The deployment of composite applications and services in distributed or compute cloud environments is still a challenging task that is a key source of operational cost and risk. Current approaches to composite deployments can be categorized as workflow based and model based. In the workflow based approach, deployers create end-to-end workflows to automate application deployment, while in the model based approach architects design detailed “desired state” models and validate they meet all requirements and constraints. Today, there is no formally understood relationship and mapping between “desired state” models and deployment workflows, posing a challenge and limitation on architects and deployers. In this paper we propose a new model based approach to bridge the gap between deployment models and workflows. Our approach supports separation of concerns where basic automation building blocks (such as scripts and workflows) can be developed independently of the resource model and with no knowledge of it. Therefore, the method enables deployers to continue to leverage useful libraries of automation building blocks, while enjoying the benefits of a sound resource model, used for validation and constraint satisfaction. We propose algorithms and implementation to generate end-to-end workflows for input “desired state” resource models, based on given libraries of automation building blocks. Our approach has been incorporated into IBM's leading deployment modeling platform [15] and is in active use by customers in a large range of applications.

The deployment and configuration of distributed applications is a human intensive and highly complex process that poses significant challenges to data center operators. The process involves many cross-cutting concerns such as connectivity, performance, and security requirements, as well as resource availability, policies and best practices. These interdependencies represent a significant source of complexity, cost, and risk in data center management. In this paper we address this problem using a new approach that leverages concepts from the model-driven architecture research domain. We describe a prototype application deployment automation system based on model transformation techniques. We show how model transformation techniques can replace the manual process of writing and adapting scripts and workflows, reduce the deployment complexity, guarantee configuration integrity and consistency, and allow for a separation of concerns.

Configuration management presently requires complex labor-intensive processes by experts. A single configuration task-installing/reconfiguring a system, or provisioning a service-typically involves a large number of activities fragmented among multiple network elements, each with its own proprietary configuration management instrumentation and tools. A change may cause configuration inconsistencies resulting in failures or inefficiencies; undoing changes to recover an operational state is often very difficult or even practically impossible. Therefore, configuration management is very costly, error prone, and often results in unpredictable failures and costly recovery. NESTOR seeks to replace labor-intensive configuration management with one that is automated and software-intensive. Configuration management is automated by policy scripts that access and manipulate respective network elements via a resource directory server (RDS). RDS provides a uniform object-relationship model of network resources and represents consistency in terms of constraints; it supports atomicity and recovery of configuration change transactions, and mechanisms to assure consistency through changes. RDS pushes configuration changes to network elements using a layer of adapters that translate operations on its object-relationship model to actions on respective elements. NESTOR has been implemented in two complementary versions and is now being applied to automate several configuration management scenarios of increasing complexity, with encouraging results.