公开(公告)号：US10581964B2

公开(公告)日：2020-03-03

申请号：US15845855

申请日：2017-12-18

Applicant: Amazon Technologies, Inc.

Inventor： Jonathan Daly Einkauf ,  Luca Natali ,  Bhargava Ram Kalathuru ,  Saurabh Dileep Baji ,  Abhishek Rajnikant Sinha

IPC: H04L29/08 ,  G06F9/50 ,  H04L12/24 ,  H04L12/26

Abstract: A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.

公开(公告)号：US12069128B2

公开(公告)日：2024-08-20

申请号：US17352065

申请日：2021-06-18

Applicant: Amazon Technologies, Inc.

Inventor： Jonathan Daly Einkauf ,  Luca Natali ,  Bhargava Ram Kalathuru ,  Saurabh Dileep Baji ,  Abhishek Rajnikant Sinha

IPC: H04L41/0893 ,  G06F9/50 ,  H04L41/0894 ,  H04L41/0897 ,  H04L41/22 ,  H04L41/5041 ,  H04L43/0876 ,  H04L67/10 ,  H04L67/1031 ,  H04L67/1074

CPC classification number: H04L67/1076 ,  G06F9/5077 ,  G06F9/5083 ,  H04L41/0893 ,  H04L41/0894 ,  H04L41/0897 ,  H04L41/22 ,  H04L41/5045 ,  H04L43/0876 ,  H04L67/10 ,  H04L67/1031

Abstract: A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.

公开(公告)号：US11044310B2

公开(公告)日：2021-06-22

申请号：US16805412

申请日：2020-02-28

Applicant: Amazon Technologies, Inc.

Inventor： Jonathan Daly Einkauf ,  Luca Natali ,  Bhargava Ram Kalathuru ,  Saurabh Dileep Baji ,  Abhishek Rajnikant Sinha

IPC: H04L29/08 ,  H04L12/26 ,  H04L12/24 ,  G06F9/50

Abstract: A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.

公开(公告)号：US20210392185A1

公开(公告)日：2021-12-16

申请号：US17352065

申请日：2021-06-18

Applicant: Amazon Technologies, Inc.

Inventor： Jonathan Daly Einkauf ,  Luca Natali ,  Bhargava Ram Kalathuru ,  Saurabh Dileep Baji ,  Abhishek Rajnikant Sinha

IPC: H04L29/08 ,  H04L12/26 ,  H04L12/24 ,  G06F9/50

Abstract: A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.

公开(公告)号：US09848041B2

公开(公告)日：2017-12-19

申请号：US14702080

申请日：2015-05-01

Applicant: Amazon Technologies, Inc.

Inventor： Jonathan Daly Einkauf ,  Luca Natali ,  Bhargava Ram Kalathuru ,  Saurabh Dileep Baji ,  Abhishek Rajnikant Sinha

IPC: G06F15/173 ,  H04L29/08 ,  H04L12/26 ,  H04L12/24 ,  G06F9/50

CPC classification number: H04L67/1076 ,  G06F9/5077 ,  G06F9/5083 ,  H04L41/0893 ,  H04L43/0876 ,  H04L67/10 ,  H04L67/1031

Abstract: A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.