Amazon Technologies, Inc.

Jai Vasanth

Peter Sirota

Richard Jeffrey Cole

Andrew J Hitchcock

Stefano Stefani

Wei Xiao

Luis Felipe Cabrera

Ian Paul Nowland

Richendra Khanna

David Alan Lutz

Timothy Andrew Rath

Kiran-Kumar Muniswamy-Reddy

Chiranjeeb Buragohain

Vaibhav Aggarwal

Jakub Kulesza

Yan Valerie Leshinsky

Maximiliano Maccanti

Barry Bailey Hunter, Jr

Chiranjeeb Buragahain

David Craig Yanacek

Stuart Henry Seelye Marshall

Pejus Manoj Das

Jian Wang

Mohammad Zeeshan Qureshi

Swaminathan Sivasubramanian

Matthew Paul Bell

Rande Anthony Blackman

Medhavi Dhawan

Adam Douglas Morley

Xianglong Huang

James Plumley

Clarence Wing Yin Ng

Anatoly Mitlin

Todd Greenwalt

Sarfraz Banglawala

Olia Kerzhner

Vincent Anthony Brancato

Maximiliano MacCanti

A fleet management system may create and manage tasks within an environment associated with fulfilling, sorting, inducting, and/or distributing packages, such as a warehouse, packaging facility, sortation center, or distribution center. The fleet management system may assign the tasks to agents within the environment. Each of the agents may have capabilities or tasks that the agents are configured to perform and the fleet management system may use these capabilities to assign the tasks to the agents. Additionally, tasks may be assigned based on a location of the agents within the environment. As tasks are generated, the fleet management system may determine a suitable agent to perform the task and may transmit instructions to the agent for carrying out the task. The fleet management system may provide a centralized platform to manage agents, optimize the assignment of tasks, and increase productivity within the environments.

Centralized fleet management system for optimizing task assignment

A system that implements a scaleable data storage service may maintain tables in a non-relational data store on behalf of clients. Each table may include multiple items. Each item may include one or more attributes, each containing a name-value pair. Attribute values may be scalars or sets of numbers or strings. The system may provide an API usable to request that values of one or more of an item's attributes be updated. An update request may be conditional on expected values of one or more item attributes (e.g., the same or different item attributes). In response to a request to update the values of one or more item attributes, the previous values and/or updated values may be optionally returned for the updated item attributes or for all attributes of an item targeted by an update request. Items stored in tables may be indexed using a simple or composite primary key.

System and method for conditionally updating an item with attribute granularity

A system that implements a scalable data storage service may maintain tables in a data store on behalf of storage service clients. The service may maintain table data in multiple replicas of partitions that are stored on respective computing nodes in the system. In response to detecting an anomaly in the system, detecting a change in data volume on a partition or service request traffic directed to a partition, or receiving a service request from a client to split a partition, the data storage service may create additional copies of a partition replica using a physical copy mechanism. The data storage service may issue a split command defined in an API for the data store to divide the original and additional replicas into multiple replica groups, and to configure each replica group to maintain a respective portion of the table data that was stored in the partition before the split.

System and method for performing live partitioning in a data store

Techniques are described for managing distributed execution of programs. In some situations, the techniques include dynamically modifying the distributed program execution in various manners, such as based on monitored status information. The dynamic modifying of the distributed program execution may include adding and/or removing computing nodes from a cluster that is executing the program, modifying the amount of computing resources that are available for the distributed program execution, terminating or temporarily suspending execution of the program (e.g., if an insufficient quantity of computing nodes of the cluster are available to perform execution), etc.

Dynamically modifying a cluster of computing nodes used for distributed execution of a program

Methods and apparatus for configurable-capacity time-series tables are disclosed. A schedule of database table management operations, including at least an operation to change a throughput constraint associated with a table in response to a triggering event, is generated. The table is instantiated with an initial throughput constraint in accordance with the schedule. Work requests directed to the table are accepted based on the initial throughput constraint. The throughput constraint is modified in response to the triggering event. Subsequent work requests are accepted based on the modified throughput constraint.

Configurable-capacity time-series tables

Methods and apparatus for resource silos at network-accessible services are disclosed. A subset of resources used for a database service, including at least one resource from each of a plurality of data centers, is selected for membership in a resource silo based on grouping criteria. A silo routing layer node identifies the resource silo as the target silo to which a client work request is to be directed. The client work request is sent to a front-end resource of the target silo either by the client, or by the silo routing layer node on behalf of the client. The front-end resource of the target silo transmits a representation of the work request to a back-end resource of the target silo, where a work operation corresponding to request is performed.

Resource silos at network-accessible services

Detecting replica faults within a replica group and dynamically scheduling replica healing operations are described. Status metadata for one or more replica groups may be accessed. Based, at least in part, the status data a number of available replicas for at least one replica group may be determined to incompliant with a healthy state definition for the replica group. One or more healing operations to restore the number of available replicas for the at least one replica group to the respective healthy state definition may be dynamically scheduled. In some embodiments, one or more resource constraints for performing healing operations and one or more resource requirements for each of the one or more healing operations may be used to order the one or more healing operations.

Dynamic replica failure detection and healing

Techniques are described for managing distributed execution of programs. In some situations, the techniques include determining configuration information to be used for executing a particular program in a distributed manner on multiple computing nodes and/or include providing information and associated controls to a user regarding ongoing distributed execution of one or more programs to enable the user to modify the ongoing distributed execution in various manners. Determined configuration information may include, for example, configuration parameters such as a quantity of computing nodes and/or other measures of computing resources to be used for the executing, and may be determined in various manners, including by interactively gathering values for at least some types of configuration information from an associated user (e.g., via a GUI that is displayed to the user) and/or by automatically determining values for at least some types of configuration information (e.g., for use as recommendations to a user).

Managing distributed execution of programs

A system that implements a data storage service may store data in multiple replicated partitions on respective computing nodes on behalf of clients. A storage node may, based on the amount of provisioned resources on a given storage device or logical volume, identify candidate partition management operations to be performed, and may send information about the operations to a central partition management scheduler. The scheduler may apply a global prioritization scheme to determine an order in which to perform the candidate operations. The order may be based on whether the operations include partition splits or partition moves, whether they aim to reduce provisioned storage capacity or reduce throughput capacity on a storage device or logical volume, whether they conflict with each other, whether the total number of partitions (or replicas thereof) involved in partition management at any given time exceeds a pre-determined limit, or whether they were requested by clients.

Scheduling of splits and moves of database partitions

A system that implements distributed storage may schedule and track control plane operations for performance at the distributed storage service. Information may be maintained for control plane events detected at a distributed storage system. Resource utilization for currently performing control plane operations and currently scheduled control plane operations of the distributed storage system may be determined. The information about detected control plane events may be analyzed to schedule control plane operations to be performed in response to detecting the control plane events. As part of scheduling control plane operations, resource constraints may be applied to the determine resource utilization for the distributed storage system.

Scheduling and tracking control plane operations for distributed storage systems

Growing community of inventors

Bellevue, WA, United States of America

Jai Vasanth