This project builds a holistic model of reliability and performance for large-scale networking system (LSNS) and develops model-based self-improvement (MBSI) technology for high reliability, high performance, and smooth network communication in LSNS. This project addresses a number of problems, including the precise formulation of a holistic model of the LSNS reliability and performance, the analytical evaluation of the reliability and performance, the scheme to reduce the jams of network traffics, and further uses the self-improvement technology to automatically glean data, build models, evaluate designs, and optimize tasks. The methods, like graph theory, Bayesian approach, maximum entropy principle, universal generating function, and Monte Carlo simulation, are mainly adopted in modeling and evaluating the LSNS, while autonomic computing technologies are implemented for the self-improvement functions. As a result, this research further advances the theory, algorithm and technology in reliability, and fills the gap of reliability modeling and self-improvement in LSNS.
Expected results: (1) Novel formulations will be designed to effectively model a LSNS with all essential components; (2) An innovative HSA (Hybrid Stochastic Algorithm) will be developed to evaluate the task reliability and performance for LSNS; (3) New optimization schemes for LSNS will be designed; (4) A novel MBSI technology will be developed for self-improving the reliability, performance, and network communication; (5) Research outcomes will be applied in a variety of LSNSs, such as NASA's outer-space exploration, tele-medicines, grid computing, etc; (6) A set of software tools will be developed. (7) Research results will be disseminated through journal/conference publications and PIs' websites.
