International Business Machines Corporation

Scott Douglas Walkty

David C Kalmuk

Paul Miller Bird

Patrick R Perez

Stephen Rees

Faizan Qazi

Ronald Jason Barber

Christian Marcelo Garcia-Arellano

Hamdi Roumani

Alexander Cheung

Richard Sefton Sidle

Examples described herein provide a computer-implemented method for storing data pages of a database in a remote storage. The method includes receiving a write request associated with one or more data pages of the database. The method further includes storing, in a first storage layer having a write ahead log, the write request associated with the one or more data pages. The method further includes storing, in a first write buffer, the received write request associated with the one or more data pages that each include a legacy data page identifier. The method further includes storing, in a second storage layer having a second write buffer, a sorted string table (SST) of the received write request. The method further includes storing, in a top level of a log structured merge (LSM) tree disposed in the remote storage, the SST from the second write buffer.

Storing data pages of a database in a cloud object storage

A system and method is provided for dynamic renewable runtime resource management in response to flexible resource allocations by a processor. In embodiments, a method includes: calculating, by a processor of a system, a resource consumption value of a first workload by aggregating allocation values of persistent resources currently allocated to the first workload by the processor; determining, by the processor, that the resource consumption value of the first workload is greater than a predefined resource allocation target for the first workload; and temporarily adjusting, by the processor, a renewable runtime resource target of the first workload from an initial target value to a temporary target value based on the resource consumption value.

Dynamic renewable runtime resource management

Predictive job admission control is provided. In response receiving a job for execution, a predicted resource utilization estimate is generated for the job prior to admission of the job to execution. Historic job execution statistics corresponding to the job are searched for. It is determined whether corresponding historic job execution statistics were found during the search. In response to determining that corresponding historic job execution statistics were found during the search, the predicted resource utilization estimate for the job is adjusted based on the corresponding historic job execution statistics found during the search to form an adjusted resource utilization estimate. The job is scheduled for execution based on the adjusted resource utilization estimate.

Predictive job admission control with feedback

According to an embodiment of the present invention, a system assigns at least one workload a hard share quantity and at least one other workload a soft share quantity or a hard share quantity. The system allocates a resource to the workloads based on the hard share quantity and the soft share quantity of active workloads in a predefined interval. A hard share quantity indicates a maximum resource allocation and a soft share quantity enables allocation of additional available processor time. Embodiments of the present invention further include a method and computer program product for allocating a resource to workloads in substantially the same manner as described above.

Adaptive resource usage limits for workload management

A monitoring of a server system during an execution of a server system processing logic, includes: during collection and storage of operational metrics by a given thread in a thread-local memory, determining that a checkpoint within the server system processing logic is reached; determining whether a threshold number of checkpoints have been encountered by the given thread; in response to the threshold number of checkpoints having been encountered, determining whether a threshold time interval since a last rollup of the collected operational metrics has been exceeded; and in response to the threshold time interval being exceeded, performing a rollup of the collected operational metrics from the thread-local memory to an accumulation point in a shared memory, where the accumulation point stores aggregated operational metrics from a plurality of threads.

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Scott Douglas Walkty