Abstract: Techniques described herein relate to methods and systems for thermal management of a thermal environment. The method may include using thermal data items from computing devices and time series analysis to predict future thermal values for the thermal data items; performing a clustering analysis using the predicted future thermal values to assign cluster labels to the computing devices; using the cluster labels and the predicted future thermal values to assign predicted thermal status labels to the computing devices; assigning a confidence value to the predicted thermal status labels and ranking the computing devices based on the confidence values; performing an analysis to determine a thermal data item contributing to the assigned thermal status; and sending the results to a thermal environment administrator.

Abstract: A system update appliance includes a processor and a memory device with a Content Addressable Storage (CAS) space and a location addressable storage space. The location addressable storage space partitioned into an object storage space and a device storage space. The processor stores a device entry in the device storage space. The device entry is associated with a device external to the system update appliance and includes a component entry for a component of the device. The component operates based on an update. The component entry includes a description of the component and a pointer to a record stored in the CAS space. The processor stores the record in the CAS space. The record is associated with a combination of the component and the first update. The record includes the description, a second pointer to an update repository, and a third pointer to the object storage space.

Abstract: In general, embodiments relate to a managing a Redundant Array of Independent Disks (RAID) group. The embodiments include determining a minimum and maximum set of spare disks to allocate to the RAID group, wherein the RAID group comprises a plurality of active members, allocating the minimum number of spare members to the RAID group, allocating an additional spare member to the RAID group, setting a mode of the additional spare member to storage mode, enabling, after the setting, the RAID controller to store data in the plurality of active members and in the additional spare member, wherein the plurality of active members, the minimum number of spare members, and the additional spare member comprise persistent storage.

Abstract: In general, in one aspect, the invention relates to a method for managing updates, the method includes identifying a sub-tree based on a call graph, generating an update sequence for the sub-tree using the call graph, selecting a first component of the sub-tree based on the update sequence, wherein the first component is a hardware resource, applying an update to the first component to obtain a first updated component, selecting, after applying the update to the first component, a second component of the sub-tree based on the update sequence, and performing an update on the second component.

Abstract: In general, in one aspect, the invention relates to a method for managing updates, the method includes identifying a sub-tree based on a call graph, generating an update sequence for the sub-tree using the call graph, selecting a first component of the sub-tree based on the update sequence, wherein the first component is a hardware resource, applying an update to the first component to obtain a first updated component, selecting, after applying the update to the first component, a second component of the sub-tree based on the update sequence, and performing an update on the second component.

Abstract: In general, embodiments relate to a managing a Redundant Array of Independent Disks (RAID) group. The embodiments include determining a minimum and maximum set of spare disks to allocate to the RAID group, wherein the RAID group comprises a plurality of active members, allocating the minimum number of spare members to the RAID group, allocating an additional spare member to the RAID group, setting a mode of the additional spare member to storage mode, enabling, after the setting, the RAID controller to store data in the plurality of active members and in the additional spare member, wherein the plurality of active members, the minimum number of spare members, and the additional spare member comprise persistent storage.

Abstract: Methods, apparatus, and processor-readable storage media for automatically allocating device resources using machine learning techniques are provided herein. An example computer-implemented method includes determining values for multiple parameters from data obtained from one or more devices; generating at least one device component failure prediction by applying one or more machine learning techniques to at least a portion of the determined values; computing one or more qualifying values attributable to the at least one generated device component failure prediction by providing the at least one generated device component failure prediction to a conformal prediction framework; and automatically allocating one or more resources to at least a portion of the one or more devices based at least in part on the at least one generated device component failure prediction and the one or more computed qualifying values.

Abstract: A system update appliance includes a processor and a memory device with a Content Addressable Storage (CAS) space and a location addressable storage space. The location addressable storage space partitioned into an object storage space and a device storage space. The processor stores a device entry in the device storage space. The device entry is associated with a device external to the system update appliance and includes a component entry for a component of the device. The component operates based on an update. The component entry includes a description of the component and a pointer to a record stored in the CAS space. The processor stores the record in the CAS space. The record is associated with a combination of the component and the first update. The record includes the description, a second pointer to an update repository, and a third pointer to the object storage space.

Abstract: A system update appliance includes a processor and a memory device with a Content Addressable Storage (CAS) space and a location addressable storage space. The location addressable storage space partitioned into an object storage space and a device storage space. The processor stores a device entry in the device storage space. The device entry is associated with a device external to the system update appliance and includes a component entry for a component of the device. The component operates based on an update. The component entry includes a description of the component and a pointer to a record stored in the CAS space. The processor stores the record in the CAS space. The record is associated with a combination of the component and the first update. The record includes the description, a second pointer to an update repository, and a third pointer to the object storage space.

Abstract: A system update appliance includes a processor and a memory device with a Content Addressable Storage (CAS) space and a location addressable storage space. The location addressable storage space partitioned into an object storage space and a device storage space. The processor stores a device entry in the device storage space. The device entry is associated with a device external to the system update appliance and includes a component entry for a component of the device. The component operates based on an update. The component entry includes a description of the component and a pointer to a record stored in the CAS space. The processor stores the record in the CAS space. The record is associated with a combination of the component and the first update. The record includes the description, a second pointer to an update repository, and a third pointer to the object storage space.

Abstract: Methods, apparatus, and processor-readable storage media for automatically allocating device resources using machine learning techniques are provided herein. An example computer-implemented method includes determining values for multiple parameters from data obtained from one or more devices; generating at least one device component failure prediction by applying one or more machine learning techniques to at least a portion of the determined values; computing one or more qualifying values attributable to the at least one generated device component failure prediction by providing the at least one generated device component failure prediction to a conformal prediction framework; and automatically allocating one or more resources to at least a portion of the one or more devices based at least in part on the at least one generated device component failure prediction and the one or more computed qualifying values.

Abstract: Tickets with RFID devices are issued to train passengers. Once the passengers board the train, the number and location of vacant seats is determined based on signals from the RFID devices detected by RFID readers configured within the passenger compartments of the train. Seating information is displayed on screens fitted in each compartment. This is done at or after the train reaches every major station, facilitating wait-listed passengers to occupy any vacant seats that become available.