Amazon Technologies, Inc.

Qijia Chen

Leslie Johann Lamprecht

Letian Feng

Mohit Gupta

Roberto Pentz De Faria

Siyu Wang

Laurent Callot

Bo Pang

Alexander Zimin

Nam Khanh Tran

Paul M Vazquez

Meredith Paige Kiessling

Joel Dan Castellon Arevalo

Chia-Yu Kao

Techniques for a cloud-based workload optimization service to identify customer workloads that are optimized to run on burstable instance types. The techniques include identifying workloads that are successfully running on burstable instance types, and using historical-utilization data for those workloads to train classification models. The optimization service can extract feature data from the historical-utilization data, where the feature data represents utilization characteristics that are indicative of burstable workloads. The feature data is then used to train classification models to receive utilization data for candidate workloads, and determine whether the candidate workloads would be optimized for burstable instance types. The optimization service can then migrate suitable workloads to burstable instance types, and/or provide users with recommendations that their workloads are optimized or suitable for burstable instance types.

Classifying workloads for burstable compute platforms

Techniques for a service provider network to allow users to quickly and easily establish a testing environment to test various virtual machine (VM) instance types for hosting their workloads. Rather than identifying and recommending optimized VM instance types for hosting workloads of users, the techniques allow for users to initially test the VM instance types and determine how well their workloads perform on the VM instance types. Users can quickly and easily (e.g., 'one-click' input) request that a testing environment be established. The optimization service can then test one or more recommended VM instance types for the users' workloads in the testing environment. The optimization service can monitor the performance of the VM instance types while they host the 'test workloads,' and provide the users with performance metrics to help them decide if they would like to migrate their workloads to the recommended VM instance types.

Testing recommended compute platforms for a workload

Methods, systems, and computer-readable media for automatically detecting root causes of anomalies occurring in information technology (IT) systems are disclosed. In some embodiments, data of a service graph depicting dependencies between nodes or services of the IT infrastructure is traversed to determine propagation patterns of anomaly symptoms/alarms through the IT infrastructure. Also, a causal inference model is used to determine probabilities that an observed propagation pattern corresponds to a stored propagation pattern, wherein a close correspondence indicates that the current anomaly is likely caused by a similar root cause as a past anomaly that caused the stored propagation pattern.

Root cause detection and corrective action diagnosis system

Growing community of inventors

Seattle, WA, United States of America

Qijia Chen