Amazon Technologies, Inc.

Telefonaktiebolaget Lm Ericsson (publ)

Finisar Corporation

Jiangtao Zhang

Ramu Panayappan

Bengt Johansson

Sten Pettersson

Boyu Wang

Gunja Agrawal

Vijay Dheeraj Reddy Mandadi

Mark Fawaz

Lasse Olsson

Fan Chen

Huiping Li

Roland Mesde

Raviprasad V Mummidi

Vivek Chawda

Fahua Lan

Bharath Bhoopalam

Sriram Venkatadri

Abdul Jabbar Abdul Rasheed

Mark Hlady

Javier Arturo Thiele-Ruiz

Yi Wang

Ting Hsiang Eddy Chu

Peter Aydin Sorensen

Wenjing Cao

Emery Jacob Comstock

Pranav Prabhakar Firake

Kanishk Kaul

Donghao Wei

Sreenaath Vasudevan

Xufan Hua

Sten Rune Pettersson

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 providing an access control list (ACL) assisted process for filtering the analysis and display of dependency relationships among software application components (e.g., packages, files, classes, etc.), e.g., as part of modernization processes aimed at decomposing monolithic applications, identifying anti-patterns, or otherwise analyzing such applications. 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 application artifacts (e.g., source code or bytecode files) associated with the software applications in users' computing environments. Various techniques are described for using ACLs containing entries enabling or disabling the analysis or display of various application components to customize various modernization processes and results displays.

Access control list (ACL) based static analysis of software application artifacts

Techniques are described for enabling model-driven server migration workflows in a cloud provider network. Cloud provider networks often provide various types of tools and services that enable users to migrate computing resources (e.g., servers, databases, applications, etc.) from users' on-premises computing environments to a cloud provider network. A model-driven server migration service as described herein comprises a plurality of modular migration components including, e.g., a snapshot validation component, a snapshot conversion component, an injection component, etc. The model-driven server migration service enables users to customize server migration workflows using server migration templates containing descriptive configurations for some or all of the provided migration components.

Model-driven server migration workflows

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 application to automatically generate and execute test cases as part of a containerization workflow used to modernize various types of legacy software applications. A software modernization application enables a user to identify a legacy application to convert into a containerized application. Once identified, the software modernization application automatically packages application artifacts and identified dependencies into container images and creates a deployment pipeline used to deploy the containerized application into testing and production environments of a service provider network, among other processes. The software modernization application also instruments the legacy application to generate log data reflecting requests and responses received and processed by the application during operation. This instrumentation data can be used in some embodiments to automatically generate test cases used to test a containerized version of the software application to ensure that the application continues to operate as expected.

Automatic test case generation and execution for containerization workflows

Techniques are described for enabling the automatic refactoring of software application source code to mitigate identified anti-patterns and other software modernization-related issues. A software modernization system analyzes software applications to generate various types of modernization report information, where the report information can include identifications of various types of design and cloud anti-patterns, proposed decompositions of monolithic applications into subunits, refactoring cost information, recommended modernization tools and migration paths, among other such information. A software modernization system further includes a refactoring engine that can automatically refactor source code based on such application analysis information, e.g., to automatically address identified anti-patterns, restructure code for decomposition, etc. A refactoring engine performs refactoring actions based on refactoring templates, machine learning (ML) refactoring models, or other input.

Automatic source code refactoring to mitigate anti-patterns

Techniques for creating an aggregated machine learning (ML) model from a plurality of candidate ML models are described. According to some embodiments, a machine learning service generates an aggregated machine learning model from a first machine learning model and a second machine learning model, selects the first machine learning model, the second machine learning model, or the aggregated machine learning model for usage, and performs an inference with the selected machine learning model. Additionally, a user may select the candidate models to be trained.

Training service for an aggregated machine learning model

This disclosure describes techniques implemented partly by a service provider network for containerizing applications. In an example, the techniques may include receiving annotated source code of an application to be containerized, analyzing one or more application component annotations included in the annotated source code to determine an application component that is to be included in a container associated with a containerized version of the application, and analyzing one or more method annotations included in the annotated source code to determine one or more methods of the application component to be included in an application programming interface (API) for the container. The container including the application component may then be built and the API for the container may be generated based at least in part on the one or more methods of the application component.

Annotation based automated containerization

Techniques for a service provider network to provide users with software components that pre-process raw data stored in user computing networks to generate training data that is usable by artificial-intelligence (AI) services. The AI services may utilize models to provide various functionality to users, and the users may desire to train the models with data sets that are specific to their data sets. The service provider network can develop software components that are configured to process raw data into training data for various AI services. The software components can be provided to the user computing networks and executed locally, rather than the raw data having to being moved from the user computing network and to the service provider network. The training data can then be sent to the service provider network and used by the AI services to train ML models for use by the user.

Pre-processing raw data in user networks to create training data for service provider networks

Machine learning assisted source code refactoring to mitigate anti-patterns

Techniques are described for enabling an application modernization system to efficiently identify various types of software development 'anti-patterns' (including cloud anti-patterns) associated with software applications. An anti-pattern is broadly any characteristic of a software application or system that represents an undesirable or suboptimal solution to a problem within a given software design context. Depending on the particular software design context, examples of possible anti-patterns and cloud anti-patterns can include the use of insecure network protocols, an application's reliance on local file system access, the use of a deprecated or outdated class, function, or library, etc. An application modernization system described herein includes a modernization assessment client application that identifies the presence of anti-patterns in users' software applications using a collection of anti-pattern definitions (e.g., defined based on an anti-pattern definition syntax) obtained from an application modernization service of a cloud provider network.

Dynamic cloud anti-pattern detection for a modernization assessment service

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

This disclosure describes techniques implemented partly by a service provider network for containerizing applications. In an example, the techniques may include requesting process relationship information for one or more potential processes of an application, receiving the requested process relationship information for the one or more potential processes of the application, and based on the received process relationship information, configuring a process relationship detection algorithm. Then, using the configured process relationship detection algorithm, the techniques may determine a respective relationship score for individual process pairs of processes operating on a system executing the application and determine one or more individual process pairs that have a respective relationship score that is equal to or above a threshold to be one or more cooperating process pairs.

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Pleasanton, CA, United States of America

Jiangtao Zhang