Compiling Abstract Specifications into Concrete Systems - Bringing Order to the Cloud

Currently, there are important limitations in the abstractions that support creating and managing services in a cloud-based IT system. As a result, cloud users must choose between managing the low-level details of their cloud services directly (as in IaaS), which is time-consuming and error-prone, and turning over significant parts of this management to their cloud provider (in SaaS or PaaS), which is less flexible and more difficult to tailor to user needs. To alleviate this situation we propose a high-level abstraction called the requirement model for defining cloud-based IT systems. It captures important aspects of a system’s structure, such as service dependencies, without introducing lowlevel details such as operating systems or application configurations. The requirement model separates the cloud customer’s concern of what the system does, from the system engineer’s concern of how to implement it. In addition, we present a “compilation” process that automatically translates a requirement model into a concrete system based on pre-defined and reusable knowledge units. When combined, the requirement model and the compilation process enable repeatable deployment of cloud-based systems, more reliable system management, and the ability to implement the same requirement in different ways and on multiple cloud platforms. We demonstrate the practicality of this approach in the ANCOR (Automated eNterprise network COmpileR) framework, which generates concrete, cloud-based systems based on a specific requirement model. Our current implementation1 targets OpenStack and uses Puppet to configure the cloud instances, although the framework will also support other cloud platforms and configuration management solutions. 1Current ANCOR implementation is available and is distributed under the GNU (version 3) General Public License terms on: https: //arguslab.github.io/ancor/ Tags: cloud, modeling networking configuration, configuration management, deployment automation