Emc IP Holding Company LLC

Angelo Ernani Maia Ciarlini

Rômulo Teixeira De Abreu Pinho

Karin Breitman

Jaumir Valença Da Silveira Junior

Jonas Furtado Dias

Vinícius Michel Gottin

Alex Laier Bordignon

Daniel Sadoc Menasché

Deep reinforcement learning techniques are provided for resource allocation in a shared computing environment. An exemplary method comprises: obtaining a specification of a workflow of a plurality of concurrent workflows in a shared computing environment, wherein the specification comprises a plurality of workflow states and one or more control variables for the workflow in the shared computing environment; evaluating values of the control variables for an execution of the concurrent workflows using a reinforcement learning agent by (i) observing the states, including a current state, and (ii) obtaining an expected utility score for combinations of the control variables for the execution of the concurrent workflows given an allocation of resources of the shared computing environment corresponding to the combination of control variables in the current state; and providing an allocation of the resources of the shared computing environment reflecting the combination having the expected utility score that satisfies a predefined score criteria.

Deep reinforcement learning for workflow optimization

Scale-out network attached storage (NAS) file systems can employ an Ingest, Transform, Store (ITS) framework for data processing. In one aspect, the ITS-NAS file systems comprise NAS nodes and high performance computing (HPC) nodes that operate under a common operating system and that are coupled to each other via a common high-bandwidth, low-latency private network infrastructure. The NAS nodes can present data to the HPC nodes as well as dispatch the execution of transform services to the HPC nodes. The ITS-NAS file systems enable massive parallelization of operations on files, for example, complex distributed operations on large files and/or simple parallel operations on large collections of small files, all within the same hardware and software architecture.

Architecture for a converged compute and file system within network-attached storage clusters

A hot folder mechanism is employed to provide a truly integrated architecture for easy-to-use, easy-to-deploy scale-out computation and scale-out storage. A folder of an Ingest, Transform, Store (ITS)-Network attached storage (NAS) system can be configured as 'hot.' The configured hot folder can then detect changes on its content, analyze such content, perform transform services on the content, and output the computation results as files on other specified output folders. In one aspect, file system nodes of the ITS-NAS can present the content to high performance computing (HPC) compute nodes of the ITS-NAS as well as to dispatch the execution of transform services to the HPC compute nodes.

Systems and methods for file triggers in a converged compute and file system

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Rio de Janeiro, Brazil

Angelo Ernani Maia Ciarlini