Volume 122, Number 3, May 2018
|Number of page(s)||6|
|Section||Condensed Matter: Structural, Mechanical and Thermal Properties|
|Published online||22 June 2018|
Design of survivable networks in the presence of aging
1 School of Reliability and Systems Engineering, Beihang University - Beijing, 100191, China
2 Science and Technology on Reliability and Environmental Engineering Laboratory - Beijing, 100191, China
3 Department of Computer Science, University of California - Davis, CA, 95616, USA
4 Department of Physics, Bar-Ilan University - Ramat-Gan 5290002, Israel
5 Department of Mathematics, Bar-Ilan University - Ramat-Gan 5290002, Israel
Received: 16 April 2018
Accepted: 31 May 2018
Networks are designed to satisfy given objectives under specific requirements. While the static connectivity of networks is normally analyzed and corresponding design principles for static robustness are proposed, the challenge still remains of how to design survivable networks that maintain the required level of connectivity during their whole lifespan, against component aging. We introduce network survivability as a new concept to evaluate the networks overall performance during their whole lifespan, considering both network connectivity and network duration. We develop a framework for designing a survivable network by allocating the expected lifetimes of its components, given a limited budget. Based on percolation theory and simulation, we find that the maximal network survivability can be achieved with a quantitative balance between network duration and connectivity. For different survivability requirements, we find that the optimal design can be separated into two categories: strong dependence of lifetime on node's degree leads to larger network lifetime, while weak dependence generates stronger network connectivity. Our findings could help network design, by providing a quantitative prediction of network survivability based on network topology.
PACS: 64.60.aq – Networks / 64.60.ah – Percolation / 89.75.Fb – Structures and organization in complex systems
© EPLA, 2018
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