Techniques described herein relate to systems and methods of data storage, and more particularly to providing layering of file system functionality on an object interface. In certain embodiments, file system functionality may be layered on cloud object interfaces to provide cloud-based storage while allowing for functionality expected from a legacy applications. For instance, POSIX interfaces and semantics may be layered on cloud-based storage, while providing access to data in a manner consistent with file-based access with data organization in name hierarchies. Various embodiments also may provide for memory mapping of data so that memory map changes are reflected in persistent storage while ensuring consistency between memory map changes and writes. For example, by transforming a ZFS file system disk-based storage into ZFS cloud-based storage, the ZFS file system gains the elastic nature of cloud storage.

A system includes a processing system and a memory system. The memory system stores instructions for identifying a cloud application in a cloud environment as a non-disposable application and monitoring a plurality of instances of the non-disposable application running in the cloud environment. The instructions when executed by the processing system further result in determining that a number of the instances of the non-disposable application should be modified based on one or more demand predictions by an artificial intelligence scaler, adjusting the number of the instances of the non-disposable application running in the cloud environment based on the one or more demand predictions, and modifying an allocation of one or more resources of the cloud environment associated with adjusting the number of the instances of the non-disposable application.

A system and method for processing application performance using application phase differentiation and detection is disclosed. Phase detection may be accomplished in a number of different ways, including by using a deterministic algorithm that looks for changes in the computing resource utilization patterns (as detected in the performance data collected). Machine learning (ML) and neural networks (e.g. sparse auto encoder SAE) may also be used. Performance data is aggregated according to phase and stored in a database along with additional application and computing system information. This database may then be used to find similar applications for performance prediction.

The present invention is capable of centrally managing the operation type of a plurality of analysis apparatus and includes: one or more analysis apparatuses; and a management apparatus that acquires various data from each of the one or more analysis apparatuses and centrally manages the acquired data, wherein each of the one or more analysis apparatuses includes an operation type data acquisition part that acquires data of each preset operation type, and an operation type data transmission part that transmits the operation type data acquired by the operation type data acquisition part, and the management apparatus includes an operation type data reception part that receives the operation type data, and a display control part that displays a list of the operation type data received by the operation type data reception part for each of the one or more analysis apparatuses.

Described embodiments provide systems and methods for detection of the degradation of a virtual desktop environment. A computing device may receive data from a plurality of client devices. The computing device may identify a subset of client devices from the plurality of client devices with at least one characteristic in common based on the received data. The computing device may determine a ratio of the identified subset of client devices, the ratio being a comparison of client devices of the subset with a value above a first threshold to a total number of client devices of the subset, and the value being indicative of a characteristic of performance for that client device. The computing device may identify a cause of an anomaly in the performance of the application based on the ratio exceeding a second threshold.

An electronic device that includes a processor configured to execute training iterations during a training process for a neural network, each training iteration including processing a separate instance of training data through the neural network, and a sparsity monitor is described. During operation, the sparsity monitor acquires, during a monitoring interval in each of one or more monitoring periods, intermediate data output by at least some intermediate nodes of the neural network during training iterations that occur during each monitoring interval. The sparsity monitor then generates, based at least in part on the intermediate data, one or more values representing sparsity characteristics for the intermediate data. The sparsity monitor next sends, to the processor, the one or more values representing the sparsity characteristics and the processor controls one or more aspects of executing subsequent training iterations based at least in part on the values representing the sparsity characteristics.

Various systems and methods are provided for analyzing the effect(s) that a configuration change to one device has on other connected devices. In one embodiment, the disclosed functionality includes determining connectivity information associated with a data center, where the data center comprises at least a first device and a second device; discovering one or more changes to a configuration of the first device; determining, based at least in part on the connectivity information, that the second device is impacted by the one or more changes to the configuration of the first device; and determining one or more impacts to the second device as a result of the one or more changes, where each of the one or more impacts indicates a positive impact to the second device, a negative impact to the second device, or no impact to the second device.

Various aspects are disclosed for distributed application management using an embedded persistent queue framework. In some aspects, task execution data is monitored from a plurality of task execution engines. A task request is identified. The task request can include a task and a Boolean predicate for task assignment. The task is assigned to a task execution engine embedded in a distributed application process if the Boolean predicate is true, and a capacity of the task execution engine is sufficient to execute the task. The task is enqueued in a persistent queue. The task is retrieved from the persistent queue and executed.

In some embodiments, a method includes: displaying, on a first client device, a plurality of tasks; identifying, by the first client device, a task from the plurality of tasks, the task transferrable to a second client device in communication with the first client device; and sending, by the first client device, metadata for the task to the second client device in response to input received by the first client device, the task including metadata to allowing the second client device to display the task in the same manner as the task was displayed by the first client device.

Methods, systems, and devices for adaptive user defined health indications are described. A host device may be configured to dynamically indicate adaptive health flags for monitoring health and wear information for a memory device. The host device may indicate, to a memory device, a first index. The first index may correspond to a first level of wear of a set of multiple indexed levels of wear for the memory device. The memory device may determine that a metric of the memory device satisfies the first level of wear and indicate, to the host device, that the first level of wear is satisfied. The host device may receive the indication that the first level of wear is satisfied and indicate, to the memory device, a second level of wear of the set of indexed levels of wear that is different than the first level of wear.