Abstract: Systems, methods and apparatus are provided for a reusable, client-server based ecosystem designed to support content-aware, LiFi-powered transfer of large-scale, semi-structured data files. Containerized client-side applications may include a LiFi communication engine (LCE), a job control engine (JCE), and an execution hub that is configured to interface with the JCE, the LCE, job stakeholders and downstream applications. A central server may include a server-side LCE configured for two-way communication with the client-side LCE. Each LCE may be configured to cluster semi-structured data into data packets, broadcast data packets using an LED array, receive data packets using an array of photoreceptors and synchronize received data packets.

Abstract: Aspects described herein relate to a centralized computing system that interacts with a plurality of data centers, each having an edge server. Each edge server obtains sensor information from a plurality of sensors and processes the sensor information to detect an imminent shutdown and sends emergency data to a centralized processing entity when detected. In order to make a decision, the edge server processes the sensor data based on dynamic sensor thresholds and dynamic prioritizer data by syncing with the centralized computing system. Because of the short time duration to report emergency data before an imminent complete shutdown, an edge server may utilize a quantum data pipeline and quantum data storage as a key medium for all data transfer in a normal condition and at the time of emergency for internally transporting processed sensor data and providing the emergency data to the centralized processing entity.

Abstract: Systems and methods for predictive performance indicators for storage devices are described. The data storage device may process host storage operations and maintenance operations that impact real-time performance. A performance value and corresponding threshold may be determined. Increases in maintenance operations and resulting changes in the performance value may be predicted. When the predicted change in performance value crosses the performance threshold, the host device may be notified.

Abstract: Systems and methods for predictive performance indicators for storage devices are described. The data storage device may process host storage operations and maintenance operations that impact real-time performance. A performance value and corresponding threshold may be determined. Increases in maintenance operations and resulting changes in the performance value may be predicted. When the predicted change in performance value crosses the performance threshold, the host device may be notified.

Abstract: Systems and methods data storage device performance prediction based on garbage collection resources are described. The data storage device may process host storage operations and determine a valid fragment count parameter for a current or future data block. Based on the valid fragment count parameter a predicted performance value for host storage operations is determined and the host device is notified of the predicted performance value.

Abstract: Systems and methods for predictive performance indicators for storage devices are described. The data storage device may process host storage operations and maintenance operations that impact real-time performance. A performance value and corresponding threshold may be determined. Increases in maintenance operations and resulting changes in the performance value may be predicted. When the predicted change in performance value crosses the performance threshold, the host device may be notified.

Abstract: Systems and methods data storage device performance prediction based on garbage collection resources are described. The data storage device may process host storage operations and determine a valid fragment count parameter for a current or future data block. Based on the valid fragment count parameter a predicted performance value for host storage operations is determined and the host device is notified of the predicted performance value.

Abstract: Systems and methods for predictive performance indicators for storage devices are described. The data storage device may process host storage operations and maintenance operations that impact real-time performance. A performance value and corresponding threshold may be determined. Increases in maintenance operations and resulting changes in the performance value may be predicted. When the predicted change in performance value crosses the performance threshold, the host device may be notified.