Amazon Technologies, Inc.

Jui Te Tseng

Forrest MacKenzie Vines

Nathan David Scott

Sevag Demirjian

Andrew Rinaldi

Xingbang Tian

David Y Yamanoha

Amey Shreekant Jahagirdar

Gianpierre Villagomez Saldana

Avinash Nagasundaram

Platon Aleksandrovich Chubotin

Lars Logen Jangaard

Tristan Daniel Fries E Bahia Luz-Wilson

Ruiqi Cheng

Ellison Kyle Sudario Bayan

Mohan Das Katragadda

To determine whether a request or an associated response is cached, the request may be received by a first server associated with a Content Delivery Network (CDN). Based on configuration data for the server, a header is generated based on characteristics of the request, indicating that a response associated with the request is able to be cached. A second server associated with generation of a response receives the request. In response to the header, the second server generates a caching determination based on configuration data and the characteristics of the request. When the first server receives the response and the caching determination, the response may be stored. Use of the second server to generate caching determinations eliminates the need to change the configuration of devices associated with the CDN while enabling requests from multiple CDN devices to be cached without requiring changes to be deployed to each device.

System for controlling storage of response data

Logs representative of interactions by users or services with interfaces, such as clickstream logs, are generated by combining logs associated with cached data with logs associated with data generated in response to a request. When a first response to a request is generated and a portion of the response data is cached, a first log representative of interactions with the cached data is stored in association with a log identifier. When a second request is received at a subsequent time and the cached data is used to generate a second response, a second log representative of interactions with the response data will include the log identifier for the first log. In response to the log identifier, the first log is accessed and combined with the second log to form a third log representative of interactions with both the cached data and response data for the second response.

System for determining cached log data associated with cached response data

Methods, systems, and computer-readable media for lifecycle management of multiple storage clusters are disclosed. A first set of data is loaded into a first storage cluster. The first storage cluster is selected for loading the first set of data based at least in part on the first set of data and the first storage cluster being associated with a first period of time. The first storage cluster is placed into a read-only state. A second set of data is loaded into a second storage cluster. The second storage cluster is selected for loading the second set of data based at least in part on the second set of data and the second storage cluster being associated with a second period of time.

Lifecycle management of storage clusters

Systems and methods are disclosed herein for generating a signature of an anomalous network event capable of adversely affecting the performance of a computing resource service provider. A signature computing workflow service receives network traffic information received at the computing resources service provider, and parses the network traffic information into a set of entries. The set of entries may include data for a set of parameters useable for communicating over a network. Entropy values may be calculated for the data and anomalies for the set of parameters may be detected based on changes in entropy for the set of parameters. A signature of an anomalous network traffic event may be generated based on characteristic entropy changes in the parameters associated with the anomalies. The signature may be useable to detect the presence of the network event.

Network management using entropy-based signatures

Web Bot detection methods and systems are provided that receive a request, in connection with a network session. The methods and systems determine whether the request is associated with potential Bot activity, and based thereon assign a Bot confidence designation. The Bot confidence designation indicates a likelihood that the request represents an agent-based request. The methods and systems analyze a session trait of the network session relative to predetermined session traits indicative of human-based requests, and assign a human confidence designation based on the analysis. The human confidence designation indicates a likelihood that the request represents a human-based request. The request is then classified to represent an agent-based request or human-based request based on the Bot and human confidence designations.

Web Bot detection and human differentiation

Embodiments of methods, systems and storage media associated with mitigating the effects of client-side (customer) bot-like activity caused by code injected into an e-commerce web page in the customer's browser after it is received from the e-commerce server system. Rather than entirely block a selected account or device (IP address) of a legitimate customer, a more politic process to mitigate the effects of bot-like scripts is implemented by embedding a 'protective script' into a web page before it is downloaded to a client device. The protective script intercepts attempts to inject new code into the page, and it may prevent the injection entirely, or modify the injected code to mitigate its impact, for example, by slowing down its operation. This process leaves the customer free to continue normal activities on the site without any adverse impact or penalty.

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Seattle, WA, United States of America

Jui Te Tseng