7 results about "Workload management" patented technology

The present technology enhances workload management of data storage systems by using an internal copy function and / or a zone-append technology. The internal copy function is used, e.g., in merge operations to move data between locations on a disk without using off-disk resources (e.g., processing or memory of a CPU). Zone-append technology uses nameless writes (e.g., write instruction without an assigned destination address on the disk) to combine IO units from different threads to be written to a common zone of a disk (e.g., a shingled magnetic recording (SMR) disk). Sequential addresses are assigned to IO units from different threads based on their order in the write queue, reducing the latency and seek time typically associated with random writes. The zone-append technology, e.g., uses sequential write operations within specified zones, allowing the disk to determine the actual write location and to report post-write logical block addresses (LBAs).

Methods, systems, and computer storage media for providing workload management using a workload management engine in an artificial intelligence (AI) system. In particular, workload management includes adaptive policies that adjust neural network models employed by processing units (e.g., NPUs / GPUs / TPUs) based on dynamic properties of workloads, workload management factors, and workload management logic. The workload management engine provides workload management logic to support policy decisions that are optimized for the processor. In operation, a plurality of states of workload management factors are identified. A task associated with a workload processing unit is identified. A neural network model is selected from a plurality of neural network models based on the task and the plurality of states of the workload processing unit. The plurality of neural network models includes a full neural network model and a simplified neural network model. The task is caused to be performed using the identified neural network model.

This invention provides a method and system for quantitatively assessing the overall work status of an organization, belonging to the field of organizational workload management technology. The method uses the quantitative assessment results of individual employee workloads as its underlying support. On the one hand, it calculates and statistically analyzes the overall workload compliance rate of the organization; on the other hand, it deeply analyzes the level distribution characteristics of each dimension, including workload, work status, work ability / efficiency, work attitude, and overtime, ultimately constructing a panoramic distribution view of the overall work status of the organization. This method effectively solves the problems of incomplete assessment scope, insufficient measurement accuracy, and lack of guidance in assessment results existing in traditional assessment models, providing standardized and quantifiable analytical basis for accurate judgment of the overall work status of the organization and scientific formulation of management decisions.

An approach is described that enables the reception of multiple client requests directed to a mainframe computer system from various transaction origination points external to the mainframe. It involves computing time-based performance metrics, including transaction times from these origination points to the mainframe. By capturing and analyzing transaction times from diverse external sources, the system evaluates mainframe performance holistically, reflecting the complete duration of transactions from their initiation outside the mainframe to their processing completion within it. This approach provides comprehensive insights into transaction efficiency across different user environments, enhancing the system’s ability to measure, optimize, and maintain performance standards under varied operating conditions.

The invention relates to a system and method for optimizing task assignments in an enterprise environment using real-time biometric data collected from wearable devices. The system comprises a wearable device for capturing biometric signals such as heart rate, EEG data, and other physiological metrics, a data processor to filter and validate the data, a cognitive analyzer to calculate cognitive metrics including cognitive load, stress levels, and focus, and a workflow optimizer to assign tasks based on these metrics. The workflow optimizer leverages machine learning algorithms to analyze the cognitive state of users, compare it with historical performance data, and adjust workload distribution dynamically. The system integrates with enterprise resource planning (ERP) systems to synchronize optimized task assignments across the organization. By monitoring cognitive metrics in real time, the invention ensures efficient workload management, reduces employee fatigue, and enhances overall productivity.

Systems and methods are provided for workload management in a healthcare setting. In one example, a method for determining a workload demand of a medical facility includes automatically determining a workload score for a care team including plurality of clinicians over a shift based on shift data and event data received from the medical facility, including calculating a cumulative workload over the shift based on the event data and a workload reduction over time based on the shift data, generating a graphical workload score tile including a visual representation of the workload score, arranging the graphical workload score tile in a workload graphical user interface (GUI), and displaying the GUI on a display device.

In various examples, systems, devices and methods are disclosed relating to management of processing resources and workloads assigned thereto. A system can obtain, from a plurality of processing resources executing a plurality of tasks, telemetry data and task assignment data. The system can perform generate, using the telemetry data and the task assignment data, a plurality of feature vectors, determine, using the plurality of feature vectors and a machine learning model employing a graph attention network (GAT) having a plurality of nodes, a performance state of a node of the plurality of nodes, and determine, based on the performance state of the node, an action to be taken to enhance performance of the plurality of processing resources or mitigate node failures. Each node can represent one or more respective processing resources and each feature vector can be associated with a respective node of the plurality of nodes.