Oracle International Corporation

Sanjeev Raghavendrachar Sondur

Kenny C Gross

Guang Chao Wang

James Rohrkemper

Kenneth Clayton Gross

Prasanta Ranjan Dash

Brandon Patrick Byers

Mark Evan Mills

Nitin B Agrawal

Eric Tran-Le

Kalyan Vaidyanathan

John Matthew Lloyd Bentley

Raja Ramaswamy

Kalyanaraman Vaidyanathan

Matthew T Gerdes

Systems, methods, and other embodiments associated with detecting feedback control instability in computer thermal controls are described herein. In one embodiment, a method includes executing a workload on the computing system, wherein the workload varies between a minimum and a maximum at a workload frequency. The method includes recording thermal telemetry from the computing system during execution of the workload. The method includes converting the recorded thermal telemetry into a frequency domain. The method includes detecting whether thermal control of the computing system exhibits feedback control instability based on dissimilarity in the frequency domain between the transformed thermal telemetry and the workload frequency. And, the method includes generating an electronic alert that indicates whether the thermal control of the computing device exhibits the feedback control instability.

Detection of feedback control instability in computing device thermal control

A model-based approach to determining an optimal configuration for a data center may use an environmental chamber to characterize the performance of various data center configurations at different combinations of temperature and altitude. Telemetry data may be recorded from different configurations as they execute a stress workload at each temperature/altitude combination, and the telemetry data may be used to train a corresponding library of models. When a new data center is being configured, the temperature/altitude of the new data center may be used to select a pre-trained model from a similar temperature/altitude. Performance of the current configuration can be compared to the performance of the model, and if the model performs better, a new configuration based on the model may be used as an optimal configuration for the data center.

Pattern-recognition enabled autonomous configuration optimization for data centers

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.

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.

Unified control of cooling in computers

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.

Determining optimum software update transmission parameters

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.

Method and apparatus for configuring a cloud storage software appliance

The disclosed embodiments relate to a system that performs low-temperature desalination. During operation, the system feeds cold saline water through a liquid-cooling system in a computer data center, wherein the cold saline water is used as a coolant, thereby causing the cold saline water to become heated saline water. Next, the system feeds the heated saline water into a vacuum evaporator comprising a water column having a headspace, which is under a negative pressure due to gravity pulling on the heated saline water in the water column. This negative pressure facilitates evaporation of the heated saline water to form water vapor. Finally, the system directs the water vapor through a condenser, which condenses the water vapor to produce desalinated water.

Using waste heat from a data center cooling system to facilitate low-temperature desalination

The disclosed embodiments relate to a system that controls cooling in a computer system. During operation, this system monitors a temperature of one or more components in the computer system. Next, the system determines a thermal-headroom margin for each of the one or more components in the computer system by subtracting the temperature of the component from a pre-specified maximum operating temperature of the component. Then, the system controls a cooling system that regulates an ambient air temperature for the computer system based on the determined thermal-headroom margins for the one or more components. In some embodiments, controlling the cooling system additionally involves minimizing a collective energy consumption of the computer system and the cooling system.

Intelligent energy-optimization technique for computer datacenters

A method for determining parameters for one or more air-cooling mechanisms for a computer system. A current operating temperature of a system component within a particular system is determined. A maximum operating temperature of the system component is obtained. A difference value between the maximum operating temperature and the current operating temperature is computed. A parameter of an air-cooling mechanism of an environment, which includes the system component, is selected and modified based on the difference value between the maximum operating temperature and the current operating temperature.

Determining parameters of air-cooling mechanisms

The disclosed embodiments provide a system that intelligently migrates workload between servers in a data center to improve efficiency in associated power supplies. During operation, the system receives time-series signals associated with the servers during operation of the data center, wherein the servers include low-priority servers and high-priority servers. Next, the system analyzes the time-series signals to predict a load utilization for the servers. The system then migrates workload between the servers in the data center based on the predicted load utilization so that: the high-priority servers have sufficient workload to ensure that associated power supplies for the high-priority servers operate in a peak-efficiency range; and the low-priority servers operate with less workload or no workload.

Growing community of inventors

Horsham, PA, United States of America

Sanjeev Raghavendrachar Sondur