Amazon Technologies, Inc.

Deepak Singh

William Andrew Thurston

Kiran Kumar Meduri

Anirudh Balachandra Aithal

Anthony Joseph Suarez

Muhammad Umer Azad

Tipu Saleem Qureshi

Daniel Robert Gerdesmeier

Euan Skyler Kemp

John Merrill Phillips

Nachiketh Rao Potlapally

William John Earl

Eric Jason Brandwine

Nicholas Patrick Wilt

Scott Michael Le Grand

Matthew James Wood

Christopher Brian Barclay

Alireza Farhangi

Yu Gu

Sairam Suresh

Sean Henry Lewis Meckley

Sharon Shapira

Pierre Rognant

Kyle West Thomson

Stefano Buliani

Techniques for leveraging a large language model (LLM) in software development are described. A description of an error message associated with a software system hosted by the multi-tenant provider network is received. An LLM is prompted with an analysis prompt to generate an analysis of the error message, the analysis prompt including the description of the error message, and the analysis of the error message is received from the LLM. The LLM is prompted with a suggested resolution prompt to generate a suggested resolution to a cause of the error message, the suggested resolution prompt including the description of the error message and the analysis of the error message, and the suggested resolution is received from the LLM. A change to resolve the cause of the error message is sent to an originator of the received description, the change based at least in part on the suggested resolution.

Automated error troubleshooting via generative AI software development assistant

A task definition is received. The task definition indicates at least a location from which one or more software image can be obtained and information usable to determine an amount of resources to allocate to one or more software containers for the one or more software image. A set of virtual machine instances in which to launch the one or more software containers is determined, the one or more software image is obtained from the location included in the task definition and is launched as the one or more of software containers within the set of virtual machine instances.

System for managing and scheduling containers

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 system and method for software deployment, where the system and method include, at a deployment service, obtaining a software package and determining that a client device is ready to receive at least a portion of the software package. If the client device is ready, providing at least the portion of the software package to the client device, launching at least the provided portion of the software package as set of instructions executing in a test container, and performing a set of tests on the executing set of instructions in the test container. Based at least in part on results of performing the set of tests, determining whether to cause at least the provided portion of the software package to execute in an active container on the client device.

Using containers for update deployment

A mobile device may include a plurality of hardware devices. The physical devices may be virtualized to enable efficient access and utilization. Furthermore, the mobile device may support containerization of applications executed by the hardware devices. The virtualized physical devices may be provided to various containers executed by the mobile device. Furthermore, a priority mechanism may be used to manage container access to the physical devices.

Virtualized hardware support for mobile devices

Systems and methods are described to enable automation of service discovery records, such as domain name system (DNS) records, based on rules. An administrator of a hosted server, such as a virtual machine instance, may submit a rule to a rule-based DNS resolution system for generating DNS records to assist in discover of the hosted server. The rule may include criteria specifying servers to which the rule applies, as well as criteria for generating a domain name to associate with the server within a DNS record. The rule-based DNS resolution system can integrate with a hosting system providing the hosted server to obtain configuration information of the hosted server and generate DNS records in accordance with the rule. Rules may also be utilized to automate other aspects of the rule-based DNS resolution system, such as health checking and load balancing.

Rule-based automation of DNS service discovery

A virtualized system that is capable of executing a computation that has been identified as a repeatable computation and recording various representations of the state of the computing environment throughout the execution of the repeatable computation, where the state of the computing environment can be cryptographically signed and/or verified using a trusted platform module (TPM), or other cryptographic module. For example, a TPM embedded in the host computing device may generate a hash measurement that captures the state of the repeatable computation at the time of the computation. This measurement can be digitally signed using one or more cryptographic keys of the TPM and recorded for future use. The recorded state can subsequently be used to repeat the computation and/or determine whether the computation was repeated successfully according to certain defined criteria.

Cryptographically verified repeatable virtualized computing

A mobile device may include a plurality of physical antennas configured to receive and transmit data. The physical antennas may be virtualized to enable efficient access and utilization of the computing resources of the physical antenna. Furthermore, the mobile device may support containerization of application executed by the mobile device. The virtualized physical antennas may be provided to various containers executed by the mobile device. Furthermore, a priority mechanism may be used to manage container access to the physical antennas.

A mobile device may provide virtualization of physical resources and containerization of software application. Various deployment strategies such as, a blue-green deployment strategy, may be utilized to perform application and other software updates between various containers of a mobile device. State information and other information from an active container may be provided to the update container during handover operations.

Container handover for device updates

A service provider system may implement ECC-like features when executing computations on GPUs that do not include sufficient error detection and recovery for computations that are sensitive to bit errors. During execution of critical computations on behalf of customers, the system may automatically instrument program instructions received from the customers to cause each computation to be executed using multiple sets of hardware resources (e.g., different host machines, processor cores, or internal hardware resources). The service may provide APIs with which customers may instrument their code for execution using redundant resource instances, or specify parameters for applying the ECC-like features. The service or customer may instrument code to perform (or cause the system to perform) checkpointing operations at particular points in the code, and to compare intermediate results produced by different hardware resources. If the intermediate results do not match, the computation may be restarted from a checkpointed state.

Growing community of inventors

Issaquah, WA, United States of America

Deepak Singh