Alexander V. Konstantinou

An architecture for virtual solution composition and deployment in infrastructure clouds

The combination of virtual server technology and the Infrastructure-as-a-Service (IaaS) approach to utility computing promises to revolutionize the way in which distributed software services are deployed. Server virtualization technology can be used to capture complete reusable software stacks, shifting the complexity of middleware installation and configuration from deployment to packaging. IaaS clouds provide a set of interfaces for controlling virtual machines and configuring their hardware and network environment, substantially reducing the complexity of service provisioning. In this paper we identify and tackle a few of the remaining challenges in fulfilling the promise of radical simplification of distributed software service composition and deployment. We propose an approach and architecture for composition and deployment of virtual software services in cloud environments. We introduce a virtual appliance model which treats virtual images as building blocks for composite solutions. Virtual appliances use a port abstraction to negotiate their communication parameters. A solution architect creates a virtual solution model by composing virtual appliances and defining requirements on the environment in a cloud-independent manner. The virtual solution model is transformed to a cloud-specific virtual solution deployment model used to generate a parameterized deployment plan that can be executed by an unskilled user. We validated our approach through a prototype implementation demonstrating flexible composition and automated deployment in our local lab virtualization infrastructure and in Amazon EC2.

Model driven provisioning: bridging the gap between declarative object models and procedural provisioning tools

Todays enterprise data centers support thousands of mission-critical business applications composed of multiple distributed heterogeneous components. Application components exhibit complex dependencies on the configuration of multiple data center network, middleware, and related application resources. Applications are also associated with extended life-cycles, migrating from development to testing, staging and production environments, with frequent roll-backs. Maintaining end-to-end data center operational integrity and quality requires careful planning of (1) application deployment design, (2) resource selection, (3) provisioning operation selection, parameterization and ordering, and (4) provisioning operation execution. Current data center management products are focused on workflow-based automation of the deployment processes. Workflows are of limited value because they hard-code many aspects of the process, and are thus sensitive to topology changes. An emerging and promising class of model-based tools is providing new methods for designing detailed deployment topologies based on a set of requirements and constraints. In this paper we describe an approach to bridging the gap between generated desired state models and the elemental procedural provisioning operations supported by data center resources. In our approach, we represent the current and desired state of the data center using object models. We use AI planning to automatically generate workflows that bring the data center from its current state to the desired state. We discuss our optimizations to Partial Order Planning algorithms for the provisioning domain. We validated our approach by developing and integrating a prototype with a state of the art provisioning product. We also present initial results of a performance study.

Pattern Based SOA Deployment

A key function of a Service Oriented Architecture is the separation between business logic and the platform of its implementation and deployment. Much of the focus in SOA research has been on service design, implementation, composition, and placement. In this paper we address the challenge of configuring the hosting infrastructure for SOA service deployment. The functional and non-functional requirements of services impose constraints on the configuration of their containers at different levels. Presently, such requirements are captured in informal documents, making service deployment a slow, expensive, and error-prone process. In this paper, we introduce a novel approach to formally capturing service deployment best-practices as model-based patterns. Deployment patterns capture the structure of a solution, without bindings to specific resource instances. They can be defined at different levels of abstraction supporting reuse, and role-based iterative refinement and composition. We show how we extended an existing model driven deployment platform to support pattern based deployment. We formally define pattern semantics, validation, and refinement. We also present an algorithm for automatically instantiating such patterns on multiple distributed service environments. Our approach has been verified in a large prototype that has been used to capture a variety of functional and non-functional deployment constraints, and demonstrate their end-to-end maintenance and realization.

Managing the configuration complexity of distributed applications in Internet data centers

In this article we examine the challenges faced by data center administrators when deploying and configuring Web applications. We discuss how the configuration dependencies of these Web applications cut across software stacks, network layers, and middleware container boundaries. We argue that the deployment and configuration process requires the combined expertise from multiple domains such as application, middleware, network, security, reliability, and performance. We review the model-based tools available today to manage the configuration complexity of these applications and introduce a new tool that extends the existing state of the art by automatically generating actionable distributed deployment models using model transformation techniques. The key idea behind this new tool is the principle of separation of concerns: developers capture the logical structure of the application in a model, best practices experts define deployment model transformation rules, deployers specify the required deployment patterns, and an operator provides a model describing the data center resources. The tool automatically finds solutions based on these four inputs and executes the deployment.

NESTOR: an architecture for network self-management and organization

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.

Reducing the complexity of application deployment in large data centers

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.

Automatic Realization of SOA Deployment Patterns in Distributed Environments

Deployment patterns have been proposed as a mechanism to support the provisioning of SOA-based services. Deployment patterns represent the structure and constraints of composite solutions, including non-functional properties, such as performance, availability, and security, without binding to specific resource instances. In previous work [1], we have presented a formal mechanism for capturing such service deployment patterns using models. Our pattern models define abstract connectivity and configuration requirements which are then realized by an existing or planned infrastructure. Realization mapping is used to enforce policies, and is materialized at deployment time. In this paper we extend that work to address the problem of automatic pattern realization over a given infrastructure. We first formalize the problem and present three variations of increasing power and complexity. We then present a variation of a search-based graph isomorphism algorithm with extensions for our pattern model semantics. Next, we show that our worst-case exponential complexity algorithm performs well in practice, over a number of pattern and infrastructure combinations. We speculate that this is because deployment topologies represent heavily labeled and sparse graphs. We present a number of heuristics which we have compared experimentally, and have identified one which performs best across most scenarios. Our algorithm has been incorporated into a large deployment modeling platform, now part of the IBM Rational Software Architect (RSA) tool [2].

A2A: An Architecture for Autonomic Management Coordination

A central challenge of autonomic systems is how to discover, monitor, analyze and control configuration data to assure operational integrity. Current architectures for configuration data management focus on federating repositories that are loosely synchronized, and do not offer autonomic coordination services. We present A2A, a novel autonomic peering architecture which delivers a unified and consistent view of actual element configuration for autonomic systems and managers, and provides synchronization primitives enabling policy coordination and mediation. We discuss the different synchronization semantics and protocols used by systems and managers to access and manipulate configuration data stored in a distributed Modeler. We show how dependent or conflicting policy actions can be automatically detected, correlated and brought to mediation. The A2A architecture has been partially implemented in a large prototype system that has been successfully demonstrated in security, network configuration, and active network applications.