Amazon Technologies, Inc.

Christopher Thomas Lewis

Kai Fan Tang

Farzad Moghimi

Stephan Weinwurm

Ahmed Usman Khalid

Manwah Wong

Ankush Singla

Nayanish Hinge

Wilson Lau

Systems and techniques are disclosed for a buffer management system that dynamically and automatically adjusts operational buffers in resources based on resource utilization based on buffer parameters. Buffer parameters for a resource can include a target utilization, a buffer capacity, a minimum buffer capacity, and a maximum buffer capacity. The buffer management detects changes in utilization, such as a number of operations performed in a time period and determines whether a candidate buffer capacity determined using the buffer capacity parameter and the current utilization is between the minimum buffer capacity and the maximum buffer capacity. If so, the buffer management system sets the buffer capacity to the candidate buffer capacity, otherwise the buffer management system sets the buffer capacity to the minimum or maximum buffer capacity as applicable.

Resource buffer capacity management

Techniques are described for filtering and normalizing training data used to build a predictive auto scaling model used by a service provider network to proactively scale users' computing resources. Further described are techniques for identifying collections of computing resources that exhibit suitably predictable usage patterns such that a predictive auto scaling model can be used to forecast future usage patterns with reasonable accuracy and to scale the resources based on such generated forecasts. The filtering of training data and the identification of suitably predictable collections of computing resources are based in part on autocorrelation analyses, and in particular on 'delayed' autocorrelation analyses, of time series data, among other techniques described herein.

Using delayed autocorrelation to improve the predictive scaling of computing resources

A policy associated with a notification received by one or more computer systems is obtained. A first request is submitted, to a 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 policy and the first current capacity. A second request is submitted, to the service, to adjust the capacity of the resource by the amount. A third request is submitted, to the 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 response to the third request.

Versatile autoscaling for containers

A scaling notification is received that includes a current capacity for a scalable target of a resource. A policy associated with the scaling notification is obtained. A new capacity is computed for the scalable target, based at least in part on the policy and the current capacity of the scalable target. A scaling request is made to an application programming interface endpoint for the scalable target, with the request including a new capacity for the scalable target, thereby causing the current capacity of the scalable target to change to the new capacity.

On-premises scaling using a versatile scaling service and an application programming interface management service

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.

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.

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Vancouver, Canada

Christopher Thomas Lewis