Genetic algorithms for finding optimal locations of mobile agents in scalable active networks

The idea of active networks has been emerged in recent years to increase the processing power inside the network. The intermediate nodes such as routers will be able to host mobile agents and many management tasks can be handled using autonomous mobile agents inside the network. One of the important limitations, which should be considered in active networks, is the restricted processing power of active nodes. In this paper, we define an optimal location problem for monitoring mobile agents in a scalable active network as a p-median problem, which is indeed a kind of facility location problem. The agents are responsible to monitor and manage the performance of all of the network nodes such that the total monitoring traffic overhead is minimized. Then we proposed two methods of finding an appropriate sub set of intermediate nodes for hosting mobile agents. In our first method, we have not considered the limited processing power of active nodes, which host mobile agents. In our second method, we have solved the problem so that the processing loads of host nodes do not exceed a predefined threshold. Since p-median problems are NP-complete and the search space of these problems is very large, our methods are based on genetic algorithms. We have tested our two methods for finding mobile agents optimal locations on four network topologies with different number of nodes and compared the obtained location. By this comparison, we have shown the importance of considering processing load limitation for active nodes as a parameter in choosing them as hosts of mobile agents in a scalable active network. The proposed locations in our second method eliminates the probability of CPU overload in the active nodes hosting the mobile agents and reduces the processing time required for finding the optimal locations of mobile agents.