Abstract: Embodiments for providing adaptive namespace verification with high efficiency. The verification process is used in modern filesystems to check the integrity of key data structures, such for backup systems where providing thorough and efficient mechanisms for verification is critical. Embodiments accelerate the verification process by selectively applying continuous, differential, and integral verification techniques for startup and runtime repair purposes to overcome present problems for system capacities that exceed the petabyte range and impose significant time requirements using existing verification methods. Embodiments work with both single-node and distributed filesystems.

Abstract: Embodiments for providing adaptive namespace verification with high efficiency. The verification process is used in modern filesystems to check the integrity of key data structures, such for backup systems where providing thorough and efficient mechanisms for verification is critical. Embodiments accelerate the verification process by selectively applying continuous, differential, and integral verification techniques for startup and runtime repair purposes to overcome present problems for system capacities that exceed the petabyte range and impose significant time requirements using existing verification methods. Embodiments work with both single-node and distributed filesystems.

Abstract: Embodiments for providing adaptive namespace verification with high efficiency. The verification process is used in modern filesystems to check the integrity of key data structures, such for backup systems where providing thorough and efficient mechanisms for verification is critical. Embodiments accelerate the verification process by selectively applying continuous, differential, and integral verification techniques for startup and runtime repair purposes to overcome present problems for system capacities that exceed the petabyte range and impose significant time requirements using existing verification methods. Embodiments work with both single-node and distributed filesystems.

Abstract: Embodiments for providing adaptive namespace verification with high efficiency. The verification process is used in modern filesystems to check the integrity of key data structures, such for backup systems where providing thorough and efficient mechanisms for verification is critical. Embodiments accelerate the verification process by selectively applying continuous, differential, and integral verification techniques for startup and runtime repair purposes to overcome present problems for system capacities that exceed the petabyte range and impose significant time requirements using existing verification methods. Embodiments work with both single-node and distributed filesystems.

Abstract: One example method includes defining a physical namespace, determining a number of virtual namespaces, virtualizing the physical namespace by defining the virtual namespaces on the physical namespace, and generating a modified lookup key that is a function of a name of one of the virtual namespaces. The modified lookup key may be moved between virtual namespaces without requiring interruption of a backup or restore process, and without requiring an associated file system to be taken offline. Movement of the modified lookup key may be transparent to a user and may permit preservation of scripts that were in place prior to the move.

Abstract: Requests from file system services of a storage system are registered. Each file system service, when executed, utilizes one or more resources of the storage system. Each request includes information describing resource requirements required by a respective file system service. Resource utilization data of the resources are collected over a period of time. The resource utilization data includes an identification of a resource, a timestamp, and a measurement indicating a utilization level of the resource corresponding to the timestamp. A machine learning model is trained to predict utilization patterns of the resources. Execution of the file system services are scheduled based on the predicted utilization patterns. Monitoring is conducted during the execution of the file system services. Based on the monitoring a determination is made as to whether the machine learning model should be retrained.

Abstract: Embodiments of the present disclosure relate to a method, a system, and a non-transitory machine-readable medium for assigning performance defects to software processing components. Provided is a method that receives performance data of a set of performance runs of a software application across a set of software processing components of a file software system, where the performance data comprises multiple labeled performance features that are associated with the set of software processing components; receives, from a software processing component expert, a user-selection of a subset of labeled performance features of the several labeled performance features; and trains a machine learning (ML) model to determine whether one or more of the set of software processing components is associated with performance regressions of the software application, using the subset of labeled performance features of the performance data as training data.

Abstract: Requests from file system services of a storage system are registered. Each file system service, when executed, utilizes one or more resources of the storage system. Each request includes information describing resource requirements required by a respective file system service. Resource utilization data of the resources are collected over a period of time. The resource utilization data includes an identification of a resource, a timestamp, and a measurement indicating a utilization level of the resource corresponding to the timestamp. A machine learning model is trained to predict utilization patterns of the resources. Execution of the file system services are scheduled based on the predicted utilization patterns. Monitoring is conducted during the execution of the file system services. Based on the monitoring a determination is made as to whether the machine learning model should be retrained.

Abstract: Various approaches for demoting a memory page are described. In one approach, a first new page is established from a subpage of a base page in response to a request to demote a specified subpage. The size of the first new page is selected from a plurality of page sizes. For each portion of the base page less the first new page, the portion is divided into one or more pages of a selected size. The selected size for the pages is a largest of the plurality of page sizes that is less than or equal to the size of the portion. If the new one or more pages do not encompass the entire portion, a new feasible, largest of the sizes is selected and the part of the portion not encompassed is further divided into one or more pages.

Abstract: Various approaches for demoting a memory page are described. In one approach, a first new page is established from a subpage of a base page in response to a request to demote a specified subpage. The size of the first new page is selected from a plurality of page sizes. For each portion of the base page less the first new page, the portion is divided into one or more pages of a selected size. The selected size for the pages is a largest of the plurality of page sizes that is less than or equal to the size of the portion. If the new one or more pages do not encompass the entire portion, a new feasible, largest of the sizes is selected and the part of the portion not encompassed is further divided into one or more pages.