One of the challenges for the future cyber-physical systems is the exploration of large design spaces. Evolutionary algorithms (EAs), which embody a simplified computational model of the mutation and selection mechanisms of natural evolution, are known to be effective for design optimization. However, the traditional formulations are limited to choosing values for a predetermined set of parameters within a given fixed architecture. This project explores techniques, based on the idea of hidden genes, which enable EAs to select a variable number of components, thereby expanding the explored design space to include selection of a system's architecture. Hidden genetic optimization algorithms have a broad range of potential applications in cyber-physical systems, including automated construction systems, transportation systems, micro-grid systems, and space systems. The project integrates education with research by involving students ranging from high school through graduate school in activities commensurate with their skills, and promotes dissemination of the research results through open source distribution of algorithm implementation code and participation in the worldwide Global Trajectory Optimization Competition. Instead of using a single layer of coding to represent the variables of the system in current EAs, this project investigates adding a second layer of coding to enable hiding some of the variables, as needed, during the search for the optimal system's architecture. This genetic hiding concept is found in nature and provides a natural way of handling system architectures covering a range of different sizes in the design space. In addition, the standard mutation and selection operations in EAs will be replaced by new operations that are intended to extract the full potential of the hidden gene model. Specific applications include space mission design, microgrid optimization, and traffic network signal coordinated planning.

Many of the ideas that drive modern cloud computing, such as server virtualization, network slicing, and robust distributed storage, arose from the research community. But because today's clouds have particular, non-malleable implementations of these ideas "baked in," they are unsuitable as facilities in which to conduct research on future cloud architectures. This project creates CloudLab, a facility that will enable fundamental advances in cloud architecture. CloudLab will not be a cloud; CloudLab will be large-scale, distributed scientific infrastructure on top of which many different clouds can be built. It will support thousands of researchers and run hundreds of different, experimental clouds simultaneously. The Phase I CloudLab deployment will provide data centers at Clemson (with Dell equipment), Utah (HP), and Wisconsin (Cisco), with each industrial partner collaborating to explore next-generation ideas for cloud architectures CloudLab will be a place where researchers can try out ideas using any cloud software stack they can imagine. It will accomplish this by running at a layer below cloud infrastructure: it will provide isolated, bare-metal access to a set of resources that researchers can use to bring up their own clouds. These clouds may run instances of today's popular stacks, modest modifications to them, or something entirely new. CloudLab will not be tied to any particular particular cloud stack, and will support experimentation on multiple in parallel. The impact of cloud computing outside the field of computer science has been substantial: it has enabled a new generation of applications and services with direct impacts on society at large. CloudLab is positioned to have an immediate and substantial impact on the research community by providing access to the resources it needs to shape the future of clouds. Cloud architecture research, enabled by CloudLab, will empower a new generation of applications and services which will bring direct benefit to the public in areas of national priority such as medicine, smart grids, and natural disaster early warning and response.