Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

Bradley Joseph Gussin

Diwakar Gupta

Michael Phillip Quinn

Chen Yang

Pratik Pradeep Ramdharne

John David Dunagan

Eric Paul Wei

Nishant Kanaiyalal Mehta

David Charles Oliver

Keith A Carlson

Srinivasan Jagannathan

Shantanu Chandra

James Stanford Gilmore

Wynand Jan Theunissen

Trent Green

Jarrod Scott Cameron

Justin J Tittelfitz

Dustin Alan Lee

Venkatesh Ashok Phadnavis

Saurabh Jain

Yahui Sun

Azbayar Demberel

Qiang Du

The system described herein provides the ability to efficiently back up and restore files within a service, such as a network-based file hosting service or a collaboration service. The system is configured to improve file restoration (may also be referred to as recovery) that occurs in response to an event (may also be referred to as an incident) that compromises a tenant device on which the files reside, thereby preventing access to the files or corrupting the files that are available to the tenant. An example of an event includes a ransomware attack. The described system utilizes a set of defined resources within a boundary to back up tenant files and restore the files. The resources include a metadata database and a blob store. The system combines mutable metadata with immutable content objects within the service to provide faster restoration than the conventional approach.

Optimizing the backup and restoration of files within a service

A host computer inventory system within a provider network detects patterns of launch requests on an individual user account basis. For a customer that cyclically submits similar launch requests, the inventory system may allocate slots in specific host computers consistent with the detected launch pattern so that future attempts to launch the virtual machines will be honored using the pre-allocated hosts.

Predictive virtual machine launch-based capacity management

Techniques for managing resource utilization across heterogeneous physical hosts are described. Resource utilization of a first plurality of physical hosts in a provider network may be monitored, each physical host comprising a plurality of resources. A future resource utilization can be determined, the future resource utilization including quantities of a plurality of resource types. The future resource utilization can be matched to a plurality of physical host types, each physical host type associated with a different plurality of resources. A second plurality of physical hosts corresponding to the plurality of physical host types can be deployed to the provider network.

Placement to optimize heterogeneous physical host utilization

Techniques for server movement control are described. A capacity library service (CLS) can manage which hosts in a provider network can be taken in and out of production. The CLS may also control which entities may remove hosts from production and under what conditions the hosts may be removed from production. In some embodiments, the CLS can execute various workflows to manage checkout and check-in of hosts. Workflows may also be used to manage hosts while they are out of production to manage state transitions (e.g., in production, in testing, in reserve, etc.) based on current host fleet capacity and checkout rules.

Server movement control system based on monitored status and checkout rules

Techniques for on demand capacity management in a provider network are described. The provider network includes electronic devices that provide computing-related resources to customers. The unused capacity of these electronic devices&#x2014;such as processor cores, memory, network bandwidth, etc.&#x2014;can be used to satisfy a variety of computing needs. Services of the provider network allocate portions of the unused capacity based on customer requests for computing-related resources.

On demand capacity management in provider networks using type-agnostic resources

Systems for processing requests to use virtual computing resources in communicatively isolated computing environments apply a multi-tier framework for limiting the amount of virtual computing resources that can be allocated to a user in a computing environment. A bottom tier is associated with a user account that has not been analyzed for security risks, a top tier is associated with a user account that has been validated to use a corresponding computing environment; an intermediate tier, having limits between the bottom and top tiers, is applied to computing environments where the user has not requested resources. Initially, all computing environments are on the bottom tier. In response to a valid request for resources, validated computing environments are moved to the top tier and all other computing environments are moved to the intermediate tier. Requests in an intermediate-tier computing environment trigger validity review; the computing environment can move to top tier.

Tiered usage limits across compute resource partitions

A method for removing computing assets from service in a provider network includes receiving, from a requestor, identification for selected ones of the computing assets requested to be removed from service. Each of the computing assets is a computing device or a slot of a computing device. A pool in which each of the computing assets requested to be removed from service is located is identified. For each identified pool: which of the computing assets requested to be removed from service is approved for removal from service is determined based on a number of resource instances available for allocation to users after the computing devices are removed from service exceeding a predetermined number of resource instances. The requestor is notified as to which of the computing assets is approved for removal from service.

System and method for host isolation in a web-based computing system

A system includes one or more computer systems that include one or more processors and memory. The memory includes instructions, that, upon being executed by the one or more processors, cause the system to generate forecast demand data for a first pool of servers configured to host virtual machine instances, generate forecast capacity data that forecasts a number of slots available to run virtual machine instances for the first pool of servers, and generate a first pool health index value based on a comparison of the forecast demand data with the forecast capacity data.

Capacity pool health index

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Seattle, WA, United States of America

Bradley Joseph Gussin