2014 · 85 citations · 11 references
ComputingCloud ComputingInformation TechnologyCloud ArchitectureCloud InfrastructureDesign AutomationCloud Infrastructure DesignCloud Resource ManagementResource ProvisioningWorkflow OrchestrationApplication ProvisioningCloud ProvisioningCloud Resource ProvisioningOrchestrationProvisioningProvisioning AutomationService ProvisioningTopology Models
Automation of application provisioning is a key challenge in cloud computing, and TOSCA offers both declarative and imperative processing models, each with distinct advantages and limitations. The study proposes a standards‑based method to merge declarative and imperative TOSCA models by generating provisioning plans from topology models. The approach generates executable provisioning workflows from TOSCA topology models, implemented in an open‑source toolchain that supports automatic execution and developer customization. The end‑to‑end toolchain demonstrates the feasibility of the combined approach and shows acceptable extensibility, performance, and manageable complexity.
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.
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