Amazon Technologies, Inc.

Melissa Elaine Davis

Jeremy Boynes

Alexander Julian Tribble

Christopher Allen Suver

Charles Alexander Carman

Thomas Lowell Keller

Muhammad Ali Siddiqui

Miles Childs Kaufmann

Amos Dylan Vance

Gavin Richard Jewell

Igor Spac

Gregory Richard Swensen

Mukul Vijay Karnik

Andrew Stuart Huntwork

Vishal Parakh

Chelsea Celest Krueger

Antoun Joubran Kanawati

Anton Vladilenovich Goldberg

Claire Elizabeth Suver

Félix Joseph Étienne Pageau

Adam Lloyd Days

Brian Jaffery Tajuddin

Erin Harding Kraemer

Robert Michael Barry

Kal Lyndon McFate

Paul Matthew Buddington

Lukasz Wojciech Cwik

Jon Arron McClintock

Brandon William Porter

Kyle Bradley Peterson

Mark Noel Kelly

Paul Derek DeMarco

Donghui Zhuo

Alex Jennifer Tribble

Brady Montz

Alec Peterson

Aram Grigoryan

Radu Weiss

Matthew Paul Wenger

David Elfi

Renaud Bordelet

At a rule processing unit of an evolving, self-organized machine learning-based resource management service, a rule of a first rule set is applied to a value of a first collected metric, resulting in the initiation of a first corrective action. A set of metadata indicating the metric value and the corrective action is transmitted to a repository, and is used as part of an input data set for a machine learning model trained to generate rule modification recommendations. In response to determining that the corrective actions did not meet a success criterion, an escalation message is transmitted to another rule processing unit.

Artificial intelligence based self-organizing event-action management system for large-scale networks

Disclosed are various embodiments for predicting and classifying events that create a sudden or substantial increase in network traffic activity. To begin, an increase of network activity can be detected. Upon detecting the increase in network activity, it can be determined that the increase in network activity is unexpected in view of one or more predicted activity events. The system can be adjusted to reduce the network activity in response to determining that the increase in network activity is unexpected.

Predicting and classifying network activity events

A computing resource service provider may operate a secure proxy fleet within a content delivery network. The secure proxy fleet may protect sensitive data communicated between a client device and a backend service over one or more networks, for example, over the content delivery network to a computing resource service provider environment. The secure proxy fleet may protect sensitive data by encrypting the sensitive data before it is forwarded to a destination.

Secure data distribution of sensitive data across content delivery networks

Non-blocking processing of federated transactions may be implemented for distributed data partitions. A transaction may be received that specifies keys at data nodes to lock in order to perform the transaction. Lock requests are generated and sent to the data nodes which identify sibling keys to be locked at other data nodes for the transaction. In response to receiving the lock requests, data nodes may send to lock queues indicating other lock requests for the keys at the data node. An evaluation of the lock queues based, at least in part, on an ordering of the lock requests in the lock queues may be performed to identify a particular transaction to commit. Once identified, a request to commit the identified transaction may be sent to the particular data nodes indicated by the sibling keys in a lock request for the identified transaction.

Non-blocking processing of federated transactions for distributed data partitions

A computing device can obtain a session key for encrypting data that is communicated between a client device and the computing device. The computing device can receive, from the client device, an encrypted request for data. The encrypted request can be encrypted by the client device using the session key. The data requested can be stored on a second computing device. The computing device can send, to the second computing device, a copy of the session key and the encrypted request for data. The second computing device can decrypt the data using the session key and can also encrypt data responsive to the request using the session key.

Secured communication in network environments

A system for transmitting data over a network may include data processed according to a data-redundancy encoding technique such as erasure coding to be transmitted via an unreliable, connectionless transmission protocol, for example user datagram protocol ('UDP'). A transmitting manager may receive a request from an application to transmit data. The transmission manager may select encoding parameters and encode data to be transferred using a data-redundancy encoding technique. The transmission manager may initiate transmission of the encoded data and payload header data may via a network, for example via a network communication stream or protocol stack. A reconstruction manager associated with the receiving node may reconstruct the original data using the received erasure coding data and payload header data. In some embodiments, the receiving node may transmit telemetry data to the erasure coding algorithm. The erasure coding algorithm may use received telemetry data to adjust parameters of the erasure coding algorithm for future erasure coding and transmission operations. In some systems, telemetry data may include a request to retransmit all or a portion of the erasure coded data.

Transfer of data-redundancy encoded data via unreliable, connectionless protocol

Methods, systems, and computer-readable media for implementing pattern-based detection are disclosed. A plurality of services monitor a plurality of service interactions comprising data or metadata. The services compare the data or metadata to a set of patterns and identify one or more matched patterns among the set of patterns. The services send data indicative of the matched patterns to a central recording service. The central recording service aggregates the data indicative of the matched patterns and generates one or more data flow visualizations indicating one or more data flows between individual ones of the services for the matched patterns.

Pattern-based detection for services in distributed systems

Described is a dynamic web platform configured to provide content rendered with one or more rendering systems. The rendering systems and the modules making up the rendering modules may interact with one another by way of declarative data. Administrators may configure the platform using the declarative data. The declarative data may express operational parameters, business rules, and so forth and may be modified while the platform is operating. The modules in the rendering system may be loosely bound, allowing for concurrent operations, dynamic changes to what content is to be rendered, and so forth.

Declarative language dynamic web platform

Disclosed are various embodiments for predicting and classifying events that create a sudden and substantial increase in network traffic activity. To begin, a sales event may be predicted based on the results following the search for one or more prediction factors that occur during a predefined period of time. Based either on the individual results of each evaluation or a combination of results of the search of two or more of the prediction factors, a sales event may be predicted. Additionally, upon detection of a sudden and substantial increase in network traffic activity, one or more classification factors may be evaluated to determine whether the cause of the increase is due to a sales event or other type of alternative activity event.

Computer applications may generate event data based on a large volume of different types of record data. Described herein are systems, methods and devices for providing website recommendations using the event data. In one example, using the event data, a computing node generates the website recommendations within a designated amount of time after the generation of the record data.

Systems and methods providing recommendation data

Computer applications may generate a large volume of different types of record data. In one example, the large volume of record data may represent millions of different processes occurring every second. Described herein are systems, methods and devices for generating parsed data based on the large volume of record data. The parsed data may be consumed by computing nodes within a designated amount of time from the generation of the record data.

Systems and methods providing event data

The disclosure describes embodiments of a distributed caching system that are configured to store handshake data between client devices and servers, enabling handshake transaction to be resumed in case of interruption. Client devices can resume the handshake transaction even if assigned to new servers as the new servers can obtain the handshake data identifiers from the distributed caching system.

Distributed caching system

Growing community of inventors

Edmonds, WA, United States of America

Melissa Elaine Davis