Welcome to the Orca project. Our web site is currently under development. To get started with Orca please follow the instructions in the Getting Started Guide .
Open Resource Control Architecture (ORCA) is an extensible architecture for on-demand networked computing infrastructure. It may be viewed as a service-oriented resource control plane for an Internet operating system . Its purpose is to manage the hosting of diverse computing environments (guests ) on a common pool of networked hardware resources such as virtualized clusters, storage, and network elements. More information about Orca is available at this site.
Orca is a resource control plane organized around resource leasing as a foundational abstraction. The architecture of Orca does not impose any particular structure on the shared resources, which means that it is possible to instantiate any experimental configuration for purposes of testing and deployment. The Orca framework is one possible way to realize the goals of NSF's GENI project . Orca is compatible with much of the experimental GENI architecture. Some concepts and entities in GENI map directly on to corresponding concepts and entities within Orca.
Both GENI and Orca are built on top of a substrate of physical resources. The GENI Management Core (GMC) corresponds to the Site authority and Broker actors in the Orca system. In both cases, user services run on hosted resources mediated by a management layer.
It is common today to manage sharing of networked resources through "middleware" such as grid computing software and job managers for networked clusters. The architectural choice to virtualize at the infrastructure level is a fundamentally new direction that is complementary to the wide range of existing middleware approaches. The value proposition is that it offers safe and effective sharing at a lower level of abstraction. For example, a user can obtain an on-demand private machine instance rather than the service of queuing their job to run on someone else's machine. In this example, the "raw" machine abstraction offers users on-demand access and control over personal, customized computing environments.