Abstract: The embodiments disclosed herein relate to intelligent configuration of a cloud-service gateway based on a pattern recognition algorithm. A machine-learning model is trained to learn the patterns of correlation among many configuration parameters affecting the performance of the system when processing an observed or estimated workload. Training the model may be performed off-line with performance data observed during experiments performed with a variety of configurations and workloads. Once trained, the model may be used to recommend: (a) new configuration parameter values based on constraints of the system being configured, (b) an amount of work that can be performed at a certain performance level when the system is configured with certain parameter values, or (c) the expected performance level when running a certain workload on the system configured with certain configuration parameter values.

Abstract: Methods and apparatus for providing a network attached storage system which does not require specialized hardware to operate. In one embodiment, a downloadable software package is provided via a web interface. After a user has downloaded and installed the software package, a sharable volume is created upon a host system. In one embodiment, the sharable volume is adapted to present the contents of one or more remote systems to the host system as a local file, drive, or directory. One or more processes resident in the host system are adapted to intercept a command interpretable by the host system and translate the command into one or more commands interpretable by at least one remote system. The one or more commands are then serviced by at least one remote system and a result set is generated. The result set may then be converted into a format interpretable by the host system and output accordingly.

Abstract: The embodiments disclosed herein relate to intelligent configuration of a cloud-service gateway based on a pattern recognition algorithm. A machine-learning model is trained to learn the patterns of correlation among many configuration parameters affecting the performance of the system when processing an observed or estimated workload. Training the model may be performed off-line with performance data observed during experiments performed with a variety of configurations and workloads. Once trained, the model may be used to recommend: (a) new configuration parameter values based on constraints of the system being configured, (b) an amount of work that can be performed at a certain performance level when the system is configured with certain parameter values, or (c) the expected performance level when running a certain workload on the system configured with certain configuration parameter values.

Abstract: The embodiments disclosed herein relate to intelligent configuration of a cloud-service gateway based on a pattern recognition algorithm. A machine-learning model is trained to learn the patterns of correlation among many configuration parameters affecting the performance of the system when processing an observed or estimated workload. Training the model may be performed off-line with performance data observed during experiments performed with a variety of configurations and workloads. Once trained, the model may be used to recommend: (a) new configuration parameter values based on constraints of the system being configured, (b) an amount of work that can be performed at a certain performance level when the system is configured with certain parameter values, or (c) the expected performance level when running a certain workload on the system configured with certain configuration parameter values.

Abstract: The embodiments disclosed herein relate to intelligent configuration of a cloud-service gateway based on a pattern recognition algorithm. A machine-learning model is trained to learn the patterns of correlation among many configuration parameters affecting the performance of the system when processing an observed or estimated workload. Training the model may be performed off-line with performance data observed during experiments performed with a variety of configurations and workloads. Once trained, the model may be used to recommend: (a) new configuration parameter values based on constraints of the system being configured, (b) an amount of work that can be performed at a certain performance level when the system is configured with certain parameter values, or (c) the expected performance level when running a certain workload on the system configured with certain configuration parameter values.

Abstract: Methods and apparatus for providing a network attached storage system which does not require specialized hardware to operate. In one embodiment, a downloadable software package is provided via a web interface. After a user has downloaded and installed the software package, a sharable volume is created upon a host system. In one embodiment, the sharable volume is adapted to present the contents of one or more remote systems to the host system as a local file, drive, or directory. One or more processes resident in the host system are adapted to intercept a command interpretable by the host system and translate the command into one or more commands interpretable by at least one remote system. The one or more commands are then serviced by at least one remote system and a result set is generated. The result set may then be converted into a format interpretable by the host system and output accordingly.

Abstract: Methods and apparatus for providing a network attached storage system which does not require specialized hardware to operate. In one embodiment, a downloadable software package is provided via a web interface. After a user has downloaded and installed the software package, a sharable volume is created upon a host system. In one embodiment, the sharable volume is adapted to present the contents of one or more remote systems to the host system as a local file, drive, or directory. One or more processes resident in the host system are adapted to intercept a command interpretable by the host system and translate the command into one or more commands interpretable by at least one remote system. The one or more commands are then serviced by at least one remote system and a result set is generated. The result set may then be converted into a format interpretable by the host system and output accordingly.

Abstract: Methods and apparatus for providing a network attached storage system which does not require specialized hardware to operate. In one embodiment, a downloadable software package is provided via a web interface. After a user has downloaded and installed the software package, a sharable volume is created upon a host system. In one embodiment, the sharable volume is adapted to present the contents of one or more remote systems to the host system as a local file, drive, or directory. One or more processes resident in the host system are adapted to intercept a command interpretable by the host system and translate the command into one or more commands interpretable by at least one remote system. The one or more commands are then serviced by at least one remote system and a result set is generated. The result set may then be converted into a format interpretable by the host system and output accordingly.