Biologically Inspired Self-Adaptation of Mobile Agent Population

Mobile agent is one of the most promising paradigms to support autonomic computing in a large scale of distributed system with 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 its application. In this paper, we consider the mobile agent population control problem in dynamic networks: it requires to adjust the number of agents to a constant fraction of the current network size. We propose algorithms inspired by a biological paradigm and show by simulations that the proposed algorithms realize self-adaptation of mobile agent population in dynamic networks