Amazon Technologies, Inc.

Other

John Merrill Phillips

Malcolm Featonby

Umesh Chandani

Leslie Johann Lamprecht

Roberto Pentz De Faria

Mihir Sadruddin Surani

Yuxuan Liu

Letian Feng

Ladan Mahabadi

Hou Liu

Douglas Cotton Kurtz

Adithya Bhat

Nicholas Patrick Wilt

William John Earl

Deepak Singh

John David Dunagan

Mohit Gupta

Ekanth Sethuramalingam

Suk Won Kim

Les Johann Lamprecht

Matthew Shawn Wilson

Jacob Adam Gabrielson

Nachiketh Rao Potlapally

Christopher Richard Jacques De Kadt

Scott Michael Le Grand

David Samuel Zipkin

Kai Fan Tang

Michael Groenewald

Ethan John Faust

Paolo Maggi

Jacobus Johannes Van Der Merwe

Erik Quanstrom

James Rigby

Wenbo Bu

Andrea Curtoni

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

A network-based virtual computing resource provider may offer virtual compute instances that implement rolling resource credits for scheduling virtual computing resources. Work requests for a virtual compute instance may be received at a virtualization manager. A resource credit balance may be determined for the virtual compute instance. The resource credit balance may accumulate resource credits in rolling fashion, carrying over unused credits from previous time periods. Resource credits may then be applied when generating scheduling instructions to provide to a physical resource to perform the work requests, such as a physical CPU in order to increase the utilization of the resource according to the number of credits applied. Applied resource credits may then be deducted from the credit balance.

Rolling resource credits for scheduling of virtual computer resources

A database service that receives and manages time series data into a set of generational data tables. The database service generates a set of generational data tables configured to receive a subset of time series data. Each generational data table is created with an initial set of access parameters that facilitates the receipt of incoming time series data. Thereafter, the database service creates additional generational data tables to receive the continuous received time series data and then modifies the access parameters of the previously created data table, such as for read only rights.

Generational databases for managing time series data

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.

Generating narratives for optimized compute platforms

Compute platform optimization across heterogeneous hardware in a distributed computing environment

Optimizing hardware platform utilization for heterogeneous workloads in a distributed computing environment

Methods, systems, and computer-readable media for interaction monitoring for virtualized graphics processing are disclosed. Execution of an application is initiated on a virtual compute instance that is implemented using CPU and memory resources of a server. Instruction calls are produced by the execution of the application and sent from the server to a graphics server over a network. The graphics server comprises a physical GPU, and a virtual GPU is implemented using the physical GPU and attached to the virtual compute instance. GPU output is generated at the graphics server based at least in part on execution of the instruction calls using the virtual GPU. A log of interactions between the application and the virtual GPU is stored. The interactions comprise the instruction calls sent to the graphics server and responses to the instruction calls sent to the virtual compute instance.

Interaction monitoring for virtualized graphics processing

A control-plane component of a network-accessible service receives an indication that a resource request has been submitted by a client. The component determines, based at least in part on metadata associated with the client, that a first parameter selection policy (PSP) of one or more PSPs created for the service is applicable. The first PSP includes a condition set to determine applicability, and a value set indicating parameter values to be used to fulfill resource requests to which the PSP applies. Using the value set, an effective parameter value list for one or more operations to be performed to allocate the requested resource is generated, and the allocation operations are initiated.

Policy-based allocation of provider network resources

Techniques for managing dynamically scalable virtualized compute instances within a provider network are described. A dynamically scalable instance has a baseline performance level and a maximum performance level associated with a computing resource, such as a processor, a memory, a network interface, etc. In response to receive a request to launch a dynamically scalable instance, one or more services select a computer system to host the requested instance from a pool of instance-hosting computer systems of a provider network. The instance-hosting computer systems include a monitoring agent that reports computing resource usage data to the one or more services. The one or more services monitor the resource usage of the instance-hosting computer systems and migrate instances so that the dynamically scalable instances can operate up to the maximum level of performance.

Adjusting dynamically scalable instance hosting based on compute resource usage

Techniques for managing compute resource usage by virtualized compute instances within a provider network are described. A computer system connected to a provider network hosts one or more compute instances. An agent associated with the computer system obtains per-compute instance resource usage information of computer system resources such as processors, memory, and network interfaces. The agent sends the usage information to a usage management service of the provider network. The usage management service generates usage limits based on the usage information from the agent and on usage targets and sends the usage limits to the computer system. The computer system limits the resource usage of the compute instance based on the usage limits.

Managing compute resource usage based on prior usage

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Seattle, WA, United States of America

John Merrill Phillips