Abstract: A system and method of selectively migrating at least one virtual machine to another physical machine is disclosed. Consumption information of a plurality of virtual machines and capacity information of a plurality of physical machines is retrieved. A first virtual machine to be potentially migrated is identified based on a consumption metric representing consumption of at least one resource dimension by the first virtual machine running on a first physical machine. A second physical machine to potentially accept migration of the first virtual machine is identified based on time window information, wherein an available capacity of the second physical machine is analyzed to determine if the first virtual machine's resource consumption exceeds the capacity of the second physical machine. The first virtual machine is migrated to the second physical machine if the first virtual machine's resource consumption does not exceed the available capacity of the second physical machine.

Abstract: Systems and methods are disclosed herein to automatically replicate virtual machine image (VM) files on secondary VM computing devices, from a primary VM computing device. The secondary VM computing devices are automatically selected by constantly reviewing the operating parameter values (e.g., cost of resources, power consumption, etc.) of a number of secondary VM computing devices available of storing VM image replicas. The replica of the primary VM image is stored in the secondary VM computing devices in geographically disparate cloud locations. The primary VM image is automatically broken into constituent data blocks stored in an active index, which is compared against a stale index of data blocks. When an update is detected in the primary VM image, the comparison of indices will indicate that there is new data. Only the new data is used to update the secondary VM images, thereby reducing network traffic and latency issues.

Abstract: Systems, methods, and computer-readable code for processing queries for XML twigs using bottom-up processing include: receiving, by a computing device, a query XML twig including at least one sub-twig; determining whether an element in an XML tree is a root element of at least one sub-twig of the query XML twig, evaluating an arbitrary Boolean operation in the at least one sub-twig; populating a logic block if the element is a root element of the at least one sub-twig; inserting the element into an appropriate stack of a hierarchical stack if the element is not a root element of the at least one sub-twig; evaluating whether the element contributes to finding results of the query; discarding the element if the element does not contribute to finding results of the query; and determining whether the element is the root of the query XML twig.

Abstract: Methods and systems for monitoring and controlling a service level agreement are disclosed. A disclosed embodiment comprises a cloud platform hosting at least one application associated with at least one service level agreement, a monitoring module for collecting at least one metric relating to an application hosted on the cloud platform and terms of a service level agreement associated with each of the one or more metrics, and monitoring the metric collected against a service level objective in the service level agreement. The system may additionally comprise a controlling module for mapping a service level objective to a predefined operation rule, identifying one or more actions to be taken in accordance with the rule, assigning a priority to the one or more actions identified in accordance with the rule; and performing the one or more actions in accordance with the assigned priority of each of the one or more actions.

Abstract: Systems and methods for deriving a service level agreement for an application hosted on a cloud platform are defined. In accordance with at least one embodiment, deriving the service level agreement comprises packaging the application for deployment on a cloud platform, executing the packaged application in a sandboxed environment and capturing one or more application performance characteristics thereby, executing the packaged application in a sandboxed virtualized platform and further capturing one or more application performance characteristics thereby, mapping the one or more captured application performance characteristics to one or more service level objectives, and deriving a service level agreement on the basis of the one or more service level objectives, wherein the service level agreement comprises at least one of the one or more service level objectives.

Abstract: A system and method of selectively consolidating applications on a machine comprises retrieving resource utilization data for a plurality of applications over a plurality of cycles for designated resource dimensions. A representative utilization profile is calculated on each application for each resource dimension. Load mean and variance values of the applications are calculated for each resource dimension. Each application is assigned to a class based the mean and variance values. A consolidation algorithm is computed on each application for each resource dimension to determine if resource consumption of the consolidated applications potentially exceed an available capacity in a machine.

Abstract: Methods and systems for monitoring and controlling a service level agreement are disclosed. A disclosed embodiment comprises a cloud platform hosting at least one application associated with at least one service level agreement, a monitoring module for collecting at least one metric relating to an application hosted on the cloud platform and terms of a service level agreement associated with each of the one or more metrics, and monitoring the metric collected against a service level objective in the service level agreement. The system may additionally comprise a controlling module for mapping a service level objective to a predefined operation rule, identifying one or more actions to be taken in accordance with the rule, assigning a priority to the one or more actions identified in accordance with the rule; and performing the one or more actions in accordance with the assigned priority of each of the one or more actions.

Abstract: A system and method of selectively migrating at least one virtual machine to another physical machine is disclosed. Consumption information of a plurality of virtual machines and capacity information of a plurality of physical machines is retrieved. A first virtual machine to be potentially migrated is identified based on a consumption metric representing consumption of at least one resource dimension by the first virtual machine running on a first physical machine. A second physical machine to potentially accept migration of the first virtual machine is identified based on time window information, wherein an available capacity of the second physical machine is analyzed to determine if the first virtual machine's resource consumption exceeds the capacity of the second physical machine. The first virtual machine is migrated to the second physical machine if the first virtual machine's resource consumption does not exceed the available capacity of the second physical machine.

Abstract: Computing resources can be assigned to sub-plans within a query plan to effect parallel execution of the query plan. For example, computing resources in a grid can be represented by nodes, and a shortest path technique can be applied to allocate machines to the sub-plans. Computing resources can be provisionally allocated as the query plan is divided into query plan segments containing one or more sub-plans. Based on provisional allocations to the segments, the computing resources can then be allocated to the sub-plans within respective segments. Multiprocessor computing resources can be supported. The techniques can account for data locality. Both pipelined and partitioned parallelism can be addressed. Described techniques can be particularly suited for efficient execution of bushy query plans in a grid environment. Parallel processing will reduce the overall response time of the query.

Abstract: A system and method for consolidating a plurality of resources on a plurality of data processing units (DPUs) in a data processing network is provided. The method includes selecting a first DPU having a highest DPU volume among the plurality of DPUs. The method further includes calculating an angle made by a first DPU vector of the first DPU with a horizontal dimension of a multi-dimensional chart using first DPU co-ordinates from the plurality of DPU co-ordinates. Further, the method includes calculating an angle made by a first resource vector of a first resource to be assigned to the first DPU with a horizontal dimension of the multi-dimensional chart. Thereafter, the first resource is assigned to the first DPU, when a deviation of the first resource vector from the first DPU vector is minimum among the plurality of resources.

Abstract: A system and method of selectively consolidating applications on a machine comprises retrieving resource utilization data for a plurality of applications over a plurality of cycles for designated resource dimensions. A representative utilization profile is calculated on each application for each resource dimension. Load mean and variance values of the applications are calculated for each resource dimension. Each application is assigned to a class based the mean and variance values. A consolidation algorithm is computed on each application for each resource dimension to determine if resource consumption of the consolidated applications potentially exceed an available capacity in a machine.

Abstract: Systems, methods, and computer-readable code for processing queries for XML twigs using bottom-up processing include: receiving, by a computing device, a query XML twig including at least one sub-twig; determining whether an element in an XML tree is a root element of at least one sub-twig of the query XML twig, evaluating an arbitrary Boolean operation in the at least one sub-twig; populating a logic block if the element is a root element of the at least one sub-twig; inserting the element into an appropriate stack of a hierarchical stack if the element is not a root element of the at least one sub-twig; evaluating whether the element contributes to finding results of the query; discarding the element if the element does not contribute to finding results of the query; and determining whether the element is the root of the query XML twig.

Abstract: Systems and methods are disclosed herein to automatically replicate and migrate live virtual machine image (VM) files from a primary VM computing device to secondary VM computing devices. The operating parameters (e.g., cost of operation, power consumption, etc.) of a number of secondary VM computing devices are analyzed. Replicas of the primary VM image is stored in the secondary VM devices with operating parameters that meet limiting parameters defined in an SLA. The primary VM image is indexed by its constituent data blocks in an active index, which is compared against a stale index of data blocks. A comparison of the indices will indicate when new data is added to the VM image. The new data is used to update the replicas. Migration is performed by copying the primary VM image or awakening a hibernated secondary VM image replica, and hibernating the current primary VM image.

Abstract: Systems and methods are disclosed herein to automatically replicate virtual machine image (VM) files on secondary VM computing devices, from a primary VM computing device. The secondary VM computing devices are automatically selected by constantly reviewing the operating parameter values (e.g., cost of resources, power consumption, etc.) of a number of secondary VM computing devices available of storing VM image replicas. The replica of the primary VM image is stored in the secondary VM computing devices in geographically disparate cloud locations. The primary VM image is automatically broken into constituent data blocks stored in an active index, which is compared against a stale index of data blocks. When an update is detected in the primary VM image, the comparison of indices will indicate that there is new data. Only the new data is used to update the secondary VM images, thereby reducing network traffic and latency issues.

Abstract: A method for disseminating Early Warning Messages (EWMs) to subscriber terminals by an Early Warning Unit (EWU) in a mobile communication network is provided. The method comprises identifying at least one base station in a first region and sending a trigger signal to the identified base station for initiating the dissemination of the EWMs. The method further comprises sending an invitation request to one or more peer base stations in a second region and receiving subscriber details from one or more base stations. The method furthermore comprises selectively disseminating the EWMs to the one or more subscriber terminals based on location details of the subscriber terminals using a grid computing architecture. The grid computing architecture facilitates parallel dissemination of the EWMs to the one or more subscriber terminals by splitting the subscriber details into one or more data fragments.

Abstract: Managing power expenditures for hosting computer applications. A smart meter can receive electricity pricing information for a data center or other group of computing resources that host computer applications, such as a cloud computing environment. An application manager to determine how much electricity can be saved by operating the applications at a reduced performance level without compromising performance metrics for the applications. A site broker can determine how to sequence the performance levels of the applications to meet an electricity usage budget or to otherwise reduce electricity consumption or costs, for example during a peak load time period. The site broker can also select one or more applications to migrate to another cloud to meet the electricity usage budget or to reduce electricity consumption or costs. A hybrid cloud broker can interact with the site broker to migrate the selected application(s) to another cloud.

Abstract: A method for disseminating Early Warning Messages (EWMs) to subscriber terminals by an Early Warning Unit (EWU) in a mobile communication network is provided. The method comprises identifying at least one base station in a first region and sending a trigger signal to the identified base station for initiating the dissemination of the EWMs. The method further comprises sending an invitation request to one or more peer base stations in a second region and receiving subscriber details from one or more base stations. The method furthermore comprises selectively disseminating the EWMs to the one or more subscriber terminals based on location details of the subscriber terminals using a grid computing architecture. The grid computing architecture facilitates parallel dissemination of the EWMs to the one or more subscriber terminals by splitting the subscriber details into one or more data fragments.

Abstract: A system and method for consolidating a plurality of resources on a plurality of data processing units (DPUs) in a data processing network is provided. The method includes selecting a first DPU having a highest DPU volume among the plurality of DPUs. The method further includes calculating an angle made by a first DPU vector of the first DPU with a horizontal dimension of a multi-dimensional chart using first DPU co-ordinates from the plurality of DPU co-ordinates. Further, the method includes calculating an angle made by a first resource vector of a first resource to be assigned to the first DPU with a horizontal dimension of the multi-dimensional chart. Thereafter, the first resource is assigned to the first DPU, when a deviation of the first resource vector from the first DPU vector is minimum among the plurality of resources.

Abstract: Computing resources can be assigned to sub-plans within a query plan to effect parallel execution of the query plan. For example, computing resources in a grid can be represented by nodes, and a shortest path technique can be applied to allocate machines to the sub-plans. Computing resources can be provisionally allocated as the query plan is divided into query plan segments containing one or more sub-plans. Based on provisional allocations to the segments, the computing resources can then be allocated to the sub-plans within respective segments. Multiprocessor computing resources can be supported. The techniques can account for data locality. Both pipelined and partitioned parallelism can be addressed. Described techniques can be particularly suited for efficient execution of bushy query plans in a grid environment. Parallel processing will reduce the overall response time of the query.