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.

An architecture for virtual solution composition and deployment in infrastructure clouds

Modern cloud computing environments support a relatively high degree of automation in service provisioning, which allows cloud service customers (CSCs) to dynamically acquire services required for deploying cloud applications. Cloud modeling languages (CMLs) have been proposed to address the diversity of features provided by cloud computing environments and support different application scenarios, such as migrating existing applications to the cloud, developing new cloud applications, or optimizing them. There is, however, still much debate in the research community on what a CML is, and what aspects of a cloud application and its target cloud computing environment should be modeled by a CML. Furthermore, the distinction between CMLs on a fine-grain level exposing their modeling concepts is rarely made. In this article, we investigate the diverse features currently provided by existing CMLs. We classify and compare them according to a common framework with the goal to support CSCs in selecting the CML that fits the needs of their application scenario and setting. As a result, not only features of existing CMLs are pointed out for which extensive support is already provided but also in which existing CMLs are deficient, thereby suggesting a research agenda.

A Systematic Review of Cloud Modeling Languages

The automation of application provisioning is one of the most important issues in Cloud Computing. The Topology and Orchestration Specification for Cloud Applications (TOSCA) supports automating provisioning by two different flavors: (i) declarative processing is based on interpreting application topology models by a runtime that infers provisioning logic whereas (ii) imperative processing employs provisioning plans that explicitly describe the provisioning tasks to be executed. Both flavors come with benefits and drawbacks. This paper presents a means to combine both flavors to resolve drawbacks and to profit from benefits of both worlds: we propose a standards-based approach to generate provisioning plans based on TOSCA topology models. These provisioning plans are workflows that can be executed fully automatically and may be customized by application developers after generation. We prove the technical feasibility of the approach by an end-to-end open source toolchain and evaluate its extensibility, performance, and complexity.

Combining Declarative and Imperative Cloud Application Provisioning Based on TOSCA

In this paper we describe a method and corresponding tool chain that allows moving an application to the cloud. In particular, we support to split an application such that various parts of it are moved to different clouds. This split can be done manually or by support of optimization algorithms. The split application is then automatically provisioned in the different target clouds. A metamodel for such applications supporting the proposed method is introduced. The architecture of a supporting tool is described. Experiences from the usage of the proposed method are reported.

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Moving applications to the cloud: an approach based on application model enrichment

A provisioning server can maintain a library of virtual images. The virtual images can be a disk image of software that has been previously installed according to metadata. The metadata can represent parameters utilized to create the virtual image and the parameters required to provision the virtual image on a target machine. The provisioning server can maintain the metadata for a virtual image in a virtual image record associated with the virtual image. The provisioning server can provision a virtual image to one or more target machines. To provision the virtual image, the provisioning server can copy the virtual image to a storage device on the one or more target machines and configure the target machine according to metadata in the associated virtual image record.

Systems and methods for providing a library of virtual images in a software provisioning environment

Today's 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.

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Alok Meghranjani

Papers

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