Abstract: A scaling policy associated with a notification received by one or more computer systems is obtained. A first request is submitted, to a software container service, for a first current capacity of a resource. An amount by which to adjust a capacity of the resource is calculated, based at least in part on the scaling policy and the first current capacity. A second request is submitted, to the software container service, to adjust the capacity of the resource by the amount. A third request is submitted, to the software container service, for a second current capacity of the resource, and whether the second request has been fulfilled is determined based at least in part on a comparison between the second current capacity and the amount.

Abstract: In response to receipt of a notification from a third service, a scaling policy specified by a customer of a computing resource service provider to be associated with the notification is obtained, with the scaling policy including a set of parameters that includes an identity of a resource of a second service of the computing resource service provider. As a result of processing the scaling policy in accordance with the set of parameters, a request is submitted to a second service to scale the resource, and output is provided that indicates whether the scaling request has been fulfilled.

Abstract: Techniques are described for scaling a group of computing resources. A computing resource service receives a scaling policy for use in scaling the group of computing resources. The scaling policy specifies a target level for a resource utilization metric and magnitude-based changes to the group. The computing resource service receives information about a magnitude of a measurement for the resource utilization metric. The computing resource service determines, based at least in part on the scaling policy, one or more changes for the group and initiates the one or more changes in the group.

Abstract: In response to receipt of a notification from a third service, a scaling policy specified by a customer of a computing resource service provider to be associated with the notification is obtained, with the scaling policy including a set of parameters that includes an identity of a resource of a second service of the computing resource service provider. As a result of processing the scaling policy in accordance with the set of parameters, a request is submitted to a second service to scale the resource, and output is provided that indicates whether the scaling request has been fulfilled.

Abstract: A scaling policy associated with a notification received by one or more computer systems is obtained. A first request is submitted, to a software container service, for a first current capacity of a resource. An amount by which to adjust a capacity of the resource is calculated, based at least in part on the scaling policy and the first current capacity. A second request is submitted, to the software container service, to adjust the capacity of the resource by the amount. A third request is submitted, to the software container service, for a second current capacity of the resource, and whether the second request has been fulfilled is determined based at least in part on a comparison between the second current capacity and the amount.

Abstract: Systems, methods and articles solve computationally complex problems. Example embodiments provide data query language features that may be used to express optimization problems. An expression of an optimization problem in the provided data query language may be transformed into a primitive problem that is equivalent to the optimization problem. An optimization solver may be invoked to provide a solution to the primitive problem. Analog processors such as quantum processors as well as digital processors may be used to solve the primitive problem. This abstract is provided to comply with rules requiring an abstract, and is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: Systems, methods and articles for performing preference filtering on a database query. Example embodiments provide a new algorithm, called BNL#, that operates with a worst-case running time of O(ntce+ne+np) where {ne, np}<<nt?O(ntce). This represents an improvement over existing algorithms that are known in the art, such as the BNL algorithm which operates with a worst-case running time of O(nt2) and the BNL++ algorithm which operates with a worst-case running time of O(ntceneme), with ne<<nt. This abstract is provided to comply with rules requiring an abstract, and is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.