Amazon Technologies, Inc.

Ramu Panayappan

Jiangtao Zhang

Raviprasad V Mummidi

Vivek Chawda

Boyu Wang

Vijay Dheeraj Reddy Mandadi

Ramapulla Reddy Chennuru

Bharath Bhoopalam

Abdul Jabbar Abdul Rasheed

Srinivasan Ramkumar

Javier Arturo Thiele-Ruiz

Mark Fawaz

Rajesh Yarlagadda

Juveria Kanodia

Rajesh Gopidi

Sreenaath Vasudevan

Cai Liu

Emery Jacob Comstock

Pranav Prabhakar Firake

Kanishk Kaul

Donghao Wei

Techniques for a license manager service of a cloud system to activate and run licensed applications in VPCs with peering connections (or 'peered VPCs') established with configured VPCs. The license manager service may provide users with the ability to select a particular VPC to be configured to use licensed applications, and may further allow users to enable peered VPCs of the configured VPC to use licensed applications. The license manager service may provide a managed experience for activating the licenses for the applications running on computing resources in the configured VPCs and peered VPCs. The license manager service may launch licensed applications on virtual resources in a user's configured VPC and peered VPCs, manage the process for activating the licensed applications with third-party providers, and provide the user with access to licensed applications that have been activated and configured by the license manager service in configured and peered VPCs.

Managed activation of licensed software for resources in peered networks

Techniques are described for assisting users with the process of modernizing software applications from a source software architectural pattern (e.g., a model—view—controller (MVC) pattern, a multitier architecture, etc.) to a target architectural pattern (e.g., microservices, event-driven architecture, etc.). A decomposition analyzer receives as input artifacts associated with a software application under analysis and an architectural pattern that a user desires to use for a modernized version of the software application. The decomposition analyzer generates, based on the application artifacts, a graph-based model of the software application, where the model includes nodes representing application components (e.g., classes) and edges representing dependency relationships among the components. The decomposition analyzer further uses machine learning techniques and other types of analyses to identify recommended groupings of nodes from the graph-based model for decomposition. These recommendations can be displayed to users in a visualization providing decomposition assessment information.

Guided modernization of software applications using application templates

Techniques are described for a software modernization service to provide the ability to re-platform software application binaries from one ISA and hardware platform to another. The software modernization service can generate assessment reports for users' legacy applications, including recommendations for hardware platforms available in a cloud provider network for use. Responsive to a user requesting to re-platform an application from one ISA and hardware platform to another, an application re-platforming service automates steps to perform a static and dynamic binary translation of an application and to combine the results into a re-platformed executable. Among other benefits, the described techniques enable the deployment of legacy software applications onto a wider set of target ISA and hardware platforms, thereby enabling such applications to take advantage of the performance, scalability, availability, and other benefits of certain types of hardware platforms as desired by a user.

Application binary replatforming as a service

A network infrastructure modernization service described herein may allow a customer to migrate the underlying network infrastructure topology and configurations of the on-premises environment onto the cloud environment. By collecting the network infrastructure configurations and/or runtime metrics, generating a network model based on the configurations and/or runtime metrics, and applying modernization rules to the network model to generate a network infrastructure template, the network infrastructure modernization service described in the present application enables the customers to be migrate their on-premises applications as well as the underlying network infrastructure onto the cloud environment.

Mapping on-premise network nodes to cloud network nodes

Techniques are described for enabling software migration and modernization services of a cloud provider network to provide completion time forecasts for various types of migration and modernization actions performed by the services relative to users' software applications. A cloud provider network provides a software migration and modernization orchestration service that helps users orchestrate the use of various software migration and modernization services. Depending on the technical characteristics of a user's application and the selection of migration and modernization processes to be performed, an amount of time needed to complete migration and modernization actions can vary widely. The migration and modernization services of the cloud provider network each train and use machine learning (ML) models to forecast an amount of time needed to complete various actions based on historical action execution data collected by the services, thereby providing useful insights into complex migration and modernization processes.

Application migration and modernization action completion time forecasting

Techniques are described for enabling a software modernization system to automatically map binary executable files and other runtime artifacts (e.g., application binaries, Java ARchive (JAR) files, .NET Dynamic Link Library (DLL) files, process identifiers, etc.) to source code associated with the binary executable files, e.g., as part of modernization processes aimed at migrating users' applications to a cloud service provider's infrastructure. A software modernization service of a cloud provider network provides discovery agents and other tools that are capable of creating an inventory of users' software applications and collecting profile data about the software applications. Various techniques are described for automatically identifying the source code associated with software applications identified by a discovery agent in a user's computing environment, thereby improving the efficiency of various software modernization analyses and other modernization processes.

Automatically mapping binary executable files to source code by a software modernization system

Techniques are described for enabling a software modernization assessment service to train and use ML models to automatically generate modernization assessment recommendations for users' software applications and systems. A modernization assessment service collects historical assessment data reflecting past modernization processes and assessments (e.g., application profile information and associated modernization strategies and tools used in past modernization projects). The modernization assessment service uses the historical assessment data to train one or more ML models (e.g., classifiers) that can be used to automatically identify relevant modernization strategies, services, and tools for given software application or system. Responsive to user requests to generate modernization assessment recommendations, the modernization assessment service can use the trained models to automatically generate modernization recommendations and reports.

Machine learning-based software application modernization assessments

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Sunnyvale, CA, United States of America

Ramu Panayappan