Abstract: Systems, methods, and other embodiments associated with unified control of cooling in computers are described. In one embodiment, a method locks operation of first and second cooling mechanisms configured to cool one or more components in the computer. In response to a first condition, the method unlocks the operation of the first cooling mechanism to allow the first cooling mechanism to make cooling adjustments while the operation of the second cooling mechanism is locked. In response to a second condition, the method unlocks the operation of the second cooling mechanism to allow the second cooling mechanism to make cooling adjustments while the operation of the first cooling mechanism is locked. In the method, the first cooling mechanism and the second cooling mechanism are prevented from making the cooling adjustments simultaneously.

Abstract: Systems, methods, and other embodiments associated with unified control of cooling in computers are described. In one embodiment, a method locks operation of first and second cooling mechanisms configured to cool one or more components in the computer. In response to a first condition, the method unlocks the operation of the first cooling mechanism to allow the first cooling mechanism to make cooling adjustments while the operation of the second cooling mechanism is locked. In response to a second condition, the method unlocks the operation of the second cooling mechanism to allow the second cooling mechanism to make cooling adjustments while the operation of the first cooling mechanism is locked. In the method, the first cooling mechanism and the second cooling mechanism are prevented from making the cooling adjustments simultaneously.

Abstract: Systems, methods, and other embodiments associated with detecting feedback control instability in computer thermal controls are described herein. In one embodiment, a method includes for a set of dwell time intervals, wherein the dwell time intervals are associated with a range of periods of time from an initial period to a base period, executing a workload that varies from minimum to maximum over the period on a computer during the dwell time interval; recording telemetry data from the computer during execution of the workload; incrementing the period towards a base period; determining that either the base period is reached or a thermal inertia threshold is reached; and analyzing the recorded telemetry data over the set of dwell time intervals to either (i) detect presence of a feedback control instability in thermal control for the computer; or (ii) confirm feedback control stability in thermal control for the computer.

Abstract: Optimum software update transmission parameters are determined and used for transmitting a software update from a host to servers of a computer network. The software update is transmitted while the servers are live and required to meet certain quality of service requirements for tenants of the computer network. Transmission parameters for transmitting the software update are adjusted and updated based on service performance data. Based on iterative adjustments, optimum transmission parameters may be determined. Additionally or alternatively, machine learning is used to generate a model that determines predicted optimum transmission parameters. The predicted optimum transmission parameters may be initially used for transmitting a software update, while the transmission parameters continue to be adjusted throughout transmission.

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: Optimum software update transmission parameters are determined and used for transmitting a software update from a host to servers of a computer network. The software update is transmitted while the servers are live and required to meet certain quality of service requirements for tenants of the computer network. Transmission parameters for transmitting the software update are adjusted and updated based on service performance data. Based on iterative adjustments, optimum transmission parameters may be determined. Additionally or alternatively, machine learning is used to generate a model that determines predicted optimum transmission parameters. The predicted optimum transmission parameters may be initially used for transmitting a software update, while the transmission parameters continue to be adjusted throughout transmission.

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: Optimum software update transmission parameters are determined and used for transmitting a software update from a host to servers of a computer network. The software update is transmitted while the servers are live and required to meet certain quality of service requirements for tenants of the computer network. Transmission parameters for transmitting the software update are adjusted and updated based on service performance data. Based on iterative adjustments, optimum transmission parameters may be determined. Additionally or alternatively, machine learning is used to generate a model that determines predicted optimum transmission parameters. The predicted optimum transmission parameters may be initially used for transmitting a software update, while the transmission parameters continue to be adjusted throughout transmission.

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: Optimum software update transmission parameters are determined and used for transmitting a software update from a host to servers of a computer network. The software update is transmitted while the servers are live and required to meet certain quality of service requirements for tenants of the computer network. Transmission parameters for transmitting the software update are adjusted and updated based on service performance data. Based on iterative adjustments, optimum transmission parameters may be determined. Additionally or alternatively, machine learning is used to generate a model that determines predicted optimum transmission parameters. The predicted optimum transmission parameters may be initially used for transmitting a software update, while the transmission parameters continue to be adjusted throughout transmission.

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.