- Information theory provides a general framework in which the fundamental limits of information systems (wired and wireless networks, cryptographic systems, data compression and storage systems, and others) can be meaningfully investigated. The investigation of such limits is traditionally conducted analytically, requiring in many cases substantial ingenuity. As modern information systems become more and more complex, such an approach becomes rather unwieldy. The main goal of this project is to build upon initial successes in applying a computational approach to characterize the fundamental limits of certain distributed storage systems, and to further understand what it takes for the proposed computational approach to be successful in the broad setting of big-data infrastructure.The proposed computational approach is built upon the vantage point that the overall process of identifying and proving fundamental limits can be alternatively viewed as an optimization problem under the specific constraints of the system at hand and the general constraints of information measures. The difficulty lies in the fact that such optimization programs are usually very large for any practically relevant problems, and numerical results are difficult to interpret. This project pursues three major thrusts: 1) finding more efficient problem representations using symmetry and other constraints; 2) improving the optimization algorithms using domain knowledge; and 3) finding intelligent interpretations of the computed results through duality to facilitate engineering design. The results are expected to have measurable impacts on the following two disciplines: 1) Big-data infrastructure: The ongoing big-data movement calls for information systems with diverse reliability and functionality requirements. By identifying the fundamental limits of such systems, accurate performance evaluations and meaningful design guidelines may be assessed; and 2) Information theory: The proposed study will bridge the disciplines of computational optimization and information theory, and instill more computerized intelligence into information theory research.