Amazon Technologies, Inc.

Yuxuan Liu

Malcolm Featonby

Mihir Sadruddin Surani

Umesh Chandani

Douglas Cotton Kurtz

Adithya Bhat

John Merrill Phillips

Nicholas Patrick Wilt

John David Dunagan

Mohit Gupta

Ethan John Faust

Anton Valter

Alexander Gregory Bestavros

Roberto Pentz De Faria

Les Johann Lamprecht

Leslie Johann Lamprecht

Alexey Gadalin

Paolo Maggi

Andries Dippenaar

Andrea Curtoni

Saurabh Nirmal Modh

Alan Powell

Siyang Mai

Erik Quanstrom

Nicholas Matthew Rudi Goossens

Siwei Liu

James Rigby

Mykhailo Karataiev

Wenbo Bu

Jr John Merrill Phillips

Techniques for managing cloud computing resources hosting burstable performance instances are described. A host computer system of a provider network executes burstable performance compute instances. Compute capacity usage data is obtained from the host computer system, the compute capacity usage data including a first indication of a first compute capacity used by a first burstable performance compute instance. A first weight for the first burstable performance compute instance is calculated, the first weight being inversely related to the first compute capacity. A scheduler of the host computer system is updated with process prioritization weights, the process prioritization weights including a first process prioritization weight that is based at least in part on the first weight. The scheduler allocates compute capacity based on the process prioritization weights.

Managing fairness of resource scheduling for performance bursting in multi-tenant environments

This disclosure describes techniques for increasing the baseline performance of a burstable instance to an increased performance level for a limited time period. For example, a user may schedule a time period which the burstable instance has access to 100% of a CPU, instead of competing with other burstable instances even during periods of bursting as in prior techniques. In some cases, the user uses credits to reserve the time periods at which the increased performance level is requested. After receiving the reservation, a resource system selects computing resources to host the burstable instance such that the burstable instance, can operate at the requested increased performance level. After the time period has ended, the burstable instance may return to the baseline performance level.

Reserving increased performance of burstable instances for a limited time period

A graphic session coordinator is established to enable remote virtualized graphics operations on behalf of a set of graphics request generators. A request generator submits a graphics session request to the session coordinator. A configuration operation is performed at one or more routing devices to enable graphics operation request packets from the request generator to be delivered to a first remote virtualized graphics device and to enable graphics operation response packets to be transmitted to a destination.

Session coordination for auto-scaled virtualized graphics processing

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

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

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

Methods, systems, and computer-readable media for capacity reservation for virtualized graphics processing are disclosed. A request is received to attach a virtual GPU to a virtual compute instance. The request comprises one or more constraints. Availability information is retrieved from a data store that indicates virtual GPUs available in a provider network and matching the one or more constraints. A virtual GPU is selected from among the available virtual GPUs in the availability information. The selected virtual GPU is reserved for attachment to the virtual compute instance. The virtual compute instance is implemented using CPU resources and memory resources of a physical compute instance, the virtual GPU is implemented using a physical GPU in the provider network, and the physical GPU is accessible to the physical compute instance over a network.

Capacity reservation for virtualized graphics processing

A first remote virtualized graphics device is instantiated in response to a determination that processing of graphics operations is to be enabled in a first availability mode on behalf of a compute instance. A configuration operation is performed at a routing device to enable packets from the first remote virtualized graphics device to be transmitted to a graphics result destination. In response to an indication of unavailability, the routing device is configured to enable packets from a second remote virtualized graphics device to be directed to the graphics result destination.

Availability modes for virtualized graphics processing

Methods, systems, and computer-readable media for dynamic and application-specific virtualized graphics processing are disclosed. Execution of an application is initiated on a virtual compute instance. The virtual compute instance is implemented using a server. One or more graphics processing unit (GPU) requirements associated with the execution of the application are determined. A physical GPU resource is selected from a pool of available physical GPU resources based at least in part on the one or more GPU requirements. A virtual GPU is attached to the virtual compute instance based at least in part on initiation of the execution of the application. The virtual GPU is implemented using the physical GPU resource selected from the pool and accessible to the server over a network.

Dynamic and application-specific virtualized graphics processing

A determination is made that network access between a virtualized graphics device and a compute instance of a client is to be enabled. A source network address for graphics-related traffic of the compute instance is identified. From a range of source port numbers associated with the source network address, a particular source port number which is unused is found. Routing metadata is transmitted to one or more routing devices indicating that a key based at least in part on (a) the source network address and (b) the particular source port number is to be used to identify a route for network packets from the first application compute instance to a virtualized graphics device.

Growing community of inventors

Seattle, WA, United States of America

Yuxuan Liu