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.

Abstract: A system and method for leveraging a native operating system page cache when using non-block system storage devices is disclosed. A computer may include a processor, memory, and a non-block system storage device. A file system may be stored in memory and running on the processor, which may include a page cache. A key-value file system (KVFS) may reside between the file system and the storage device and may map received file system commands to key-value system commands that may be executed by the storage device. Results of the key-value system commands may be returned to the file system, permitting the operating system to cache data in the page cache.

Abstract: Systems and methods for finding nearest neighbors. In some embodiments, the system includes a processing circuit. The processing circuit may be configured to perform a method, the method including: selecting, based on a first query vector, a selected method, the selected method being a nearest neighbor selection method; and performing the selected method to select a first nearest neighbor from a data set, based on the first query vector.

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: 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 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.

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: 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: 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.

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 system and a method to allocate data to a first cache increments a first counter if a reuse indicator for the data indicates that the data is likely to be reused and decremented the counter if the reuse indicator for the data indicates that the data is likely not to be reused. A second counter is incremented upon eviction of the data from the second cache, which is a higher level cache than the first cache. The data is allocated to the first cache if the value of the first counter is equal to or greater than the first predetermined threshold or the value of the second counter equals zero, and the data is bypassed from the first cache if the value of the first counter is less than the first predetermined threshold and the value of the second counter is not equal to zero.

Abstract: A system and method for leveraging a native operating system page cache when using non-block system storage devices is disclosed. A computer may include a processor, memory, and a non-block system storage device. A file system may be stored in memory and running on the processor, which may include a page cache. A key-value file system (KVFS) may reside between the file system and the storage device and may map received file system commands to key-value system commands that may be executed by the storage device. Results of the key-value system commands may be returned to the file system, permitting the operating system to cache data in the page cache.

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 leveraging a native operating system (130) page cache (315) when using non-block system storage devices (120) is disclosed. A computer (105) may include a processor (110), memory (115), and a non-block system storage device (120). A file system (135) may be stored in memory (115) and running on the processor (110), which may include a page cache (315). A key-value file system (KVFS) (145) may reside between the file system (135) and the storage device (120) and may map received file system commands (310) to key-value system commands (330) that may be executed by the storage device (120). Results of the key-value system commands (330) may be returned to the file system (135), permitting the operating system (130) to cache data in the page cache (315).

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 a method to allocate data to a first cache increments a first counter if a reuse indicator for the data indicates that the data is likely to be reused and decremented the counter if the reuse indicator for the data indicates that the data is likely not to be reused. A second counter is incremented upon eviction of the data from the second cache, which is a higher level cache than the first cache. The data is allocated to the first cache if the value of the first counter is equal to or greater than the first predetermined threshold or the value of the second counter equals zero, and the data is bypassed from the first cache if the value of the first counter is less than the first predetermined threshold and the value of the second counter is not equal to zero.

Abstract: A system and a method to allocate data to a first cache increments a first counter if a reuse indicator for the data indicates that the data is likely to be reused and decremented the counter if the reuse indicator for the data indicates that the data is likely not to be reused. A second counter is incremented upon eviction of the data from the second cache, which is a higher level cache than the first cache. The data is allocated to the first cache if the value of the first counter is equal to or greater than the first predetermined threshold or the value of the second counter equals zero, and the data is bypassed from the first cache if the value of the first counter is less than the first predetermined threshold and the value of the second counter is not equal to zero.

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.