International Business Machines Corporation

Boe Technology Group Co., Ltd.

Baidu Online Network Technology (beijing) Co., Ltd.

Tsinghua University

Toshiba Medical Systems Corporation

Microsoft Technology Licensing, LLC

Canon Medical Systems Corporation

Beijing Baidu Netcom Science and Technology Co., Ltd.

China Petroleum & Chemical Corporation

Beijing Research Institute of Chemical Industry, China Petroleum & Chemical Corporation

Nanjing Industrial Furnace Institute of Tianhua of Chemical Machinery & Automation

Sinopac Engineering Incorporation

Guang Cheng Li

Jing Min Xu

Yuan Wang

Lin Y Yang

Jian Ma

Xiao Xi Liu

Yubo Li

Qi Ming Teng

Chao Zhu

Yanyan Hu

Ming Lei Zhang

Yu Zhu

Xinshe Yin

Yong Sen Zheng

Kaiming Yang

Wensheng Yin

Jinsong Wang

Guanghong Duan

Ying Wang

Liang Sun

Jiawei Gu

Dengfeng Xu

Yuning Du

Guoyi Liu

Lin Feng Shen

Rong Yan

Li Ni Zhang

Libin Liu

Chen Hui Xu

Lei Li

Cheng Fang Wang

Tuo Sun

Li Tian

Yan Xu

Jing Yan Ma

Guoqing Wang

Tian Xia

Li Cao

Sergio Paolantonio

Jinchun Hu

Chang Huang

Xiang Cao

Zuqiang Wang

Hiroki Yoshiara

Lijun Zhang

Li Xu

Yujie Li

Shanshan Liu

Shufu Xie

Jingtuo Liu

Yunlong Zhang

Xiaoyong Lu

Zhe Tang

Chen Xu

Jiabin He

Xiou He

Changli Li

Yuping Guo

Chenxia Li

Fuguo Zhu

Jianping Hao

Chen Shao

Yuhua Liao

Shasha Yan

Zhengping Wei

Sicong Zhao

Lingying Chen

Jinke Li

Methods and systems for managing persistent volumes include mounting a distributed filesystem, located outside the processing node, to a local mount point on the processing node. Access to the local mount point is given to a container on the processing node.

Host-level persistent volume management for container clouds

Disclosed are a computer-implemented method, a system, and a computer program product for system-level tunable parameter identification. Performance characteristic data for an application to be tuned can be obtained by one or more processing units. At least one system-level tunable parameter for the application to be tuned can be identified by one or more processing units based on the obtained performance characteristic data for the application to be tuned and a pattern between training performance characteristic data and a set of training system-level parameter-related correlation coefficients. The set of training system-level parameter-related correlation coefficients can be respective correlation coefficients of system-level tunable parameters with respect to at least one performance metric.

System-level tunable parameter identification

A computer-implemented method, system, and computer program product are provided for optimization with domain knowledge requirements. The method includes receiving, by a processor device, domain knowledge requirements for a target system. The method also includes defining, by a domain knowledge manager, a status of the domain knowledge employing a factor responsive to the domain knowledge. The method additionally includes computing, by an effective metric generator, effective performance metrics responsive to the status of the domain knowledge requirements and real performance metrics. The method further includes generating, by an optimization engine, a target system configuration responsive to the effective performance metrics and the real performance metrics. The method also includes improving the target system by changing a state of a function in the target system responsive to the target system configuration.

Target system optimization with domain knowledge

Methods and systems for deploying images to computing systems include predicting an environment for a plurality of processing nodes. Image deployment to the plurality of processing nodes is simulated to determine a subset of the plurality of processing nodes for deployment. One or more images is pre-loaded to the subset of the plurality of processing nodes in advance of a deployment time.

Accelerating large-scale image distribution

The present disclosure provides an image recognition method and apparatus, a device and a non-volatile computer storage medium. In embodiments of the present disclosure, it is feasible to obtain the to-be-recognized image of a designated size, extract the different-area image from the to-be-recognized image, and obtain the image feature of the different-area image according to the different-area image, so as to obtain the recognition result of the to-be-recognized image, according to the image feature of the different-area image and a preset template feature. In this way, recognition processing can be performed for images in a limited number of classes without employing the deep learning method based on hundreds of thousands of even millions of training samples.

Image recognition method and apparatus, device and non-volatile computer storage medium

Method and apparatus for reducing resource consumption in container image management. Embodiments include receiving directory information and checksum information corresponding to a plurality of files of a container image. Embodiments include determining, based on the directory information and the checksum information, whether each file of the plurality of files is present in a repository. For each respective file of the plurality of files that is determined to be present in the repository, embodiments include updating a file list for the container image to indicate a respective location in the repository of the respective file. For each given file of the plurality of files that is determined not to be present in the repository, embodiments include storing the given file at a given location in the repository and updating the file list for the container image to indicate the given location in the repository of the given file.

Reducing resource consumption in container image management

The present disclosure provides a computer-implemented method, computer system and computer program product for application containerization. According to the computer-implemented method, an application to be containerized can be traced. Information obtained in the tracing can be analyzed to determine one or more features of the application. An image template for the application can be created based on the one or more features. Then, a container image for the application can be built based on the image template.

Application containerization based on trace information

Embodiments of the present invention relate to a computer-implemented method, system and computer program product facilitating launching a middleware-based application. In some embodiments, a computer-implemented method includes: preparing, by a device operatively coupled to one or more processing units, an execution environment for middleware to be included in a container hosted on a machine; detecting, by the device, a request to schedule an application to be executed in the machine using the middleware; and in response to the request being detected, launching, by the device, the application within the container based on the prepared execution environment.

Launching a middleware-based application

A log augmentation system is provided for augmenting log management in a distributed micro-services environment. The log augmentation system includes a trigger device for triggering an augmentation process responsive to micro-service related events and a context of the micro-service related events. The log augmentation system further includes an augmentation rules database with rule management components for determining an augment direction, an augmentation target, and an augmentation correlation relating to the distributed micro-services. The log augmentation system also includes a coordinator for coordinating the augmentation process in the distributed micro-service using a rule-based decision approach. The log augmentation system additionally includes a labeler for applying a set of labels to augment log entries responsive to instructions from the coordinator derived from results of the rule-based decision approach used by the coordinator.

Logging implementation in micro-service applications

Examples of techniques for running kernel dependent applications in containers are disclosed. In one example implementation, a method includes initiating, by a processing system, a user mode Linux (UML) kernel in a container of a host. The method further includes initiating, by the processing system, an agent inside the UML, the agent configured to handle system call redirection from a host kernel of the host. The method further includes enabling, by the processing system, a system call hook kernel module on the host to hook a system call received from the container. The method further includes determining, by the processing system, whether the UML kernel is running inside the container. The method further includes, based at least in part on determining that the UML is running inside the container, passing, by the processing system, the system call to the UML kernel inside the container.

Running a kernel-dependent application in a container

A method, computer program product, and computer system are provided for running an application over a host kernel. The method intercepts a system call initiated by a container instance running on a host kernel, then determines whether the initiating container instance is tagged with a mapping to a virtual machine (VM) also running on the host kernel. The method further redirects the system call to the mapped VM responsive to the initiating container instance being tagged with a mapping to a VM, wherein the system call is to be processed by the VM.

Running kernel dependent applications inside a container

Methods and systems for managing persistent volumes include receiving a request from a container on a processing node to access a local mount point. A distributed filesystem located outside the processing node is mounted to a local mount point. Access to the local mount point is provided to the container.

Embodiments of the present invention relate to methods, systems, and computer program products for container image management. In a method, an image layer in a container image may be received by one or more processors, and the container image is to be stored in an image server comprising a group of image layers. A base portion may be selected by one or more processors from the group of image layers based on a similarity analysis between the image layer and the group of image layers. A patch portion may be generated by one or more processors based on a difference between the image layer and the selected base portion. With these embodiments, the container image may be stored based on multiple image layers, and thus the container image may be maintained in a much finer granularity so as to reduce requirements on the bandwidth and time cost for transmitting the container image.

Container image management

A resource management method, system, and computer program product for creating a dummy virtual machine (VM) in a Virtual Machine (VM) hypervisor and a dummy container in a container engine for a resource management purpose, adding hooks for each of the VM and the container, and calling the container engine to update a resource allocation in the dummy container when the hook of the VM is started and the VM hypervisor to update a resource allocation in the dummy VM when the hook of the container is started.

Managing system resources in containers and virtual machines in a coexisting environment

Growing community of inventors

Beijing, China

Guang Cheng Li