Search for: All records

The Chameleon project developed a unique experimental testbed by adapting a mainstream cloud implementation to the needs of systems research community and thereby demonstrated that clouds can be configured to serve as a platform for this type research. More recently, the CloudBank project embarked on a mission of providing a conduit to commercial clouds for the systems research community that eliminates much of the complexity and some of the cost of using them for research. This creates an opportunity to explore running systems experiments in a combined setting, spanning both research and commercial clouds. In this paper, we present an extension to Chameleon for constructing controlled experiments across its resources and commercial clouds accessible via CloudBank, present a case study of an experiment running across such combined resources, and discuss the impact of using a combined research platform. 

Infrastructure cloud computing allows its clients to allocate on-demand resources, typically consisting of a repre- sentation of a compute node. In general however, there is a need for allocating resources other than nodes and managing them in more controlled ways than simply on demand. This paper generalizes the familiar “compute power on demand” pattern by introducing the abstraction of an allocatable resource, describing its properties, and implementation for different types of resources. We further describe architecture for a generic allocatable resource management service that can be extended to manage diverse types of resources as well as the implementation of this architecture in the OpenStack Blazar service to manage resources ranging from bare-metal compute nodes to network segments. Finally, we provide a usage analysis of this service on the Chameleon testbed and use it to illustrate the effectiveness of resource management methods as well as the need for incentives in usage arbitration. 

Chameleon is a large-scale, deeply reconfigurable testbed built to support Computer Science experimentation. Unlike traditional systems of this kind, Chameleon has been configured using an adaptation of a mainstream open source infrastructure cloud system called OpenStack. We show that operating cloud systems requires both more skill and extra effort on the part of the operators - in particular where those systems are expected to evolve quickly - which can make systems of this kind expensive to run. We discuss three ways in which those operations costs can be managed: innovative mon- itoring and automation of systems tasks, building “operator co-ops”, and collaborating with users.