Increasing the stability of an Artificial Hormone System for task allocation by accelerator bounds

The Artificial Hormone System (AHS) is a completely decentralized operation principle for a middleware which can be used to allocate tasks in a system of heterogeneous processing elements (PEs) or cores. Tasks are scheduled according to their suitability for the heterogeneous PEs, the current PE load and task relationships. The AHS also provides properties like self-configuration, self-optimization and self-healing by task allocation. The AHS is able to guarantee real-time bounds for such self-X-properties. Clustering of related tasks is done by the AHS through the emission of accelerator hormones, which attract related tasks to neighboring PEs. However, accelerators may increase the task load of PEs and even cause instability. In this paper we present two new approaches to eliminate the destabilizing effect of accelerators but keeping their property to attract related tasks. The accelerator threshold approach and the accelerator saturation approach introduce two different kinds of accelerator bounds. A theoretical analysis and a practical evaluation show the effectiveness and the different properties of both approaches.