single VM instance into multiple VM instances would cause a workload to be processed more efficiently, resulting in a more favorable cost/benefit ratio).

Abstract: Embodiments of the present invention provide an approach for configuring virtual machine (VM) images in a networked computing environment (e.g., a cloud computing environment). Specifically, in a typical embodiment, a provided VM image is associated with a set of competing VM images (e.g., via a web-based portal). Once the competing VM image(s) are associated with the provided VM image, a designation of a set of statistical actions can be designated for the provided VM image. Thereafter, a performance of the provided VM image and a performance of each of the set of competing VM images are monitored with respect to a set of predetermined attributes. This allows the performance of the provided VM image to be compared to the performance of each of the set of corresponding VM images. Based on the comparison, at least one of the set of statistical actions can be performed.

Abstract: Embodiments of the present invention provide an approach for determining starting values of attributes for a requested virtual machine. Specifically, in a typical embodiment, a request for a virtual machine is received from a requester. An image corresponding to the virtual machine is then obtained. A set of statistics pertaining to at least one previous operation of a related virtual machine is analyzed and used to recommend starting values for a set of attributes of the virtual machine (e.g., a processor speed, a memory capacity, a disk capacity, a network capacity, etc.). The starting values can be determined by selecting a graphical curve, which depicts attribute values versus time, pertaining to a particular attribute and then providing recommended the starting values for the attribute based on a position along the graphical curve as determined based upon the analysis of the set of statistics. Thereafter, an instance of the virtual machine that has the starting values can be generated based on the image.

Abstract: Embodiments of the present invention provide an approach for identifying optimal virtual machine (VM) images in a networked computing environment (e.g., a cloud computing environment). Specifically, in a typical embodiment, a set of system requirements, a profile, and a performance state of the networked computing environment are received as input and analyzed against a library of VM images. Based on the analysis, a set of VM images having software programs (e.g., also referred to herein as a software stack) capable of accommodating requirements defined by the input is identified. A requester can select one or more of the identified VM images, which can then be provisioned/deployed accordingly.

Abstract: Embodiments of the present invention provide an approach for optimizing a configuration of virtual machine (VM) instances. In a typical embodiment, such optimization comprises either the splitting of a single VM instance into multiple VM instances or the consolidation of multiple VM instances into fewer (e.g., a single) VM instance. Along these lines, it will first be determined which VM instances would be good candidates for reconfiguration. Under one approach, VM instances that are candidates for reconfiguration are identified based upon an analysis of applicable/associated service level agreement (SLA) terms versus the performance of the VM instances. For example, VM instances can be reconfigured if such reconfiguration will maximize a benefit provided by the applicable SLA terms (e.g., if the splitting of a single VM instance into multiple VM instances would cause a workload to be processed more efficiently, resulting in a more favorable cost/benefit ratio).

Abstract: Embodiments of the present invention provide an approach for configuring virtual machine (VM) images in a networked computing environment (e.g., a cloud computing environment). Specifically, in a typical embodiment, a provided VM image is associated with a set of competing VM images (e.g., via a web-based portal). Once the competing VM image(s) are associated with the provided VM image, a designation of a set of statistical actions can be designated for the provided VM image. Thereafter, a performance of the provided VM image and a performance of each of the set of competing VM images are monitored with respect to a set of predetermined attributes. This allows the performance of the provided VM image to be compared to the performance of each of the set of corresponding VM images. Based on the comparison, at least one of the set of statistical actions can be performed.

Abstract: Embodiments of the present invention provide an approach for identifying optimal virtual machine (VM) images in a networked computing environment (e.g., a cloud computing environment). Specifically, in a typical embodiment, a set of system requirements, a profile, and a performance state of the networked computing environment are received as input and analyzed against a library of VM images. Based on the analysis, a set of VM images having software programs (e.g., also referred to herein as a software stack) capable of accommodating requirements defined by the input is identified. A requester can select one or more of the identified VM images, which can then be provisioned/deployed accordingly.

Abstract: Embodiments of the present invention provide an approach for determining starting values of attributes for a requested virtual machine. Specifically, in a typical embodiment, a request for a virtual machine is received from a requester. An image corresponding to the virtual machine is then obtained. A set of statistics pertaining to at least one previous operation of a related virtual machine is analyzed and used to recommend starting values for a set of attributes of the virtual machine (e.g., a processor speed, a memory capacity, a disk capacity, a network capacity, etc.). The starting values can be determined by selecting a graphical curve, which depicts attribute values versus time, pertaining to a particular attribute and then providing recommended the starting values for the attribute based on a position along the graphical curve as determined based upon the analysis of the set of statistics. Thereafter, an instance of the virtual machine that has the starting values can be generated based on the image.