Amazon Technologies, Inc.

Derek Solomon Pai

Eric Samuel Stone

Joshua Marc Burgin

Ahmed Usman Khalid

Shawn Jones

Michael R Foster

Mai-Lan Tomsen Bukovec

Anupama Sharma

John David Dunagan

John Walter Heitmann

Brian Dennehy

Nikita Pinski

Mihir Rajendrabhai Patel

Joby Sky Lafky

Richard Nel-Boland

Dmitry Pushkarev

Caitlyn Rose Shim

David John Ward, Jr

Kai Fan Tang

Christopher Brian Barclay

Gautam Shanbhag

Alexander Kulikov

Thomas Hans Deml

Alison Qing-Ning Truong

Jacob Adam Gabrielson

Matthew Scott Garman

Christoph Saalfeld

Michael Patrick Meaney

Stephen Alden Elliott

Asem Rustum

Isaac Ben Jeppsen

Kyriaki Levanti

Lawrence Wheeless

Aniruddha Shrlram Daptardar

A 3D modeling service of a provider network may receive, from a client facility, different video streams from different cameras at the facility (e.g., security cameras). The 3D modeling service may generate a collection of image data based on the different video streams. The 3D modeling service may construct a 3D model of the facility based at least on the collection of image data and other data from one or more other sources (e.g., CAD files other floor plans). The 3D model may be analyzed to determine various aspects, such as a location to place equipment or coverage gaps of the security cameras. Facility management may perform various actions based on updated 3D models. For example, equipment may be installed at a location or cameras may be installed to eliminate coverage gaps.

Constructing a 3D model of a facility based on video streams from cameras at the facility

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 service provider may provide computing instances organized into logical groups, such as auto-scaling groups. Computing instances assigned to an auto-scaling group may be place into standby. Standby instances may still be managed by the auto-scaling group but may not contribute to the capacity of the auto-scaling group for auto-scaling purposes.

Standby instances for auto-scaling groups

Systems and methods permit customers of a service provider network to specify various constraints on a desired fleet of virtual machine instances without having to specify the hardware types of instances to be included in the fleet. Instead, the customer can specify per-instance hardware constraints (number of CPUs, amount of memory, etc.) and job constraints (e.g., deadline, budget, application type, etc.). A provisioning service accesses an internal database containing instance cost data, instance availability data, and mappings between application type and fleet configurations to propose a fleet of instances that complies with the customer-specified per-instance hardware and job constraints, thereby freeing the customer from having to be conversant in the particular instances offered by the service provider.

Computing resource provisioning

A computing resource service provider provides computing resources to customers, for example, computer system instances supported by server computer systems. These computer system instances may include logical volumes to support execution of the computer system instance. In addition, these instances may be terminated from time to time for various reasons. A stop signal transmitted to computing resource supporting a particular computer system instance may indicate that the particular computer system instance is to be terminated. In response to termination, the particular computer system instance may operate in accordance to a particular behavior.

User and system initiated instance hibernation

A computing resource associated with a user is scheduled to undergo a transition. Prior to the scheduled transition, the computing resource is placed in a pending state. The user is notified that the computing resource is scheduled to undergo the transition. In response to an input received from the user, the computing resource is allowed to undergo the transition when the input indicates that the transition can proceed.

Computing resource transition notification and pending state

A virtual computer system service receives a request from a customer to instantiate a virtual machine instance onto a computing device. The virtual computer system service obtains a set of preferences from the request that can be used for selecting the computing device from a variety of data zones. The virtual computer system service identifies one or more data zones where virtual machine instances of the customer are operating. Based on the set of preferences and the one or more data zones where the virtual machine instances are operating, the virtual computer system service selects a data zone where the virtual machine instance can be instantiated. The virtual computer system service uses a computing device in the selected data zone to instantiate the virtual machine instance.

Automatic placement of virtual machine instances

A computing resource service provider may provide computing instances organized in to logical groups, such as auto-scaling groups. Computing instances assigned to an auto-scaling group may be detached from auto-scaling group. Detached instances may no longer be managed by the auto-scaling group and may not contribute to the capacity of the auto-scaling group for auto-scaling purposes.

Detaching instances from auto-scaling group

A resource manager may monitor resources offered by a resource provider. The resource manager receives computing metrics from the resources and may perform a management action when a customer defined usage limit is satisfied. The management action may include the resource manager generating an alert, or the resource manager preventing the customer from further accessing the resources offered by the resource provider. The resource manager may also be used to reduce the cost of running a customer's application that uses resources offered by the service provider. The resource manager may also receive business metrics from the customer and determine rates of return using the business metrics and the computing metrics.

Resource manager

An instance service of a computing resource service provider may provide computing system instances to customers of the computing resource service provider. The computing system instances may be used by the customer to execute various customer workloads. Furthermore, the computing system instances may include an instance type indicating an amount of computing resources allocated to computing system instance of the instance type. The instance service may obtain requirement and/or constraints associated with the workload and determine a configuration of instance types to include in a set of instances configured to execute the customer workload.

Multiple instance types serving a single workload or application

Technology for scaling computing instances is provided. In one example, a method may include identifying vertical scaling rules defining a criterion for vertically scaling a computing instance from a first instance type to a second instance type. The method may further include monitoring usage of the computing instance and vertically scaling the computing instance from the first instance type to the second instance type based on the usage as compared to the vertical scaling rules.

Vertical scaling of computing instances

Systems and methods permit customers to purchase resource instances on a spot purchasing mode basis, which permits a customer to specify a maximum price that the customer is willing to pay for one or more resource instances (e.g., virtual machine instances running on physical servers). Further, the resource instances purchased under the spot purchasing mode can be guaranteed to run for at least a specified period of time, even if the current variable spot cost value is exceeds the original bid price. Such instances run for defined blocks of time which may be selectable by the customer, thereby providing the customer some level of assurance that the instances will not terminate prematurely due to spot cost value fluctuations. Instances are assigned into a spot block instance pool from other types of pool based on the estimated excess capacity of such other types of pools. The estimate of excess capacity is based on statistical analysis of the usage of the instances.

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Seattle, WA, United States of America

Derek Solomon Pai