Amazon Technologies, Inc.

Daniel Lee Osiecki

Reto Kramer

Venkates Paramasivam Balakrishnan

Christopher Whitaker

David R Azari

Nishanth Shankaran

Monty Vanderbilt

Prashant L Sarma

Caitlyn R Schmidt

Geoffrey Scott Pare

Avinash Jaisinghani

Blake Meike

Prashant Jayaraman

Jaykumar Harish Gosar

Adam M Thomas

Jeffrey Lloyd Baker

Danny Rabbani

Pieter Joris De Temmerman

Ronald Kim Peterson

Venkates P Balakrishnan

A resource provisioning service allows users to provision multiple, different network resources in an atomic manner and with a single call to a resource provisioning service. In some instances, the multiple, different network resources comprise individual types of resources that form a portion of one or more cloud-computing platforms. For instance, one or more entities may host and operate a cloud-computing platform that includes different types of network resources, such a storage service, a load balancing service, a compute service, a security service, or any other similar or different type of network-accessible service.

Provisioning multiple network resources

Techniques are described that enable a user to upgrade a stack of resources by providing a template that reflects the desired end state of the stack. Upon receiving a new template, the system automatically detects changes that should be performed and determines the order in which the changes should be performed. The system can also detect if the desired changes to the stack are a result of direct modifications; because parameters to the resources have changed; or the changes are indirectly caused by other dependency/attribute changing. Additionally, the system determines whether the changes require creating new resources or if the changes can be made to the resources live. In case of replacement of resources, the system will first create the new resource, move that new resource into the stack and remove the old resource(s). In case of failures, the system ensures that the stack rolls back to the initial state.

Using a template to update a stack of resources

Disclosed are various embodiments for processing and storing mass data, where the data may include metrics generated based on performance of an event in a monitored system. Metrics describing a state of a monitored system may be received, accessed, and aggregated to generate a data model that describes performance of the monitored system. The metrics utilized in generating the data model may be disregarded after the data model has been generated. An output describing the state of the monitored system may be generated based on the data model, and the output may be communicated over a network, for example, to a requesting service.

Storage of mass data for monitoring

Various systems, methods, and programs embodied on a computer readable medium that facilitate monitoring of services and/or servers. In one embodiment, an amount of data is stored in at least one storage device, the data being generated by a plurality of services executed on a plurality of servers, and/or by the servers upon which the services are executed. A plurality of monitoring applications are executed in a monitoring server, the monitoring applications being configured to perform a plurality of monitoring functions with respect to at least a portion of the data to provide information associated with an operating condition of the services and/or the servers. An interface layer surrounds the monitoring applications in the monitoring server. The interface layer defines a messaging format that is used by external devices to interact with the monitoring applications.

Monitoring of services

Disclosed are various in various embodiments are systems and methods providing for storage of mass data such as metrics. A plurality of data models are generated in the server from a stream of metrics describing a state of a system. Each of the metrics is associated with one of a plurality of consecutive periods of time, and each data model represents the metrics associated with a corresponding one of the consecutive periods of time. The data models are stored in a data store and each of the metrics is discarded after use in generating at least one of the data models.

Various systems, methods, and programs embodied on a computer readable medium that facilitate monitoring of services and servers. In one embodiment, an amount of data is stored in at least one storage device, the data being generated by a plurality of services executed on a plurality of servers, and by the servers upon which the services are executed. A plurality of monitoring applications are executed in a monitoring server, the monitoring applications being configured to perform a plurality of monitoring functions with respect to at least a portion of the data to facilitate an assessment of an operating condition of the services and the servers. An interface layer surrounds the monitoring applications in the monitoring server. The interface layer defines a messaging format that is used by devices external to the interface layer to interact with the monitoring applications.

Disclosed in various embodiments are systems and methods providing for storage of mass data such as metrics. A plurality of data models are generated in the server from a stream of metrics describing a state of a system. Each of the metrics is associated with one of a plurality of consecutive periods of time, and each data model represents the metrics associated with a corresponding one of the consecutive periods of time. The data models are stored in a data store and each of the metrics is discarded after use in generating at least one of the data models.

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Seattle, WA, United States of America

Daniel Lee Osiecki