Private cloud infrastructures are now widely deployed and adopted across technology industries and research institutions. Although cloud computing has emerged as a reality, it is now known that a single cloud provider cannot fully satisfy complex user requirements. This has resulted in a growing interest in developing hybrid cloud solutions that bind together distinct and heterogeneous cloud infrastructures. In this paper we describe the orchestration approach for heterogeneous clouds that has been implemented and used within the INDIGO-DataCloud project. This orchestration model uses existing open-source software like OpenStack and leverages the OASIS Topology and Specification for Cloud Applications (TOSCA) open standard as the modeling language. Our approach uses virtual machines and Docker containers in an homogeneous and transparent way providing consistent application deployment for the users. This approach is illustrated by means of two different use cases in different scientific communities, implemented using the INDIGO-DataCloud solutions.
However, providers and users perceive that lower level (i.e. infrastructure provision and management) standards hinder the adoption of cloud infrastructures @cite_41 . Cloud technologies and frameworks tend to have a fast development pace, adding new functionalities as they evolve, whereas standards' evolution is sometimes not as fast as the underlying technologies. This has been perceived as a negative fact limiting the potential of a given cloud infrastructure, that sees its functionality and flexibility decreased. On top of this, infrastructure management is also perceived too low level when moving to more service-centric approaches that require not only the deployment and management of services, but also all their operational concerns (like fault or error handling, auto-scaling, etc.) @cite_80 .
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We present a new environment for computations in particle physics phenomenology employing recent developments in cloud computing. On this environment users can create and manage “virtual” machines on which the phenomenology codes tools can be deployed easily in an automated way. We analyze the performance of this environment based on “virtual” machines versus the utilization of physical hardware. In this way we provide a qualitative result for the influence of the host operating system on the performance of a representative set of applications for phenomenology calculations. Cloud computing provides users near instant access to seemingly unlimited resources, and provides service providers the opportunity to deploy complex information technology infrastructure, as a service, to their customers. Providers bene t from economies of scale and multiplexing gains a orded by sharing of resources through virtualization of the underlying physical infrastructure. However, the scale and highly dynamic nature of cloud platforms impose signi cant new challenges to cloud service providers. In particular, realizing sophisticated cloud services requires a cloud control framework that can orchestrate cloud resource provisioning, con guration, utilization and decommissioning across a distributed set of physical resources. In this paper we advocate a data-centric approach to cloud orchestration. Following this approach, cloud resources are modeled as structured data that can be queried by a declarative language, and updated with well-de ned transactional semantics. We examine the feasibility, bene ts and challenges of the approach, and present our design and prototype implementation of the Data-centric Management Framework (DMF) as a solution, with data models, query languages and semantics that are speci cally designed for cloud resource orchestration.