Amazon Technologies, Inc.

Brian Dennehy

Gary Taylor

Sebastien Alexandre Roland Rodriguez

Julio Delgado Mangas

Derek Solomon Pai

Mai-Lan Tomsen Bukovec

Richard Nel-Boland

Pierre Saurel

David R Azari

Monty Vanderbilt

Prashant L Sarma

Disclosed are various embodiments for an instance monitoring service. Instances are associated with alarm conditions indicating a deviation in the operational health of the instance. Upon an alarm condition being satisfied, a remedy operation may be applied to restore the operational health of the instance. A notification system may let customers know of satisfied alarms, and confirm or cancel remedy operations.

Managing operation of instances

A computing resource monitoring service receives a request to store a measurement for a metric associated with a computing resource. The request includes the measurement itself and metadata for the measurement, which specifies attributes of the measurement. Based at least in part on the metadata, the computing resource monitoring service generates a fully-qualified metric identifier and, using the identifier, selects a logical partition for placement of the measurement. From the logical partition, the computing resource monitoring service transmits the measurement to an aggregator sub-system comprising one or more in-memory datastores. The computing resource monitoring service stores the measurement in an in-memory datastore within the aggregator sub-system.

Datastore for aggregated measurements for metrics

A computing resource monitoring service receives a request to retrieve measurement data for a metric. The computing resource monitoring service determines, based at least in part on information included in the request, one or more in-memory datastores where the measurement data is stored. If the information indicates that the computing resource monitoring service is to provide an authoritative subset of the measurement data, the computing resource monitoring service limits the data provided in response to the request to the authoritative subset of the measurement data. Otherwise, if the information does not specify this indication, the computing resource monitoring service will provide the measurement data that is available.

Retrieval of authoritative measurement data from in-memory datastores

A computing resource monitoring service receives a request to store metrics data. The request includes the metrics data and a fully qualified metric identifier generated based at least in part on metadata included in an earlier request to store other metrics data for the same metric. The service uses the fully qualified metric identifier to identify an aggregator datastore for storing the metrics data. Once the service has identified the aggregator datastore, the service accesses the aggregator datastore to store the metrics data such that the metrics data and the other metrics data can be obtained in response to a request to obtain data for the metric, where the request includes the metadata.

Aggregation of metrics data with fine granularity

A web server computer system receives a plurality of measurements for a metric from one or more computing resources associated with the web server computer system. Each measurement includes metadata for the measurement, which specifies attributes of the measurement. The web server computer system determines, for each measurement and based at least in part on the metadata, a fully qualified metric identifier for the measurement. The web server computer system uses the fully qualified metric identifier to partition the plurality of measurements into various partitions. Once completed, the web server computer system transmits a request to one or more aggregator sub-systems of a computing resource monitoring service to store the plurality of measurements.

Architecture for metrics aggregation without service partitioning

A computing resource monitoring service receives a plurality of measurements for a metric associated with an auto-scale group. Each measurement is associated with metadata for the measurement, which specifies attributes for the measurement. The computing resource monitoring service determines, for each measurement and based at least in part on the metadata, a fully qualified metric identifier for the measurement. The service partitions the plurality of measurements into a plurality of logical partitions associated with one or more in-memory datastores. The service transmits the measurements from the plurality of logical partitions to the one or more datastores for storage of the measurements. These measurements are provided to one or more computing resource managers for the auto-scale group to enable automatic scaling of computing resources of the group based at least in part on the measurements.

Automatic scaling of computing resources using aggregated metrics

A computing resource monitoring service receives a request to store a measurement for a metric associated with a computing resource. The request includes the measurement itself and metadata for the measurement, which specifies attributes of the measurement. Based at least in part on the metadata, the computing resource monitoring service generates a fully qualified metric identifier and, using the identifier, selects a logical partition for placement of the measurement. From the logical partition, the computing resource monitoring service transmits the measurement to an aggregator sub-system comprising one or more in-memory datastores. The computing resource monitoring service stores the measurement in an in-memory datastore within the aggregator sub-system.

Systems and methods for efficient data aggregation with sparse exponential histograms may support a performance monitor monitoring and recording of one or more performance metrics for one of one or more dimensions. In response to receiving performance data, a performance data aggregator may parse the performance data for one of one or more performance metrics and one or more dimensions. A sparse exponential histogram generator may create a sparse exponential histogram with the parsed performance data. The sparse exponential histogram generator may transmit or store the sparse exponential histogram representation for subsequent analysis.

System and method for efficient data aggregation with sparse exponential histogram

Attacks from automated scripts or processes, such as Web bots, can be dynamically blocked by monitoring dimensions of requests or submissions received by a system. Each host receiving requests can log information about the requests over a specified period of time. For each period of time, specified dimensions of the requests for that host can be analyzed to determine whether the number of requests having a common value for any of those dimensions meets or exceeds a specified threshold. If so, any requests having those specified dimension values can be automatically blocked for the next specified period of time. The requests can be automatically unblocked after that period of time if the requests do not again meet or exceed the threshold, but can be dynamically blocked for subsequent periods of time if the threshold is again met or exceeded.

Dynamic blocking of suspicious electronic submissions

A plurality of data models are generated in a server from a stream of metrics describing a state of at least one system. Each of the data models represents a time grouping of a subset of the metrics. One or more dimensions are associated with each of the metrics. The data models are stored in association with respective ones of the dimensions in a memory. The dimensions with which the data models are associated in the memory are increased based upon an appearance of at least one previously non-existing dimension associated with a metric in the stream.

Growing community of inventors

Dublin, Ireland

Brian Dennehy