Abstract:
In a method for training a machine learning model, the method includes: segmenting, by a processor, a dataset from a database into one or more datasets based on time period windows; assigning, by the processor, one or more weighted values to the one or more datasets according to the time period windows of the one or more datasets; generating, by the processor, a training dataset from the one or more datasets according to the one or more weighted values; and training, by the processor, the machine learning model using the training dataset.
Abstract:
Embodiments of the inventive concept include solid state drive (SSD) multi-card adapters that can include multiple solid state drive cards, which can be incorporated into existing enterprise servers without major architectural changes, thereby enabling the server industry ecosystem to easily integrate evolving solid state drive technologies into servers. The SSD multi-card adapters can include an interface section between various solid state drive cards and drive connector types. The interface section can perform protocol translation, packet switching and routing, data encryption, data compression, management information aggregation, virtualization, and other functions.
Abstract:
Embodiments of the inventive concept include solid state drive (SSD) multi-card adapters that can include multiple solid state drive cards, which can be incorporated into existing enterprise servers without major architectural changes, thereby enabling the server industry ecosystem to easily integrate evolving solid state drive technologies into servers. The SSD multi-card adapters can include an interface section between various solid state drive cards and drive connector types. The interface section can perform protocol translation, packet switching and routing, data encryption, data compression, management information aggregation, virtualization, and other functions.
Abstract:
A computing system includes: a fetch block configured to provide an initial destination and a way prediction associated with the initial destination for accessing a retrieval target; a way block, coupled to the fetch block, configured to determine a way-fetch result based on the way prediction; a parallel circuit, coupled to the fetch block, configured to determine an access destination based on the initial destination in parallel and concurrently with the way block; and an access block, coupled to the way block and the parallel circuit, configured to access the retrieval target based on comparing the access destination and the way-fetch result.
Abstract:
A system and method for distributed caching, the system having at least one network-connected storage device, a content server, and a control server. The control server is configured to discover the at least one network-connected storage device, collect device information from the at least one network-connected storage device, where the device information comprises a device location, assign each of the at least one network-connected storage device to a device domain based on each device location, and provide the content server with the device information for the one or more network-connected storage.
Abstract:
A controller of a data storage device includes: a host interface providing an interface to a host computer; a flash translation layer (FTL) translating a logical block address (LBA) to a physical block address (PBA) associated with an input/output (I/O) request; a flash interface providing an interface to flash media to access data stored on the flash media; and one or more deep neural network (DNN) modules for predicting an I/O access pattern of the host computer. The one or more DNN modules provide one or more prediction outputs to the FTL that are associated with one or more past I/O requests and a current I/O request received from the host computer, and the one or more prediction outputs include at least one predicted I/O request following the current I/O request. The FTL prefetches data stored in the flash media that is associated with the at least one predicted I/O request.
Abstract:
A system and method for distributed caching, the system having at least one network-connected storage device, a content server, and a control server. The control server is configured to discover the at least one network-connected storage device, collect device information from the at least one network-connected storage device, where the device information comprises a device location, assign each of the at least one network-connected storage device to a device domain based on each device location, and provide the content server with the device information for the one or more network-connected storage.
Abstract:
A system and method for distributed caching, the system having at least one network-connected storage device, a content server, and a control server. The control server is configured to discover the at least one network-connected storage device, collect device information from the at least one network-connected storage device, where the device information comprises a device location, assign each of the at least one network-connected storage device to a device domain based on each device location, and provide the content server with the device information for the one or more network-connected storage.
Abstract:
A system and method for distributed caching, the system having at least one network-connected storage device, a content server, and a control server. The control server is configured to discover the at least one network-connected storage device, collect device information from the at least one network-connected storage device, where the device information comprises a device location, assign each of the at least one network-connected storage device to a device domain based on each device location, and provide the content server with the device information for the one or more network-connected storage.
Abstract:
A method for predicting a time-to-failure of a target storage device may include training a machine learning scheme with a time-series dataset, and applying the telemetry data from the target storage device to the machine learning scheme which may output a time-window based time-to-failure prediction. A method for training a machine learning scheme for predicting a time-to-failure of a storage device may include applying a data quality improvement framework to a time-series dataset of operational and failure data from multiple storage devices, and training the scheme with the pre-processed dataset. A method for training a machine learning scheme for predicting a time-to-failure of a storage device may include training the scheme with a first portion of a time-series dataset of operational and failure data from multiple storage devices, testing the machine learning scheme with a second portion of the time-series dataset, and evaluating the machine learning scheme.