Abstract: Embodiments of the invention provide a method, a system and a computer program product configured to automatically auto-scale a user compute instance to multiple cloud providers while considering a multiplicity of user requirements. The method, executed on a digital data processor, includes obtaining information, via a user interface, that is descriptive of user cloud computing related preferences, including a user cloud computing budgetary preference. The method further includes sensing properties of a plurality of clouds and making decisions, based at least on the obtained information and on the sensed properties, of when to scale up or scale down the user cloud instance, of selecting one of the plurality of clouds as where to scale the user cloud instance, and determining which resource or resources of the selected cloud to add or remove from the selected cloud. The method further includes automatically executing the decisions on the selected cloud.

Abstract: Using a metadata of a layer, a prediction factor including a level of participation of the layer in a set of container images is computed. Each container image includes a corresponding set of layers and is usable to configure a container in a container-based virtualized data processing environment. Using a set of levels of participation corresponding to a set of layers, and using a condition in a prediction algorithm, a subset of layers that have to be pre-provisioned at a node is predicted. The subset of layers is adjusted, to form an adjusted subset of layers, by looking ahead at a container requirement of a workload that is planned for processing at a future time. The adjusted subset of layers is caused to be provisioned on the node prior to the future time.

Abstract: Embodiments of the invention provide a method, a system and a computer program product configured to automatically auto-scale a user compute instance to multiple cloud providers while considering a multiplicity of user requirements. The method, executed on a digital data processor, includes obtaining information, via a user interface, that is descriptive of user cloud computing related preferences, including a user cloud computing budgetary preference. The method further includes sensing properties of a plurality of clouds and making decisions, based at least on the obtained information and on the sensed properties, of when to scale up or scale down the user cloud instance, of selecting one of the plurality of clouds as where to scale the user cloud instance, and determining which resource or resources of the selected cloud to add or remove from the selected cloud. The method further includes automatically executing the decisions on the selected cloud.

Abstract: Disclosed is a novel system and method for managing requests for an additional virtual machine. The method begins with operating at least one virtual machine accessing at least one computer resource associated with at least one physical machine within a computing cluster. One or more non-deterministic virtual machine requests for the computer resource are received. An over-utilization of the computer resource as a probability distribution function is modeled. In one example, the probability distribution function is a Beta distribution function to represent a one of a plurality of probability distribution functions. Next, an additional virtual machine on the physical machine associated with the computer resource is added in response to a probability of a utilization of the computer resource being greater than a probalistic bound on the over-utilization of the computer resource. Otherwise, the additional virtual machine is not added.

Abstract: There are provided a system, a method and a computer program product for operating a cloud computing infrastructure. In one embodiment, the system and method performs allocation domain modeling and provides a cloud scheduler framework that takes as input desired optimization objectives and the workload constraints and efficiently produces a placement solution that satisfies the constraints while optimizing the objectives in a way that adjusts itself depending on the objectives. As the objectives change, e.g., due to actions from system administrators or due to changes in business policies, the system optimizes itself accordingly and still produces efficient and optimized placement solutions. The system and method constructs an Allocation Domain (AD) that is a particular facet for allocating a logical entity to a physical entity. An AD is created using: variables, functional definitions (functions of variables), and a policy specification that includes a Boolean expression (of the functional definitions).

Abstract: A system, method and computer program product for detecting data omissions between intermittently-connected devices. An example system includes physical computing resources available for utilization. A placement server communicates with a client. The client seeks to use a portion of the computer resources to execute the software product instances. The placement server receives resource utilization parameters from the client and assigns the portion of the computer resources to the software product instances based on the resource utilization parameters and the physical computing resources available for utilization. The resource utilization parameters include specification of a hierarchal arrangement of the software product instances.

Abstract: A system, method and computer program product for detecting data omissions between intermittently-connected devices. An example system includes physical computing resources available for utilization. A placement server communicates with a client. The client seeks to use a portion of the computer resources to execute the software product instances. The placement server receives resource utilization parameters from the client and assigns the portion of the computer resources to the software product instances based on the resource utilization parameters and the physical computing resources available for utilization. The resource utilization parameters include specification of a hierarchal arrangement of the software product instances.

Abstract: Systems and methods for managing a system of heterogeneous workloads are provided. Work that enters the system is separated into a plurality of heterogeneous workloads. A plurality of high-level quality of service goals is gathered. At least one of the plurality of high-level quality of service goals corresponds to each of the plurality of heterogeneous workloads. A plurality of control functions are determined that are provided by virtualizations on one or more containers in which one or more of the plurality of heterogeneous workloads run. An expected utility of a plurality of settings of at least one of the plurality of control functions is determined in response to the plurality of high-level quality of service goals. At least one of the plurality of control functions is exercised in response to the expected utility to effect changes in the behavior of the system.

Abstract: Disclosed is a novel system and method for managing requests for an additional virtual machine. The method begins with operating at least one virtual machine accessing at least one computer resource associated with at least one physical machine within a computing cluster. One or more non-deterministic virtual machine requests for the computer resource are received. An over-utilization of the computer resource as a probability distribution function is modeled. In one example, the probability distribution function is a Beta distribution function to represent a one of a plurality of probability distribution functions. Next, an additional virtual machine on the physical machine associated with the computer resource is added in response to a probability of a utilization of the computer resource being greater than a probalistic bound on the over-utilization of the computer resource. Otherwise, the additional virtual machine is not added.

Abstract: Disclosed is a novel system and method for managing requests for an additional virtual machine. The method begins with operating at least one virtual machine accessing at least one computer resource associated with at least one physical machine within a computing cluster. One or more non-deterministic virtual machine requests for the computer resource are received. An over-utilization of the computer resource as a probability distribution function is modeled. In one example, the probability distribution function is a Beta distribution function to represent a one of a plurality of probability distribution functions. Next, an additional virtual machine on the physical machine associated with the computer resource is added in response to a probability of a utilization of the computer resource being greater than a probabilistic bound on the over-utilization of the computer resource. Otherwise, the additional virtual machine is not added.

Abstract: There are provided a system, a method and a computer program product for operating a cloud computing infrastructure. In one embodiment, the system and method performs allocation domain modeling and provides a cloud scheduler framework that takes as input desired optimization objectives and the workload constraints and efficiently produces a placement solution that satisfies the constraints while optimizing the objectives in a way that adjusts itself depending on the objectives. As the objectives change, e.g., due to actions from system administrators or due to changes in business policies, the system optimizes itself accordingly and still produces efficient and optimized placement solutions. The system and method constructs an Allocation Domain (AD) that is a particular facet for allocating a logical entity to a physical entity. An AD is created using: variables, functional definitions (functions of variables), and a policy specification that includes a Boolean expression (of the functional definitions).

Abstract: There are provided a method for operating a cloud computing infrastructure. In one embodiment, the method performs allocation domain modeling and provides a cloud scheduler framework that takes as input desired optimization objectives and the workload constraints and efficiently produces a placement solution that satisfies the constraints while optimizing the objectives in a way that adjusts itself depending on the objectives. As the objectives change, e.g., due to actions from system administrators or due to changes in business policies, the system optimizes itself accordingly and still produces efficient and optimized placement solutions. The method constructs an Allocation Domain (AD) that is a particular facet for allocating a logical entity to a physical entity. An AD is created using: variables, functional definitions (functions of variables), and a policy specification that includes a Boolean expression (of the functional definitions).

Abstract: Embodiments of the invention provide a method, a system and a computer program product configured to automatically auto-scale a user compute instance to multiple cloud providers while considering a multiplicity of user requirements. The method, executed on a digital data processor, includes obtaining information, via a user interface, that is descriptive of user cloud computing related preferences, including a user cloud computing budgetary preference. The method further includes sensing properties of a plurality of clouds and making decisions, based at least on the obtained information and on the sensed properties, of when to scale up or scale down the user cloud instance, of selecting one of the plurality of clouds as where to scale the user cloud instance, and determining which resource or resources of the selected cloud to add or remove from the selected cloud. The method further includes automatically executing the decisions on the selected cloud.

Abstract: Embodiments of the invention provide a method, a system and a computer program product configured to automatically auto-scale a user compute instance to multiple cloud providers while considering a multiplicity of user requirements. The method, executed on a digital data processor, includes obtaining information, via a user interface, that is descriptive of user cloud computing related preferences, including a user cloud computing budgetary preference. The method further includes sensing properties of a plurality of clouds and making decisions, based at least on the obtained information and on the sensed properties, of when to scale up or scale down the user cloud instance, of selecting one of the plurality of clouds as where to scale the user cloud instance, and determining which resource or resources of the selected cloud to add or remove from the selected cloud. The method further includes automatically executing the decisions on the selected cloud.

Abstract: Placing an application on a private portion and a public portion of a hybrid computing environment for processing. An application may be received for placement and processing. A primary processing objective and a split preference of the application may be determined. The split preference indicates whether the application can be processed using one or both of the private portion and the public portion of the hybrid computing environment. The application may be placed on one or both of the private portion and the public portion of the hybrid computing environment for processing, based on the primary processing objective and based on the split preference.

Abstract: Techniques for managing a system of heterogeneous applications are provided. A plurality of applications is classified into a plurality of application types. One or more of the plurality of applications in each of the plurality of application types are classified into one or more collections. A utility function of possible resource allocations is computed for each of the one or more collections. An application placement is computed that optimizes a global utility of the plurality of applications in accordance with the one or more utility functions. Placement and resource allocation of the plurality of applications are modified in the system in accordance with the application placement.

Abstract: Various embodiments select at least one service provider from a plurality of service providers in a computing environment to satisfy at least one service request. In one embodiment, a service request is received from a user. The service request includes at least a set of service requirements to be satisfied by at least one service provider. A satisfaction level is predicted for each of a plurality of service providers with respect to each of the set of service requirements. The prediction is based on a prediction satisfaction model associated with each of the plurality of service providers. At least one service provider is selected from the plurality of service providers for satisfying the service request based on the satisfaction level predicted for each of the plurality of service providers.

Abstract: Systems and methods for admission control to a physical host system are provided herein. One aspect provides for receiving at least one resource request at an admission control component of a distributed computing system, the at least one resource request comprised of at least one system type; processing the at least one resource request utilizing at least one physical host accessible to the distributed computing system; specifying a number of resource request slots to be reserved for at least one system type based on at least one future reservation threshold accessible to the admission control component; and blocking resource requests from entering the system through the admission control component based on a number of available resource request slots and the at least one future reservation threshold. Other embodiments and aspects are also described herein.

Abstract: A method for use in a system in which computational entities are distributed across physical computing resources to place the entities on the resources includes estimating actual resource demand for each entity on each resource based on application resource usage data collected from a data source external from the entity, computing a best allocation of the resources to the entities from the estimated actual resource demand for each entity and distributing the resources to the entities in accordance with the computed best allocation.

Abstract: A technique is provided for creating virtual units in a computing environment. A virtual system definition is received by a processor that is utilized to create the virtual units for a virtual system. Relationship constraints between the virtual units in the virtual system are received by the processor. The relationship constraints between the virtual units include a communication link requirement between the virtual units and/or a location requirement between the virtual units. The virtual units in the virtual system are deployed by the processor according to the relationship constraints between virtual units.