Amazon Technologies, Inc.

Alexander S Borst

Muhammad Ali Siddiqui

Kendra Anne Yourtee

Thomas Lowell Keller

Sanjeev Kewal Verma

Gregory Suhyun Kang

Josh M Goodspeed

Miles Childs Kaufmann

James Connelly Petts

Vivek A Shah

Amos Dylan Vance

Olga I Kuznetsova

Roy N Harkness

Michael R Brauwerman

Theodore H Romer

Examples of systems and methods are described for managing computing capacity by a provider of computing resources. The computing resources may include program execution capabilities, data storage or management capabilities, network bandwidth, etc. Multiple user programs can consume a single computing resource, and a single user program can consume multiple computing resources. Changes in usage and other environmental factors can require scaling of the computing resources to reduce or prevent a negative impact on performance. In some implementations, a fuzzy logic engine can be used to determine the appropriate adjustments to make to the computing resources associated with a program in order to keep a system metric within a desired operating range.

Reactive auto-scaling of capacity

Technologies are described herein for incorporating streaming and/or sampling in real-time log analysis. Representative samples of log data are extracted from the log files on a number of monitored hosts and streamed in real-time to log processors for processing. The log processors accumulate and process the representative samples of log data, and track a data completeness value representing an indication of a proportion of total log data represented by the representative samples received. The representative samples of log data are merged and collated. Estimated metrics are calculated from the merged and collated representative samples and the data completeness, and the estimated metrics are published to consumers in near real-time.

Streaming and sampling in real-time log analysis

Disclosed are various embodiments for generating automobile parts bundles associated with maintenance events. A maintenance schedule is retrieved and various maintenance events are identified. Various maintenance tasks associated with maintenance events are also identified. Parts associated with these maintenance tasks are placed into parts bundles associated with the maintenance event and a unique identifier within a parts catalog.

Generating parts bundles

Growing community of inventors

Seattle, WA, United States of America

Alexander S Borst