Abstract: The present invention discloses an angle-adjustable crowbar that comprises a crowbar body and a crowbar head. The crowbar head includes a first hinge base and a second hinge base in a spaced manner, and a head of the crowbar body is rotatably mounted between the first hinge base and the second hinge base through an angle adjustment mechanism. The angle adjustment mechanism comprises a limit shaft penetrating through the first hinge base, the crowbar body and the second hinge base, a first tooth portion, a polygonal limit portion and a second tooth portion are sequentially disposed on an outer wall of the limit shaft in an axial direction. The angle-adjustable crowbar overcomes the defects that the limit strength of existing crowbars is weak, the tooth portion is unlikely to be damaged, and the crowbar head cannot be replaced.

Abstract: The present invention discloses an angle-adjustable crowbar that comprises a crowbar body and a crowbar head. The crowbar head includes a first hinge base and a second hinge base in a spaced manner, and a head of the crowbar body is rotatably mounted between the first hinge base and the second hinge base through an angle adjustment mechanism. The angle adjustment mechanism comprises a limit shaft penetrating through the first hinge base, the crowbar body and the second hinge base, a first tooth portion, a polygonal limit portion and a second tooth portion are sequentially disposed on an outer wall of the limit shaft in an axial direction. The angle-adjustable crowbar overcomes the defects that the limit strength of existing crowbars is weak, the tooth portion is unlikely to be damaged, and the crowbar head cannot be replaced.

Abstract: The present concepts may automate and optimize deployment to a cloud computing fleet. Artificial intelligence and/or optimization algorithms may be used to find optimal deployment parameters (e.g., deployment order of computers in the fleet) that minimize deployment time and minimize deployment risk. For example, machine-learning prediction models may be used to generate a shortest path graph problem models a deployment planning problem. Then, optimization algorithms may be used to efficiently find approximations of Pareto-optimal solutions to the shortest path graph problem. Depending on the preferred level of time and risk, one of the solutions may be used to run the deployment.

Abstract: The present concepts may automate and optimize deployment to a cloud computing fleet. Artificial intelligence and/or optimization algorithms may be used to find optimal deployment parameters (e.g., deployment order of computers in the fleet) that minimize deployment time and minimize deployment risk. For example, machine-learning prediction models may be used to generate a shortest path graph problem models a deployment planning problem. Then, optimization algorithms may be used to efficiently find approximations of Pareto-optimal solutions to the shortest path graph problem. Depending on the preferred level of time and risk, one of the solutions may be used to run the deployment.

Abstract: Methods, systems, apparatuses, and computer program products are provided that enable the automated deployment of microservices to a network-accessible server set. The automated deployment may be based on constraint(s) that are specified by a declarative deployment model that is associated with the microservice to be deployed. For example, a centralized deployment orchestrator may receive microservice(s) and their associated declarative deployment model(s). The deployment orchestrator analyzes the declarative deployment model(s) and determines which microservice(s) are to be deployed based on the constraint(s) specified by the declarative deployment model(s). The foregoing techniques advantageously determine when to deploy microservice(s), while also minimizing human intervention typically required to deploy microservice(s).

Abstract: Methods, systems, and apparatuses manage rolling out of updates in a network-accessible server infrastructure which operates a plurality of instances of a supporting service. The supporting service is comprised by a plurality of service portions. The instances of the supporting service each include of the service portions. The instances of the supporting service are partitioned into a plurality of slices. Each instance is partitioned to include one or more of the slices, and each slice of an instance includes one or more of the service portions. A software update is deployed to the instances by applying the software update to the slices in a sequence such that the software update is applied to a same slice in parallel across the instances containing that same slice before being applied to a next slice, and waiting a wait time before applying the software domain to a next slice in the sequencing.

Abstract: Methods, systems, and apparatuses manage rolling out of updates in a network-accessible server infrastructure which operates a plurality of instances of a supporting service. The supporting service is comprised by a plurality of service portions. The instances of the supporting service each include of the service portions. The instances of the supporting service are partitioned into a plurality of slices. Each instance is partitioned to include one or more of the slices, and each slice of an instance includes one or more of the service portions. A software update is deployed to the instances by applying the software update to the slices in a sequence such that the software update is applied to a same slice in parallel across the instances containing that same slice before being applied to a next slice, and waiting a wait time before applying the software domain to a next slice in the sequencing.

Abstract: Methods, systems, and apparatuses manage rolling out of updates in a network-accessible server infrastructure which operates a plurality of instances of a supporting service. The supporting service is comprised by a plurality of service portions. The instances of the supporting service each include of the service portions. The instances of the supporting service are partitioned into a plurality of slices. Each instance is partitioned to include one or more of the slices, and each slice of an instance includes one or more of the service portions. A software update is deployed to the instances by applying the software update to the slices in a sequence such that the software update is applied to a same slice in parallel across the instances containing that same slice before being applied to a next slice, and waiting a wait time before applying the software domain to a next slice in the sequencing.

Abstract: Methods, systems, and apparatuses manage rolling out of updates in a network-accessible server infrastructure which operates a plurality of instances of a supporting service. The supporting service is comprised by a plurality of service portions. The instances of the supporting service each include of the service portions. The instances of the supporting service are partitioned into a plurality of slices. Each instance is partitioned to include one or more of the slices, and each slice of an instance includes one or more of the service portions. A software update is deployed to the instances by applying the software update to the slices in a sequence such that the software update is applied to a same slice in parallel across the instances containing that same slice before being applied to a next slice, and waiting a wait time before applying the software domain to a next slice in the sequencing.

Abstract: Methods, systems, apparatuses, and computer program products are provided that enable the automated deployment of microservices to a network-accessible server set. The automated deployment may be based on constraint(s) that are specified by a declarative deployment model that is associated with the microservice to be deployed. For example, a centralized deployment orchestrator may receive microservice(s) and their associated declarative deployment model(s). The deployment orchestrator analyzes the declarative deployment model(s) and determines which microservice(s) are to be deployed based on the constraint(s) specified by the declarative deployment model(s). The foregoing techniques advantageously determine when to deploy microservice(s), while also minimizing human intervention typically required to deploy microservice(s).