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UBITECH exhibits the MAESTRO distributed apps composition and cloud services orchestration platform at DockerCon 2019

Sponsoring the 3-day technology conference DockerCon 2019( that takes place from April 29 – May 2 at Moscone West in San Francisco, UBITECH has a dedicated exhibition booth at the Ecosystem Expo of the conference, for presenting and demonstrating the sophisticated MAESTRO platform that enables distributed applications composition and cloud services orchestration. The MAESTRO platform ( is an advanced developer framework for cloud orchestration and infrastructure automation, that gives you the power to design, deploy, and manage cloud-native containerized components in both public and private cloud environments. Built with IaaS (Infrastructure-as-a-Service) abstraction, the MAESTRO platform lets you create easy-to-manage, easy-to-scale workflows with Docker Compose applications. It comes with advanced off-the-shelf features to support extensive monitoring, security enforcement, elasticity management, and operational analytics.

In particular, MAESTRO provides you with a pragmatic, efficient approach to addressing the various sophisticated challenges of both container and multi-cloud adoption such as optimal infrastructure provisioning, seamless monitoring, autonomic elasticity management, and security. Through a set of intelligent orchestration mechanisms, the MAESTRO framework lets you optimize the placement of cloud-native applications based on user-defined constraints. Thanks to an IaaS abstraction layer, MAESTRO deploys applications in many different IaaS backends like OpenStack, Amazon, and Google Cloud. Before deployment, you can configure and activate a set of your preferred monitoring metrics or even attach your own custom monitoring probes. MAESTRO also lets you create rich expressions that trigger scaling or security events during runtime; these events are tackled by configurable handlers. Finally, deployed applications are automatically benchmarked though a sophisticated profiling engine, with regards to their computational and memory intensiveness.