Support Vector Machine (SVM) has good performance in dealing with dimensionality disaster, over fitting and nonlinearity, which other traditional situation assessment algorithms does not have. However SVM has low efficiency when dealing with large-scale data. To effectively confront the challenge of handling big data, a MapReduce-based SVM (MR-SVM) situation assessment algorithm was proposed. Considering the characteristics of SVM algorithm, the parallelization and parameter selection of SVM based on MapReduce programming was implemented by designing procedures of map function and reduce function. The performances of MR-SVM and SVM were compared on Hadoop platform, MR-SVM had lower efficiency than SVM when dealing with small-scale data, but much better performance when dealing with large-scale data. SVM had an exponential growth on training time with the growth of data scalability while MR-SVM has slow growth. The experiment results show that MR-SVM solves the problem of data scalability, therefore it is suitable for situation assessment in big data environment.

In mobile agent systems, in order to make mobile agents(MA) cooperate well, and to locate mobile agents quickly, it is necessary to design an efficient and reliable MA locating protocol. It should meet the requirements proposed by traditional distributed systems, like transparency, scalability and efficiency, and maintain the consistency of the information concerning the MA environment. A new MA locating protocol called DRSP(Distributed Registration Search Protocol) was proposed to meet all the requirements above, and the specific details in DRSP were described. Its consistency was proved by a search program.