Amazon Technologies, Inc.

Kai Fan Tang

Ahmed Usman Khalid

Christopher Thomas Lewis

Farzad Moghimi

Stephan Weinwurm

Jacob Adam Gabrielson

Joshua Marc Burgin

Brad Bonnett

Derek Solomon Pai

Eric Samuel Stone

Alison Qing-Ning Truong

Malcolm Featonby

John Merrill Phillips

Leslie Johann Lamprecht

Letian Feng

Mai-Lan Tomsen Bukovec

Roberto Pentz De Faria

Manwah Wong

Nayanish Hinge

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 notification for an application stack is received, where the application stack includes a plurality of resource types. At least one policy associated with the notification is obtained, with the first policy being a policy for scaling a first resource of a first resource type and a second resource of a second resource type of the application stack. A first capacity for the first resource and a second capacity for the second resource is determined based at least in part on the at least one policy. The first resource and the second resource are caused to be scaled according to the first capacity and the second capacity respectively.

Customer resource monitoring for versatile scaling service scaling policy recommendations

Techniques are described for optimizing the allocation of computing resources provided by a service provider network&#x2014;for example, compute resources such as virtual machine (VM) instances, containers, standalone servers, and possibly other types of computing resources&#x2014;among computing workloads associated with a user or group of users of the service provider network. A service provider network provides various tools and interfaces to help businesses and other organizations optimize the utilization of computing resource pools obtained by the organizations from the service provider network, including the ability to efficiently schedule use of the resources among workloads having varying resource demands, usage patterns, relative priorities, execution deadlines, or combinations thereof. A service provider network further provides various graphical user interfaces (GUIs) to help users visualize and manage the historical and scheduled uses of computing resources by users' workloads according to user preferences.

Coordinated predictive autoscaling of virtualized resource groups

Interactive GUI for bin-packing virtual machine workloads based on predicted availability of compute instances and scheduled use of the compute instances

Bin-packing virtual machine workloads using forecasted capacity usage

Techniques for an optimization service of a service provider network to generate an architecture diagram that represents an architecture of a web-based application. The optimization service may use the architecture diagram to determine modifications or changes to make to the application. For example, the optimization service may compare the architecture diagram with optimized architecture diagrams that represent application best practices, and determine the modifications or change to make to the application to optimize the application and bring the application in-line with best practices. Further, the optimization service may use the architecture diagram to generate a visualization, and provide the user account with the visualization of the architecture diagram to show users their application architecture.

Application architecture optimization and visualization

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.

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.

Prioritization-based scaling of computing resources

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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Coquitlam, Canada

Kai Fan Tang