Task allocation in organic computing systems: networks with reconfigurable helper units

In this paper, computing systems that have no central control and consist of many partially autonomous components are studied. Two types of components are distinguished which are called workers and helpers respectively. The components are connected via a network and the helper components perform service tasks for the worker components. It is assumed that the helper components exhibit reconfigurable hardware to be able to execute different service tasks efficiently. The problem addressed in this paper is how to organise such a decentralised system in a way that requests of the workers are executed by suitable helpers and the total reconfiguration costs of the helpers are small. Several decentralised task allocation methods are proposed. One of them uses a combination of a fully decentralised dynamic clustering algorithm and a self-organised task allocation system. The clustering algorithm is used to classify the service requests that are sent through the network in order to give the helpers hints which requests are suitable to be executed by them. Simulations are done for static and dynamic scenarios to investigate the reconfiguration costs and the number of dropped requests, i.e., requests that could not be satisfied. The results show that the clustering-based system has a strong adaptive behaviour and that the decentralised clustering is able to reduce the reconfiguration costs significantly.