Abstract: A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric b

Abstract: A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric b

Abstract: Managing a cache memory in a storage system includes maintaining a queue that stores data indictive of the read requests for a particular logical storage unit of the storage system in an order that the read requests are received by the storage system, receiving a read request for a particular page of the particular logical storage unit, and removing a number of elements in the queue and resizing the queue in response to the queue being full. Managing the cache memory also includes placing data indicative of the read request in the queue, determining a prefetch metric that varies according to a number of adjacent elements in a sorted version of the queue having a difference that is less than a predetermined value and greater than zero, and prefetching a plurality of pages that come after the particular page sequentially if the prefetch metric is greater than a predefined value.

Abstract: A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric b

Abstract: Managing a cache memory in a storage system includes maintaining a first queue that stores data indictive of the read requests for a particular logical storage unit of the storage system in an order that the read requests are received by the storage system and maintaining a second queue that stores data indictive of the read requests for the particular logical storage unit in a sort order corresponding to page numbers of the read requests, the second queue persisting for a plurality of iterations of read requests. A read request is received and data indicative of the read request is placed in the first queue and in the second queue while maintaining the sort order of the second queue. The second queue is used to determine a prefetch metric that varies according to a number of adjacent elements in the second queue.

Abstract: Managing a cache memory in a storage system includes maintaining a first queue that stores data indictive of the read requests for a particular logical storage unit of the storage system in an order that the read requests are received by the storage system and maintaining a second queue that stores data indictive of the read requests for the particular logical storage unit in a sort order corresponding to page numbers of the read requests, the second queue persisting for a plurality of iterations of read requests. A read request is received and data indicative of the read request is placed in the first queue and in the second queue while maintaining the sort order of the second queue. The second queue is used to determine a prefetch metric that varies according to a number of adjacent elements in the second queue.

Abstract: Managing a cache memory in a storage system includes maintaining a queue that stores data indictive of the read requests for a particular logical storage unit of the storage system in an order that the read requests are received by the storage system, receiving a read request for a particular page of the particular logical storage unit, and removing a number of elements in the queue and resizing the queue in response to the queue being full. Managing the cache memory also includes placing data indicative of the read request in the queue, determining a prefetch metric that varies according to a number of adjacent elements in a sorted version of the queue having a difference that is less than a predetermined value and greater than zero, and prefetching a plurality of pages that come after the particular page sequentially if the prefetch metric is greater than a predefined value.

Abstract: A pre-fetching technique determines what data, if any, to pre-fetch on a per-logical storage unit basis. For a given logical storage unit, what, if any, data to prefetch is based at least in part on a collective sequential proximity of the most recently requested pages of the logical storage unit. Determining what, if any, data to pre-fetch for a logical storage unit may include determining a value for a proximity metric indicative of the collective sequential proximity of the most recently requested pages, comparing the value to a predetermined proximity threshold value, and determining whether to pre-fetch one or more pages of the logical storage unit based on the result of the comparison. A data structure may be maintained that includes most recently requested pages for one or more logical storage units. This data structure may be a table.

Abstract: A pre-fetching technique determines what data, if any, to pre-fetch on a per-logical storage unit basis. For a given logical storage unit, what, if any, data to prefetch is based at least in part on a collective sequential proximity of the most recently requested pages of the logical storage unit. Determining what, if any, data to pre-fetch for a logical storage unit may include determining a value for a proximity metric indicative of the collective sequential proximity of the most recently requested pages, comparing the value to a predetermined proximity threshold value, and determining whether to pre-fetch one or more pages of the logical storage unit based on the result of the comparison. A data structure may be maintained that includes most recently requested pages for one or more logical storage units. This data structure may be a table.

Abstract: A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric b