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.
In this service-centric context, cloud orchestration is being considered more and more important, as it will play the role needed to perform the abstractions needed to deploy complex service architectures for a wide range of application domains, such as e-government, industry and science. Cloud orchestration involves the automated arrangement, coordination and management of cloud resources (i.e. compute, storage and network) to meet to the user's needs and requirements @cite_27 . Those requirements normally derive from the user demand of delivering a service (such as a web service where there is a need to orchestrate and compose different services together), performing a business logic or process, or executing a given scientific workflow.
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Cloud infrastructures can provide resource sharing between many applications and usually can meet the requirements of most of them. However, in order to enable an efficient usage of these resources, automatic orchestration is required. Commonly, automatic orchestration tools are based on the observability of the infrastructure itself, but that is not enough in some cases. Certain classes of applications have specific requirements that are difficult to meet, such as low latency, high bandwidth and high computational power. To properly meet these requirements, orchestration must be based on multilevel observability, which means collecting data from both the application and the infrastructure levels. Thus in this work we developed a platform aiming to show how multilevel observability can be implemented and how it can be used to improve automatic orchestration in cloud environments. As a case study, an application of computer vision and robotics, with very demanding requirements, was used to perform two experiments and illustrate the issues addressed in this paper. The results confirm that cloud orchestration can largely benefit from multilevel observability by allowing specific application requirements to be met, as well as improving the allocation of infrastructure resources. 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. Highly scalable, resilient commodity hardware along with the availability of huge amounts of data allow consumers and companies to access to a wide range of high-value services through a pay-per-use, pay-as-you-go model. The increasing need for intelligent services at large scale is bringing providers to optimise the use of resources in order to meet the increasing web-scale demand. Programming models are evolving to address the high complexity of service composition, which need resiliency and efficiency. Algorithms and technologies for intelligent orchestration allow multiple owners of cloud and grid infrastructures to satisfy customer requirements and business constraints. We assist in the development of new architectures, frameworks, middlewares and computing models, as well as innovative applications, aimed at supporting scientists and business analysts in the development of unified computing frameworks and data analysis at large scale. This paper summarises a number of contributions given in the area of distributed cloud and grid computing, with particular emphasis on service orchestrations and emergent applications for high-value services. Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments The proposed InterCloud environment supports scaling of applications across multiple vendor clouds We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.