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    Using delayed autocorrelation to improve the predictive scaling of computing resources
    1.
    发明授权
    Using delayed autocorrelation to improve the predictive scaling of computing resources 有权

    公开(公告)号：US11550635B1

    公开(公告)日：2023-01-10

    申请号：US16368775

    申请日：2019-03-28

    Applicant: Amazon Technologies, Inc.

    Inventor： Manwah Wong ,  Kai Fan Tang ,  Christopher Thomas Lewis

    IPC: G06F9/50 ,  G06F9/455 ,  G06F17/15 ,  G06N5/02 ,  G06F16/901 ,  G06N3/08

    Abstract: 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.

    Bin-packing virtual machine workloads using forecasted capacity usage
    2.
    发明授权
    Bin-packing virtual machine workloads using forecasted capacity usage 有权

    公开(公告)号：US11221887B2

    公开(公告)日：2022-01-11

    申请号：US16362539

    申请日：2019-03-22

    Applicant: Amazon Technologies, Inc.

    Inventor： Jacob Adam Gabrielson ,  Joshua M. Burgin ,  Brad Bonnett ,  Kai Fan Tang

    IPC: G06F9/50 ,  G06N3/08 ,  G06F9/451

    Abstract: Techniques are described for optimizing the allocation of computing resources provided by a service provider network—for example, compute resources such as virtual machine (VM) instances, containers, standalone servers, and possibly other types of computing resources—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.

    Versatile autoscaling
    3.
    发明授权
    Versatile autoscaling 有权

    公开(公告)号：US10069869B2

    公开(公告)日：2018-09-04

    申请号：US15194479

    申请日：2016-06-27

    Applicant: Amazon Technologies, Inc.

    Inventor： Christopher Thomas Lewis ,  Kai Fan Tang ,  Farzad Moghimi ,  Ahmed Usman Khalid ,  Stephan Weinwurm

    IPC: H04L29/06 ,  H04L29/08 ,  H04Q9/02

    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.

    SOLVER EXECUTION SERVICE MANAGEMENT
    4.
    发明公开
    SOLVER EXECUTION SERVICE MANAGEMENT 审中-公开

    公开(公告)号：US20240111832A1

    公开(公告)日：2024-04-04

    申请号：US17936801

    申请日：2022-09-29

    Applicant: Amazon Technologies, Inc.

    Inventor： Shreyas Vathul Subramanian ,  Amey K Dhavle ,  Guvenc Degirmenci ,  Kai Fan Tang ,  Daniel Romero

    IPC: G06F17/18

    CPC classification number: G06F17/18

    Abstract: A multitenant solver execution service provides managed infrastructure for defining and solving large-scale optimization problems. In embodiments, the service executes solver jobs on managed compute resources such as virtual machines or containers. The compute resources can be automatically scaled up or down based on client demand and are assigned to solver jobs in a serverless manner. Solver jobs can be initiated based on configured triggers. In embodiments, the service allows users to select from different types of solvers, mix different solvers in a solver job, and translate a model from one solver to another solver. In embodiments, the service provides developer interfaces to, for example, run solver experiments, recommend solver types or solver settings, and suggest model templates. The solver execution service relieves developers from having to manage infrastructure for running optimization solvers and allows developers to easily work with different types of solvers via a unified interface.

    Customer resource monitoring for versatile scaling service scaling policy recommendations
    5.
    发明授权
    Customer resource monitoring for versatile scaling service scaling policy recommendations 有权

    公开(公告)号：US11347549B2

    公开(公告)日：2022-05-31

    申请号：US16565051

    申请日：2019-09-09

    Applicant: Amazon Technologies, Inc.

    Inventor： Kai Fan Tang ,  Ahmed Usman Khalid

    IPC: G06F9/50

    Abstract: 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.

    MULTI-TENANT SOLVER EXECUTION SERVICE
    6.
    发明公开
    MULTI-TENANT SOLVER EXECUTION SERVICE 审中-公开

    公开(公告)号：US20240111831A1

    公开(公告)日：2024-04-04

    申请号：US17936789

    申请日：2022-09-29

    Applicant: Amazon Technologies, Inc.

    Inventor： Shreyas Vathul Subramanian ,  Amey K Dhavle ,  Guvenc Degirmenci ,  Kai Fan Tang ,  Daniel Romero

    IPC: G06F17/18

    CPC classification number: G06F17/18

    Abstract: A multitenant solver execution service provides managed infrastructure for defining and solving large-scale optimization problems. In embodiments, the service executes solver jobs on managed compute resources such as virtual machines or containers. The compute resources can be automatically scaled up or down based on client demand and are assigned to solver jobs in a serverless manner. Solver jobs can be initiated based on configured triggers. In embodiments, the service allows users to select from different types of solvers, mix different solvers in a solver job, and translate a model from one solver to another solver. In embodiments, the service provides developer interfaces to, for example, run solver experiments, recommend solver types or solver settings, and suggest model templates. The solver execution service relieves developers from having to manage infrastructure for running optimization solvers and allows developers to easily work with different types of solvers via a unified interface.

    Versatile autoscaling for containers
    7.
    发明授权
    Versatile autoscaling for containers 有权

    公开(公告)号：US10397240B2

    公开(公告)日：2019-08-27

    申请号：US16195645

    申请日：2018-11-19

    Applicant: Amazon Technologies, Inc.

    Inventor： Christopher Thomas Lewis ,  Kai Fan Tang ,  Farzad Moghimi ,  Ahmed Usman Khalid ,  Stephan Weinwurm

    IPC: H04L29/06 ,  H04L29/08 ,  H04Q9/02

    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.

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