Biologically-inspired approaches are one of the most promising approaches to realize highly-adaptive distributed systems. Biological systems inherently have self-* properties, such as self-stabilization, self-adaptation, self-configuration, self-optimization and self-healing. Thus, the application of biological systems into distributed systems has attracted a lot of attention recently. In this paper, we present one successful result of bio-inspired approach: we propose distributed algorithms for resource replication inspired by the single species population model. Resource replication is a crucial technique for improving system performance of distributed applications with shared resources. In systems using resource replication, generally, a larger number of replicas lead to shorter time to reach a replica of a requested resource but consume more storage of the hosts. Therefore, it is indispensable to adjust the number of replicas appropriately for the resource sharing application. This paper considers the problem for controlling the densities of replicas adaptively in dynamic networks and proposes two bio-inspired distributed algorithms for the problem. In the first algorithm, we try to control the replica density for a single resource. However, in a system where multiple resources coexist, the algorithm needs high network cost and the exact knowledge at each node about all resources in the network. In the second algorithm, the densities of all resources are controlled by the single algorithm without high network cost and the exact knowledge about all resources. This paper shows by simulations that these two algorithms realize self-adaptation of the replica density in dynamic networks.

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.