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    Processing of long running processes
    6.
    发明授权
    Processing of long running processes 有权

    公开(公告)号：US09703594B1

    公开(公告)日：2017-07-11

    申请号：US14635254

    申请日：2015-03-02

    申请人： Amazon Technologies, Inc.

    发明人： Ankit Kamboj ,  Xing Wu ,  George Steven McPherson ,  Jian Fang ,  Dag Stockstad ,  Abhishek Rajnikant Sinha

    IPC分类号： G06F9/46 ,  G06F9/48

    CPC分类号： G06F9/4806 ,  G06F9/485 ,  G06F9/4881 ,  G06F9/4887 ,  G06F11/30

    摘要： A system adapted to process long-running processes is disclosed. A request to upload data is received at a server. The server divides the data into multiple parts and launches a separate process to upload each of the divided parts. The server records for each process the processing time or duration that the particular process used to upload its corresponding data item. The server maintains an average processing duration that is calculated from the processing durations of the completed processes. The server identifies that one process is continuing to run and compares a processing duration for the particular process to a threshold derived from the average processing duration. If the processing duration for the particular process exceeds the threshold, the server initiates a new process to upload the same data item. When one of either the new process or the still running process has completed processing, the server terminates the other process.

    Automatic scaling of resource instance groups within compute clusters
    9.
    发明授权
    Automatic scaling of resource instance groups within compute clusters 有权

    公开(公告)号：US11044310B2

    公开(公告)日：2021-06-22

    申请号：US16805412

    申请日：2020-02-28

    申请人： Amazon Technologies, Inc.

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

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

    摘要： 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.