This chapter analyzes the Hybrid Cloud computing model, a paradigm that combines on-premise Private Clouds with the resources of Public Clouds. This new model is not yet fully developed, and there is still a lot of work to be done before true multi-Cloud installations become mature enough to be used in production environments. A review of some of its limitations and the challenges that have to be faced is done in this chapter, and some common techniques to address the challenges studied are also included. It also presents a Hybrid Cloud architecture based on the OpenNebula Cloud toolkit, trying to overcome some of the challenges, and present some real-life experiences with this proposed architecture and Amazon EC2.
TopBackground
Usually, computational resources needed by organization's services are leased in the form of Virtual Machines (VMs) from the local infrastructure. Hence, these organizations are effectively transforming their rigid infrastructure into a flexible and agile provisioning platform.
A natural step for these companies is to outsource part of the computational capacity they need from an external provider. In this way, they can face peak demands in a cost-effective manner. Also, they can better serve user requests by moving some services to an external Cloud closer to the user, or implement high availability strategies federating different Cloud infrastuctures.
The federation of Cloud infrastructures offers multiple benefits, such as the possibility of scaling-out the local data center with external resources of a remote Cloud; the possibility of aggregating resources from different Cloud infrastructures to increase the computing capacity; or the possibility of gaining access to different Cloud providers that offer different features, different types of resources, and different price schemes, allowing multi-cloud deployment of services based on cost and/or performance optimization criteria.
Although there is no general agreement on the classification of these architectures, the federation architecture models can be classified in the following four groups (Rochwerger et al., 2009):