Abstract: Techniques for estimating runtimes of one or more machine learning tasks are provided. For example, one or more embodiments described herein can regard a system that can comprise a memory that stores computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an extraction component that can extract a parameter from a machine learning task. The parameter can define a performance characteristic of the machine learning task. Also, the computer executable components can comprise a model component that can generate a model based on the parameter. Further, the computer executable components can comprise an estimation component that can generate an estimated runtime of the machine learning task based on the model.

Abstract: Embodiments include methods, systems and computer program products for offloading multiple processing operations to an accelerator. Aspects include receiving a database query from an application, performing an analysis on the query, and identifying a plurality of available accelerators. Aspects further include retrieving cost information for one or more templates available on each of the plurality of available accelerators, determining a query execution plan based on the cost information and the analysis on the query, and offloading one or more query operations to at least one of the plurality of accelerators based on the query execution plan.

Abstract: Embodiments include methods, systems and computer program products a for offloading multiple processing operations to an accelerator includes receiving, by a processing device, a database query from an application. The method also includes performing analysis on the database query and selecting an accelerator template from a plurality of accelerator templates based on the analysis of the database query. The method further includes transmitting an indication of the accelerator template to the accelerator and executing at least a portion of the database query on the accelerator.

Abstract: Methods and arrangements for mapping a query operation to an accelerator are provided. The method includes receiving, by a processor, a query operation and determining the design logic of the query operation, receiving a configuration of one or more available accelerators and a design logic of each of the one or more available accelerators, and determining if the query operation can be offloaded to one or more of the one or more available accelerators. Based on a determination that the query operation can be offloaded to one or more of the one or more available accelerators, the method also includes creating software structures to interface with a selected accelerator and executing the query operation on the selected accelerator. Based on a determination that the query operation cannot be offloaded to one or more of the one or more available accelerators, the method further includes executing the query operation in software.

Abstract: A computer-implemented method includes determining that a database query warrants a first projection operation to project a plurality of input rows to a plurality of projected rows, where each of the plurality of input rows has one or more variable-length columns. A first projection control block is constructed, by a computer processor, to describe the first projection operation. The first projection operation is offloaded to a hardware accelerator. The first projection control block is provided to the hardware accelerator, and the first projection control block enables the hardware accelerator to perform the first projection operation at streaming rate.

Abstract: A data processing system includes a plurality of virtual machines each having associated memory pages; a shared memory page cache that is accessible by each of the plurality of virtual machines; and a global hash map that is accessible by each of the plurality of virtual machines. The data processing system is configured such that, for a particular memory page stored in the shared memory page cache that is associated with two or more of the plurality of virtual machines, there is a single key stored in the global hash map that identifies at least a storage location in the shared memory page cache of the particular memory page. The system can be embodied at least partially in a cloud computing system.

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: Embodiments include methods, systems and computer program products a for offloading multiple processing operations to an accelerator includes receiving, by a processing device, a database query from an application. The method also includes performing analysis on the database query and selecting an accelerator template from a plurality of accelerator templates based on the analysis of the database query. The method further includes transmitting an indication of the accelerator template to the accelerator and executing at least a portion of the database query on the accelerator.

Abstract: A structure and method includes a detector to determine that a caller is waiting for service. A presentation module presents an advertisement to the waiting caller, wherein a compensation is paid to have the advertisement presented to the waiting caller.

Abstract: In an approach for scheduling the execution of a workload in a computing environment, a computer receives a request for scheduling execution of a computing job, wherein the computing job includes a plurality of computing tasks to be executed in a sequence, and wherein at least one computing task requires access to a set of data. The computer identifies information related to the computing environment, wherein the information comprises at least processors available to execute each computing task of the plurality of computing tasks and storage device proximity to the processors. The computer determines an execution configuration for the computing job based, at least in part, on the received request, the information related to the computing environment, and current utilization of the processors' resources. The computer schedules execution of the execution configuration for the computing job.

Abstract: A computer-implemented method includes determining that a database query warrants a first projection operation to project a plurality of input rows to a plurality of projected rows, where each of the plurality of input rows has one or more variable-length columns. A first projection control block is constructed, by a computer processor, to describe the first projection operation. The first projection operation is offloaded to a hardware accelerator. The first projection control block is provided to the hardware accelerator, and the first projection control block enables the hardware accelerator to perform the first projection operation at streaming rate.

Abstract: In an approach for scheduling the execution of a workload in a computing environment, a computer receives a request for scheduling execution of a computing job, wherein the computing job includes a plurality of computing tasks to be executed in a sequence, and wherein at least one computing task requires access to a set of data. The computer identifies information related to the computing environment, wherein the information comprises at least processors available to execute each computing task of the plurality of computing tasks and storage device proximity to the processors. The computer determines an execution configuration for the computing job based, at least in part, on the received request, the information related to the computing environment, and current utilization of the processors' resources. The computer schedules execution of the execution configuration for the computing job.

Abstract: In some embodiments, a query recipient is configured to determine that a database query warrants a first projection operation to project a plurality of input rows to a plurality of projected rows. Each of the input rows has one or more variable-length columns. A projection controller is configured to construct a first projection control block to describe the first projection operation. For this construction, the projection controller is configured to construct a plurality of projection control elements, each one corresponding to a corresponding column in the input rows, and a header to specify the order of the projection control elements. The projection controller is further configured to offload the first projection operation to a hardware accelerator, and to provide the first projection control block to the hardware accelerator, where the first projection control block enables the hardware accelerator to perform the first projection operation at streaming rate.

Abstract: Embodiments include methods, systems and computer program products for offloading multiple processing operations to an accelerator. Aspects include receiving a database query from an application, performing an analysis on the query, and identifying a plurality of available accelerators. Aspects further include retrieving cost information for one or more templates available on each of the plurality of available accelerators, determining a query execution plan based on the cost information and the analysis on the query, and offloading one or more query operations to at least one of the plurality of accelerators based on the query execution plan.

Abstract: In an approach for scheduling the execution of a workload in a computing environment, a computer receives a request for scheduling execution of a computing job, wherein the computing job includes a plurality of computing tasks to be executed in a sequence, and wherein at least one computing task requires access to a set of data. The computer identifies information related to the computing environment, wherein the information comprises at least processors available to execute each computing task of the plurality of computing tasks and storage device proximity to the processors. The computer determines an execution configuration for the computing job based, at least in part, on the received request, the information related to the computing environment, and current utilization of the processors' resources. The computer schedules execution of the execution configuration for the computing job.

Abstract: Embodiments include methods, systems and computer program products for offloading multiple processing operations to an accelerator. Aspects include receiving a database query from an application, performing an analysis on the query, and identifying a plurality of available accelerators. Aspects further include retrieving cost information for one or more templates available on each of the plurality of available accelerators, determining a query execution plan based on the cost information and the analysis on the query, and offloading one or more query operations to at least one of the plurality of accelerators based on the query execution plan.

Abstract: A method includes obtaining a demand specification specifying a plurality of multi-modal freight shipment scenarios, each of the multi-modal freight shipment scenarios including at least a destination and an origin, generating, with a booking tool, a plurality of feasible multi-modal routes for each of the multi-modal freight shipment scenarios using route information from a carrier database, determining a plurality of business compliant routes among the plurality of feasible multi-modal routes for each of the multi-modal freight shipment scenarios using a rules specification specifying different business rules for each of the multi-modal freight shipment scenarios, comparing the multi-modal freight shipment scenarios by the business compliant routes determined for each respective one of the multi-modal freight shipment scenarios, and identifying at least one business rule, among the different business rules, affecting an aggregate cost-savings using the comparison of the multi-modal freight shipment scenario

Abstract: A method includes obtaining a demand specification specifying a plurality of multi-modal freight shipment scenarios, each of the multi-modal freight shipment scenarios including at least a destination and an origin, generating, with a booking tool, a plurality of feasible multi-modal routes for each of the multi-modal freight shipment scenarios using route information from a carrier database, determining a plurality of business compliant routes among the plurality of feasible multi-modal routes for each of the multi-modal freight shipment scenarios using a rules specification specifying different business rules for each of the multi-modal freight shipment scenarios, comparing the multi-modal freight shipment scenarios by the business compliant routes determined for each respective one of the multi-modal freight shipment scenarios, and identifying at least one business rule, among the different business rules, affecting an aggregate cost-savings using the comparison of the multi-modal freight shipment scenario

Abstract: Identifying resource bottleneck in multi-stage workflow processing may include identifying dependencies between logical stages and physical resources in a computing system to determine which logical stage involves what set of resources; for each of the identified dependencies, determining a functional relationship between a usage level of a physical resource and concurrency level of a logical stage; estimating consumption of the physical resources by each of the logical stages based on the functional relationship determined for each of the logical stages; and performing a predictive modeling based on the estimated consumption to determine a concurrency level at which said each of the logical stages will become bottleneck.