Abstract: A computer-implemented method includes: receiving, by a computing device, a template for creating a virtual machine (VM) instance; separating, by the computing device, the template into a repeated portion and a unique portion; determining, by the computing device, whether the repeated portion is stored in a cache; creating, by the computing device and based on determining that the repeated portion is stored in the cache, the VM instance using the repeated portion stored in the cache; completing, by the computing device, the unique portion of the VM instance to create a completed VM instance; and deploying, by the computing device, the completed VM instance.

Abstract: A computer-implemented method includes: receiving, by a computing device, a template for creating a virtual machine (VM) instance; separating, by the computing device, the template into a repeated portion and a unique portion; determining, by the computing device, whether the repeated portion is stored in a cache; creating, by the computing device and based on determining that the repeated portion is stored in the cache, the VM instance using the repeated portion stored in the cache; completing, by the computing device, the unique portion of the VM instance to create a completed VM instance; and deploying, by the computing device, the completed VM instance.

Abstract: A mechanism is provided for implementing operation optimization. Responsive to a request to load data via a input/output (I/O) load operation, prior to loading the data, transformed data in a local memory is searched for a match to the requested data. Responsive to identifying transformed that matches the requested data, the transformed data in the local memory is used to fulfill the request. Responsive to failing to identify transformed data in the local memory that matches the requested data, untransformed data in the local memory is searched for a match to the requested data. Responsive to identifying untranstormed data that matches the requested data, the untransformed data in the local memory is used to fulfill the request. Responsive to failing to identify untransformed data in the local memory that matches the requested data, the requested data may be loaded from the storage system via the I/O load operation.

Abstract: A mechanism is provided for implementing operation optimization. Responsive to a request to load data via a input/output (I/O) load operation, prior to loading the data, transformed data in a local memory is searched for a match to the requested data. Responsive to identifying transformed that matches the requested data, the transformed data in the local memory is used to fulfill the request. Responsive to failing to identify transformed data in the local memory that matches the requested data, untransformed data in the local memory is searched for a match to the requested data. Responsive to identifying untransformed data that matches the requested data, the untransformed data in the local memory is used to fulfill the request. Responsive to failing to identify untransformed data in the local memory that matches the requested data, the requested data may be loaded from the storage system via the I/O load operation.

Abstract: A method for scaling a cloud infrastructure, comprises receiving at least one of resource-level metrics and application-level metrics, estimating parameters of at least one application based on the received metrics, automatically and dynamically determining directives for scaling application deployment based on the estimated parameters, and providing the directives to a cloud service provider to execute the scaling.

Abstract: A method for scaling a cloud infrastructure, comprises receiving at least one of resource-level metrics and application-level metrics, estimating parameters of at least one application based on the received metrics, automatically and dynamically determining directives for scaling application deployment based on the estimated parameters, and providing the directives to a cloud service provider to execute the scaling.

Abstract: Managing data in a cloud computing environment, including data transfers. File level and block level similarities are identified, including for archive and nested archive files, residing on datacenters and regional repositories. A replication plan is generated based on receiving a replication instruction, and further based on similarity clusters by transferring unique data blocks and files from best available sources including regional repositories.

Abstract: A computer-implemented method includes: receiving, by a computing device, a template for creating a virtual machine (VM) instance; separating, by the computing device, the template into a repeated portion and a unique portion; determining, by the computing device, whether the repeated portion is stored in a cache; creating, by the computing device and based on determining that the repeated portion is stored in the cache, the VM instance using the repeated portion stored in the cache; completing, by the computing device, the unique portion of the VM instance to create a completed VM instance; and deploying, by the computing device, the completed VM instance.

Abstract: Methods and systems for data transfer include adding a data chunks to a priority queue in an order based on utilization priority. A reducibility score for the data chunks is determined. A data reduction operation is performed on a data chunk having a highest reducibility in the priority queue using a processor if sufficient resources are available. The data chunk having the lowest reducibility score is moved from the priority queue to a transfer queue for transmission if the transfer queue is not full.

Abstract: Methods and systems for shaping content include determining a degree of compression for a piece of content, using a processor, based on a user's cognitive state and a set of temporal circumstances. The piece of content is modified to constrain its play time in accordance with the degree of compression.

Abstract: Managing data in a cloud computing environment, including data transfers. File level and block level similarities are identified, including for archive and nested archive files, residing on datacenters and regional repositories. A replication plan is generated based on receiving a replication instruction, and further based on similarity clusters by transferring unique data blocks and files from best available sources including regional repositories.

Abstract: Managing data in a cloud computing environment, including data transfers. File level and block level similarities are identified, including for archive and nested archive files, residing on datacenters and regional repositories. A replication plan is generated based on receiving a replication instruction, and further based on similarity clusters by transferring unique data blocks and files from best available sources including regional repositories.

Abstract: A system and method for efficiently transferring virtual machine images across nodes in a cloud computing environment, includes analyzing each image on each node to create hash code clusters and a similarity matrix. An instruction to transfer an image from a source node to a target node is received. The clusters and the similarity matrix are used to determine to what extent the data from the image is already on the source node, or on any other node, and further determines the cost and speed of transferring such data to the target node. An optimal transfer plan is generated, and data that is not already on the target node is transferred to the target node from the most efficient node on which it is available, according to the optimal transfer plan.

Abstract: One embodiment of a system for provisioning a new virtual machine instance on a target host based on an image of the new virtual machine instance includes a local storage for storing images of virtual machine instances currently running on the target host and a virtual machine creator coupled to the local storage for constructing the image of the new virtual machine instance at least in part using the images of virtual machine instances currently running on the target host.

Abstract: A plurality of traditional software applications are packaged as custom composite solutions, and are stored as custom composite solution deployable images. Customer input is obtained from a customer. At least one of the custom composite solution deployable images is deployed into a management cloud in response to the customer input.

Abstract: A method for scaling a cloud infrastructure, comprises receiving at least one of resource-level metrics and application-level metrics, estimating parameters of at least one application based on the received metrics, automatically and dynamically determining directives for scaling application deployment based on the estimated parameters, and providing the directives to a cloud service provider to execute the scaling.

Abstract: A method for scaling a cloud infrastructure, comprises receiving at least one of resource-level metrics and application-level metrics, estimating parameters of at least one application based on the received metrics, automatically and dynamically determining directives for scaling application deployment based on the estimated parameters, and providing the directives to a cloud service provider to execute the scaling.

Abstract: A method for scaling a cloud infrastructure, comprises receiving at least one of resource-level metrics and application-level metrics, estimating parameters of at least one application based on the received metrics, automatically and dynamically determining directives for scaling application deployment based on the estimated parameters, and providing the directives to a cloud service provider to execute the scaling.

Abstract: A method for scaling a cloud infrastructure, comprises receiving at least one of resource-level metrics and application-level metrics, estimating parameters of at least one application based on the received metrics, automatically and dynamically determining directives for scaling application deployment based on the estimated parameters, and providing the directives to a cloud service provider to execute the scaling.

Abstract: A system and method for efficiently transferring virtual machine images across nodes in a cloud computing environment, includes analyzing each image on each node to create hash code clusters and a similarity matrix. An instruction to transfer an image from a source node to a target node is received. The clusters and the similarity matrix are used to determine to what extent the data from the image is already on the source node, or on any other node, and further determines the cost and speed of transferring such data to the target node. An optimal transfer plan is generated, and data that is not already on the target node is transferred to the target node from the most efficient node on which it is available, according to the optimal transfer plan.