Alibaba Group Holding Limited

Jun Song

Yijun Lu

Youquan Feng

Zhengxiong Tian

Ying Chu Wang

Jiesheng Wu

Bo Yang

Bo Chen Zhu

Yi Liu

Ling Xu

Qingda Lu

Hong Tang

Zhiyang Tang

Lingling Jin

Hong Zhou

Zhu Pang

Lin Cheng

Guan Wang

Lin Yu

Jiangwei Huang

Junjie Cai

Haihong Xu

Jun Ruan

Yu Zhou

Shiwei Li

Zhixing Ren

Chao Qian

Xiaolin Meng

Nan Zhang

Shuiwang Liu

Linquan Jiang

Ying Wang

Apparatus and methods are provided for improving thermal control, including collecting data of a plurality of systems, each of the plurality of systems including at least one first cooling element and at least one first heat-generating element; conducting a first simulation using a simulation model based on the collected data to generate a first set of simulation results; conducting a first training on a control system using the first set of simulation results to obtain a first trained control system; and using the first trained control system to monitor a field system with a space having at least one second cooling element and at least one second heat-generating element and to control the at least one second cooling element and the at least one second heat-generating element.

Thermal control optimization based on monitoring/control mechanism

Apparatuses and methods for providing resources are provided that include receiving power statuses of resources of a system capable of providing the resources; quantifying the power statuses of the resources; calculating an available soft capacity of the system based on the quantified power statuses and a total capacity of the system; and providing an assigning amount of the resources beyond the calculated available soft capacity to one or more users.

Power management method

A method and a system for scheduling a virtual machine are disclosed. The method includes: obtaining energy consumption data of a rack and energy consumption data of host computers, wherein the rack is used to place the host computers; determining power consumption data of a virtual machine; determining a target host computer corresponding to the virtual machine according to the energy consumption data of the rack, the energy consumption data of the host computers, and the power consumption data of the virtual machine; and dispatching the virtual machine to the target host computer. The present disclosure solves the technical problems of unbalanced power consumption scheduling caused by virtual machine scheduling methods that are implemented only according to CPU parameters in the existing technologies.

Virtual machine scheduling method and system

System and methods may include receiving a request for assigning resources to a first application associated with a first entity, the first application having a plurality of levels of performance each corresponding to a different power consumption; and determining a particular level of performance of the first application to run on the resource under a first constraint that a sum of a power consumption of the first application running at the particular level of performance and power consumptions of one or more other resources running other applications associated with the first entity is less than or equal to a power budget associated with the first entity.

Unified power management

A power management system may provide power management recommendations to a computer system including a plurality of computing nodes (which may include processors, etc.), to cause the computing nodes to individually or collectively adjust power states or modes of respective processors to achieve power management of the computer system. The power management system may be provided with a power management framework that continuously utilizes direct and indirect service-level feedbacks to guide power management decisions. The power management system may employ a reinforcement learning algorithm to make power management decisions at a user level, and provide a fast decision overriding mechanism for platform events or service-requested performance boosts.

Service-level feedback-driven power management framework

Systems and methods for a hybrid system-on-chip usable for predicting performance and power requirements of a host server include a big cores module, including central processing units, for receiving and pre-processing performance and power metrics data of the host server and to allocate computing resources, a small cores module, including massively parallel processing units, for mapping each instance associated with host server in the performance and power metrics data to a corresponding massively parallel processing unit based on the allocated computing resources for a per-instance metrics calculation, and an artificial intelligence (AI) accelerator for calculating performance and power prediction results based on the per-instance calculations from the small cores module.

Hybrid system-on-chip for power and performance prediction and control

Growing community of inventors

Tacoma, WA, United States of America

Jun Song