Abstract: An approach for optimizing storage on a local storage device. The approach receives a cloud resource budget limit and a cloud budget time interval. The approach estimates future cloud resource requests expected to arrive before the end of the cloud budget time interval. The approach calculates definitive and estimated costs of cloud resource usage types. The approach calculates a total estimated resource budget consumption. The approach determines if the total estimated resource budget consumption exceeds the cloud resource budget limit. If the approach determines the cloud resource budget limit is not exceeded, then the approach outputs a set of existing unfulfilled cloud resource requests for fulfillment. If the approach determines the cloud resource budget limit is exceeded, then the approach outputs a subset of set of existing unfulfilled cloud resource requests that do not exceed the cloud resource budget limit for fulfillment.

Abstract: An approach for estimating future cloud resource requests. The approach receives information defining a present budget interval. The approach analyzes a cloud resource request database based on entry times in respective past budget intervals occurring within a remaining time in the present budget interval. The approach creates estimates of future expected cloud resource requests based on the analysis.

Abstract: Provided are a computer program product, system, and method for estimating resource requests for workloads to offload to host systems in a computing environment. A calculation is made required resources of computational resources required to complete processing a plurality of unfinished workloads that have not completed. A determination is made of allocated resources that are not yet provisioned to workloads. The required resources are reduced by the allocated resources not yet provisioned to determine resources to provision. The resources to provision for the unfinished workloads are requested.

Abstract: Selecting an optimal combination of cloud resources within budget constraints, by a processor. All possible combinations for cloud resource bundles are computed which are available for allocation. The possible combinations are filtered according to predetermined criteria. The filtered possible combinations are divided into a first set that satisfies an overall workload demand for resources and a second set that partially satisfies the overall workload demand for resources. A level of goodness may be calculated for one or both of the first and second sets, and resources may be allocated from the first or second set to a cluster according to the calculated level of goodness. In some embodiments, the level of goodness may be defined based on the relative desirability (i.e., a user's preference) of aspects such as compute power, cost of resources, and the distribution or co-location of respective resources of the cloud resource bundles.

Abstract: Calculating a remaining budget in a cloud bursting environment, by a processor. Within a particular time window, a first cost is computed of allocated and released cloud hosts. A second cost is computed of the cloud hosts that are allocated and not yet released. A remaining budget is computed as a difference between a budget limit and a sum of the first cost and the second cost, at the current point in time. A third, estimated cost of allocated and not yet released cloud hosts; and a fourth, estimated cost of cloud host requests currently in progress are each then determined. A safe remaining budget is then computed by subtracting the third and fourth estimated costs from the remaining budget determined prior.

Abstract: Calculating a remaining budget in a cloud bursting environment, by a processor. Within a particular time window, a first cost is computed of allocated and released cloud hosts. A second cost is computed of the cloud hosts that are allocated and not yet released. A remaining budget is computed as a difference between a budget limit and a sum of the first cost and the second cost, at the current point in time. A third, estimated cost of allocated and not yet released cloud hosts; and a fourth, estimated cost of cloud host requests currently in progress are each then determined. A safe remaining budget is then computed by subtracting the third and fourth estimated costs from the remaining budget determined prior.

Abstract: Selecting an optimal combination of cloud resources within budget constraints, by a processor. All possible combinations for cloud resource bundles are computed which are available for allocation. The possible combinations are filtered according to predetermined criteria. The filtered possible combinations are divided into a first set that satisfies an overall workload demand for resources and a second set that partially satisfies the overall workload demand for resources. A level of goodness may be calculated for one or both of the first and second sets, and resources may be allocated from the first or second set to a cluster according to the calculated level of goodness. In some embodiments, the level of goodness may be defined based on the relative desirability (i.e., a user's preference) of aspects such as compute power, cost of resources, and the distribution or co-location of respective resources of the cloud resource bundles.

Abstract: Provided are a computer program product, system, and method for estimating resource requests for workloads to offload to host systems in a computing environment. A calculation is made required resources of computational resources required to complete processing a plurality of unfinished workloads that have not completed. A determination is made of allocated resources that are not yet provisioned to workloads. The required resources are reduced by the allocated resources not yet provisioned to determine resources to provision. The resources to provision for the unfinished workloads are requested.

Abstract: Provided are a computer program product, system, and method for estimating resource requests for workloads to offload to host systems in a computing environment. A calculation is made required resources of computational resources required to complete processing a plurality of unfinished workloads that have not completed. A determination is made of allocated resources that are not yet provisioned to workloads. The required resources are reduced by the allocated resources not yet provisioned to determine resources to provision. The resources to provision for the unfinished workloads are requested.

Abstract: Provided are a computer program product, system, and method for profiling workloads in host systems allocated to a cluster to determine adjustments to allocation of host systems to the cluster. A determination is made of workloads processing and resource usage in a computing system. An aggregate completion duration for determined workloads that have completed processing is updated. An aggregate consumed resources, comprising an aggregate of resources consumed by workloads, by resources consumed by the determined workloads is updated. The aggregate completion duration and the aggregate consumed resources are used to determine resources to provision for workloads.

Abstract: Provided are a computer program product, system, and method for determining allocatable host system resources to remove from a cluster and return to a host service provider. A determination is made of unused host system resources, that are not currently being used by workloads, in a plurality of host systems. A determination is made of required resources for computational resources required to complete processing unfinished workloads that have not completed. A determination is made of an amount of resources to remove from the cluster by subtracting the unused host system resources by the required resources for computational resources. At least one of the host systems available for the workloads is selected to remove from the cluster having resources that satisfy the amount of resources to remove.

Abstract: Provided are a computer program product, system, and method for profiling workloads in host systems allocated to a cluster to determine adjustments to allocation of host systems to the cluster. A determination is made of workloads processing and resource usage in a computing system. An aggregate completion duration for determined workloads that have completed processing is updated. An aggregate consumed resources, comprising an aggregate of resources consumed by workloads, by resources consumed by the determined workloads is updated. The aggregate completion duration and the aggregate consumed resources are used to determine resources to provision for workloads.

Abstract: Provided are a computer program product, system, and method for determining allocatable host system resources to remove from a cluster and return to a host service provider. A determination is made of unused host system resources, that are not currently being used by workloads, in a plurality of host systems. A determination is made of required resources for computational resources required to complete processing unfinished workloads that have not completed. A determination is made of an amount of resources to remove from the cluster by subtracting the unused host system resources by the required resources for computational resources. At least one of the host systems available for the workloads is selected to remove from the cluster having resources that satisfy the amount of resources to remove.

Abstract: Provided are a computer program product, system, and method for estimating resource requests for workloads to offload to host systems in a computing environment. A calculation is made required resources of computational resources required to complete processing a plurality of unfinished workloads that have not completed. A determination is made of allocated resources that are not yet provisioned to workloads. The required resources are reduced by the allocated resources not yet provisioned to determine resources to provision. The resources to provision for the unfinished workloads are requested.

Abstract: A mechanism for folding all the data dependencies in a loop into a single, conservative dependence. This mechanism leads to one pair of synchronization primitives per loop. This mechanism does not require complicated, multi-stage compile time analysis. This mechanism considers only the data dependence information in the loop. The low synchronization cost balances the loss in parallelism due to the reduced overlap between iterations. Additionally, a novel scheme is presented to implement required synchronization to enforce data dependences in a DOACROSS loop. The synchronization is based on an iteration vector, which identifies a spatial position in the iteration space of the loop. Multiple iterations executing in parallel have their own iteration vector for synchronization where they update their position in the iteration space. As no sequential updates to the synchronization variable exist, this method exploits a greater degree of parallelism.

Abstract: A computer implemented method, computer usable program code, and a system for parallelizing a loop. A parameter that will be used to limit parallelization of the loop is identified to limit parallelization of the loop. The parameter specifies a minimum number of loop iterations that a thread should execute. The parameter can be adjusted based on a parallel performance factor. A parallel performance factor is a factor that influences the performance of parallel code. A number of threads from a plurality of threads is selected for processing iterations of the loop based on the parameter. The number of threads is selected prior to execution of the first iteration of the loop.

Abstract: A mechanism for folding all the data dependencies in a loop into a single, conservative dependence. This mechanism leads to one pair of synchronization primitives per loop. This mechanism does not require complicated, multi-stage compile time analysis. This mechanism considers only the data dependence information in the loop. The low synchronization cost balances the loss in parallelism due to the reduced overlap between iterations. Additionally, a novel scheme is presented to implement required synchronization to enforce data dependences in a DOACROSS loop. The synchronization is based on an iteration vector, which identifies a spatial position in the iteration space of the loop. Multiple iterations executing in parallel have their own iteration vector for synchronization where they update their position in the iteration space. As no sequential updates to the synchronization variable exist, this method exploits a greater degree of parallelism.

Abstract: A computer implemented method, computer usable program code, and a system for parallelizing a loop. A parameter that will be used to limit parallelization of the loop is identified to limit parallelization of the loop. The parameter specifies a minimum number of loop iterations that a thread should execute. The parameter can be adjusted based on a parallel performance factor. A parallel performance factor is a factor that influences the performance of parallel code. A number of threads from a plurality of threads is selected for processing iterations of the loop based on the parameter. The number of threads is selected prior to execution of the first iteration of the loop.