Big Data Synchronization among Isolated Data Servers in Disaster

When a large-scale disaster happens, efficient network connection and communication becomes difficult due to serious damage of existing network infrastructures. Meantime, people have strong demands of information sharing with each other for evacuation and disaster-relief activities in such a disaster environment. To serve these heavy communication demands, establishing local area networks (LANs) consisting of portable servers has been considered as one of the most promising solutions. Based on the established LANs, people can share disaster-related information in covered area. However, due to the lack of stable Internet connection, these LANs are isolated and cannot be synchronized in real time. To tackle this problem, in this paper, we propose an intermittent data synchronization scheme by introducing moving vehicles as relays to exchange data between isolated data servers after disasters. With the objective of maximizing the synchronized weighted data volume under the capability constraints of the mobile relay, we formulate a stochastic programming problem for trajectory planning. We leverage queueing theory and the Lyapunov-drift technique to solve this problem in an online setting, which is practical for a real disaster environment. Our theoretical analysis shows that the performance gap of our proposed online algorithm is (1/V) of the optimum. Additionally, extensive simulations and comparisons with other algorithms are conducted to show the superior performance of our proposed online algorithm.