Adobe, Inc.

Carsten Ziegeler

Allaert Joachim David Bosschaert

Michael A Marth

Philipp Suter

Nitika Agarwal

Systems and methods use machine learning to find an efficient startup sequence for the components of an application. An example method includes receiving the plurality of components of the modular system and a mechanism for launching the modular system and invoking a machine-learning ordering tool to generate at least one startup sequence. The at least one startup sequence may be returned with corresponding launch metrics. The method may also include determining, based on launch metrics for the at least one startup sequence, whether an efficient sequence has been identified. Responsive to the efficient sequence being identified, the operations can include returning the efficient startup sequence, wherein the modular system is launched using the efficient startup sequence. Responsive to failing to identify the efficient sequence, the operations can include repeating the invoking and determining.

Optimizing the startup speed of a modular system using machine learning

Techniques disclosed herein dynamically scale components of an application using microservices. An exemplary technique involves executing the application on a computing device such as a server. For example, the application can be executed to host server-side services using the components of the application locally on the computing device. The technique monitors use of the components during execution of the application and determines to migrate a component to a microservice based on the use of the component during the execution of the application. The technique then migrates the component to a micro service by initiating launch of the micro service on a remote computing device and converting local calls for the component to remote calls for the microservice in the application.

Dynamically scaling application components using microservices

Embodiments of the present invention provide systems, methods, and computer storage media directed at automated testing of shell scripts and core functionality of a software application under test utilizing the same testing environment. In embodiments, a testing environment that is configured to perform automated testing of core functionality of a software application under test is provided with a test library that extends the testing capabilities of the testing environment to also perform automated testing of shell scripts of the software application under test. The core functionality can be encoded in a first programming language, while the shell scripts are encoded in a second programming language. Other embodiments may be described and/or claimed.

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Paderborn, Germany

Carsten Ziegeler