32 results about "Orchestration (computing)" patented technology

An example computing system includes a database disposed within a remote network management platform associated with a managed network that obtains service from a computing cluster that includes one or more worker nodes configured to execute containerized software applications using a containerized orchestration engine. The computing system also includes a computing device configured to identify a namespace associated with the containerized orchestration engine. The computing device is also configured to query a deployment configuration application programming interface (API) associated with a containerized application platform to obtain deployment configuration data. Further, the computing device is configured to query a build configuration API associated with the containerized application platform to obtain build configuration data. In addition, the computing device is configured to store, in the database, the deployment configuration data, the build configuration data, and one or more relationships between the deployment configuration data and the build configuration data.

The invention discloses a method for scheduling Zeppelin tasks. The method comprises the following steps that Zeppelin generates notebook containing a plurality of tasks; analyzing to obtain a dependency relationship between the tasks; generating an arrangement file according to the dependency relationship; completing tasks from orchestration files. The execution sequence of the Zeppelin tasks with the dependency relationship does not need to be manually set, and the Zeppelin tasks with the dependency relationship can be automatically scheduled. The invention further provides a system for scheduling the Zeppelin tasks, computing equipment and a storage medium.

The invention discloses a resource management method and device, a storage medium and an electronic device in the field of cloud computing. The method comprises the steps of acquiring a target command, wherein the target command is used for indicating that the state of target resources in the container arrangement system needs to be changed; obtaining a first state and a second state of the targetresource, with the first state being a target state of the target resource recorded in the target warehouse, and the second state being a state of the target resource recorded in the container orchestration system; and if the second state is not matched with the first state, executing an operation of changing the state of the target resource from the second state to the first state in the container orchestration system. According to the invention, the technical problem of inaccurate operation results in related technologies is solved.

A server computer (DNPS) commissions/decommissions networks provisioned using one or more orchestration solutions (OS) in a client-server architecture. Program code instructions instructing the servercomputer to implement a user interface (UI) for remote management of the server computer, wherein the user interface provides access to data managed by the server computer and a web- based application programming interface (API) that supports service oriented architecture ['SOA'], and a network management logic (NME) that dynamically assigns and releases networks via the one or more orchestrationsolutions (OS) and the web-based application programming interface (API). In an alternative implementation, the network management logic cooperates with Software-Defined Networking Controller (s) SDNC to commissions/decommission networks. A physical embodiment may implement either or both of the SOA-based and the SDN-based.

Concepts and technologies are described herein for providing an automated mechanism for grouping devices to allow safe and scalable actions to be taken in parallel. A computing device obtains data that defines service relationships between computing entities in a network of computing entities controlled by a service provider. The computing device determines two or more groups of computing entitieshaving one of a direct or indirect relationship with other computing entities within one of the two or more groups based on the obtained data. Then, the computing device determines one or more subgroups of one of the two or more groups based on the obtained data. Individual computing entities within a first subgroup of the one or more subgroups do not have a direct relationship with any of the other computing entities with the first subgroup. Output data identifying at least a portion of the subgroups is generated.

A hyper-converged hybrid architecture computing material platform includes: an infrastructure module configured to implement a cloud host service, a cloud storage service, a high performance computing service, and a research and development environment support service with a resilient infrastructure architecture; the data service module that is configured to manage data by taking a data packet as granularity, improve data productivity by constructing an annotation system and standardizing a data annotation behavior, shield various heterogeneous storage and file systems, and realize operation of various data tasks by taking a shielding layer as a support so as to realize data management; the platform service module that is configured to realize an application service by integrating a plurality of HPC applications related to computing materials and combining access of a data warehouse, and realize a development service through an HPC application orchestration subsystem, a Monte Carlo algorithm and a general algorithm library; an application software module that is configured to implement compilation and operation of a computing job by assigning computing material-related software; and a user interface module.

A system using a suite of modular and clearly structured Artificial Intelligence application design tools (SOACAIA), executable on distributed or undistributed computing platforms to browse, develop, make available and manage AI applications, this set of tools implementing three functions. A Studio function making it possible to establish a secure and private shared space for the company. A Forge function making it possible to industrialize AI instances and make analytical models and their associated datasets available to the development teams. An Orchestration function for managing the total implementation of the AI instances designed by the Studio function and industrialized by the Forge function and to perform permanent management on a hybrid cloud or HPC infrastructure.

A system and method are disclosed for orchestrating the execution of computing tasks. An orchestration engine can receive task requests over a network from a plurality of process engines. The process engines may correspond to respective edge or field devices that are remotely located as compared to the orchestration engine. Each task request may indicate at least one task requirement for executing a respective computing task. A plurality of computing instances that have available computing resources can be selected from a set of computing instances. A predicted runtime can be generated for each of the computing tasks. In an example, based on the predicted runtimes, task requirements, available computing resources, and associated network conditions, a schedule and allocation scheme are determined by the orchestration engine. The schedule and allocation scheme define when each of the plurality of computing tasks is performed, and which of the plurality of selected computing instances performs each of the plurality of computing tasks. The selected computing instances execute the plurality of computing tasks according to the schedule and allocation scheme.