Amazon Technologies, Inc.

Other

Letian Feng

Leslie Johann Lamprecht

Malcolm Featonby

Roberto Pentz De Faria

John Merrill Phillips

Umesh Chandani

Ladan Mahabadi

Hou Liu

Mohit Gupta

Siyu Wang

Qijia Chen

Nathaniel Baird Jones

Shaokang Ni

Zachary Daniel Casper

Jacob Adam Gabrielson

Kai Fan Tang

Bo Pang

Chia-Yu Kao

Systems and methods for implementing a right-sizing service that recommends and automatically implements resource sizing recommendations for compute clusters executing applications is disclosed. Performance metrics for compute nodes executing the application may be gathered and normalized, with anomalous nodes excluded. Then, respective time series data for the compute nodes may be temporally aligned to identify phases of execution of the application and resource sizes may be estimated for the various phases. A resource sizing recommendation may then be made based in part on this estimated resource sizes. The sizing recommendation may further integrate with other sizing recommendations and be used to manually or automatically size a compute cluster or selected among a number of pre-sized compute clusters, in various embodiments.

Resource right-sizing for compute clusters

Systems and methods for implementing a right-sizing service that recommends and automatically implements sizing recommendations for nodes and resources of compute clusters executing applications is disclosed. Performance metrics for compute nodes executing the application may be gathered and normalized with anomalous nodes excluded. Then, respective time series data for the compute nodes may be temporally aligned to identify phases of execution of the application and resource sizes may be estimated for the various phases. A sizing recommendation may then be made based in part on this estimated resource sizes. The sizing recommendation may further integrate with other sizing recommendations and be used to manually or automatically size a compute cluster or selected among a number of pre-sized compute clusters, in various embodiments.

Cluster right-sizing for cloud-based applications

Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.

Compute platform recommendations for new workloads in a distributed computing environment

Compute platform optimization over the life of a workload in a distributed computing environment

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

Features are disclosed for correlating a workload type with particular volume characteristics for a block storage volume. The volume characteristics may include a durability or a performance consistency for a particular volume. A computing device can obtain a set of workload parameters indicating a workload associated with a particular block storage volume. Based on the set of workload parameters, the computing device can determine a workload classification that links the set of workload parameters to a set of volume characteristics. The computing device can further compare the set of volume characteristics with the current set of volume characteristics for the block storage volume. Based on comparing the sets of volume characteristics, the computing device may determine a recommendation for a user. The computing device can dynamically modify the block storage volume based on the recommendation.

Dynamically modifying block-storage volumes using workload classifications

Features are disclosed for forecasting a usage of a block storage volume with a first configuration by a user. A computing device can forecast the usage of the block storage volume based on the historical usage of the block storage volume by the user. The computing device can determine additional potential configurations of the block storage volume. The computing device can further simulate the additional potential configurations of the block storage volume based on the forecasted usage of the block storage volume. The additional potential configurations may include a volume type, a volume size, or other volume characteristics. Based on the simulations of the additional potential configurations, the computing device may determine a recommended configuration. The computing device can dynamically modify the block storage volume based on the recommended configuration of the block storage volume.

Dynamically modifying block-storage volumes using forecasted metrics

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

Determining configuration parameters to provide recommendations for optimizing workloads

Techniques for an optimization service of a service provider network to generate an architecture diagram that represents an architecture of a web-based application. The optimization service may use the architecture diagram to determine modifications or changes to make to the application. For example, the optimization service may compare the architecture diagram with optimized architecture diagrams that represent application best practices, and determine the modifications or change to make to the application to optimize the application and bring the application in-line with best practices. Further, the optimization service may use the architecture diagram to generate a visualization, and provide the user account with the visualization of the architecture diagram to show users their application architecture.

Application architecture optimization and visualization

Techniques for an optimization service of a service provider network to provide users with machine-generated narratives that include human-intelligible, credible, and transparent recommendations and rationales for recommended VM instance types. The optimization service may gather various information or data about the workload, such as utilization characteristics of the underlying computing resources, and decompose the workloads through a number of dimensions that can be used to describe the workload. Further, the optimization service may analyze the utilization characteristics and/or other data to determine more optimized VM instance types for the workloads that are to be recommended to the users, and also rationales that describes why each recommendation is an appropriate fit for the workload being assessed. Using this information, the optimization service may generate narratives that include a description of the workload behaviors and utilization patterns, a set of recommendations, and supporting narrative or rationales for each of the recommendations.

Growing community of inventors

Seattle, WA, United States of America

Letian Feng