Self-adaptive mobile agent population control in dynamic networks based on the single species population model

Mobile-agent-based distributed computing is one of the most promising paradigms to support autonomic computing in a large-scale of distributed system with dynamics and diversity: mobile agents traverse the distributed system and carry out a sophisticated task at each node adaptively. In mobile-agent-based systems, a larger number of agents generally require shorter time to complete the whole task but consume more resources (e.g., processing power and network bandwidth). Therefore, it is indispensable to keep an appropriate number of agents for the application on the mobile-agent-based system. This paper considers the mobile agent population control problem in dynamic networks: it requires adjusting the number of agents to a constant fraction of the current network size. This paper proposes algorithms inspired by the single species population model, which is a well-known population ecology model. These two algorithms are different in knowledge of networks each node requires. The first algorithm requires global information at each node, while the second algorithm requires only the local information. This paper shows by simulations that the both algorithms realize self-adaptation of mobile agent population in dynamic networks, but the second algorithm attains slightly lower accuracy than the first one.