Two‐level extensions of an artificial hormone system

Abstract

SummaryThe ARTIFICIAL HORMONE SYSTEM (AHS) is a decentralized software that is able to allocate tasks in a system of heterogeneous processing elements (PEs). Tasks are allocated 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 in the context of task allocation. In addition, it is able to guarantee real‐time bounds for such self‐X properties. However, using self‐organization principles introduces increased system complexity such as control of system parameters for self‐organization and additional communication effort. In this contribution, we address these problems by using two different two‐level extensions of the AHS: we consider the choice and control of hormone parameters by an observer/controller architecture as first extension of the AHS and a HIERARCHICAL AHS (HAHS) to save communication bandwidth as second extension of the AHS. For the first extension, we use a machine learning approach for gradually learning the hormone values of different tasks. This is a major advance because expert knowledge is needed to configure the AHS up to now. We present an observer/controller architecture as an extension of the AHS to monitor and control its behavior. The user has to provide a simple set of initial rules, and the observer/controller is able to generate new rules if needed. The evaluation of our approach using a benchmark (containing six different types of tasks) shows that the observer/controller is able to match the goals provided by the user, and we discuss it in detail. The second extension is the hierarchical AHS where the PEs of the system consist of several different clusters each of them having its own communication infrastructure, for example, a bus system. The HAHS is able to save communication effort because the broadcast communication of the hormones is limited to the clusters. Nevertheless, it provides the same self‐X properties as the AHS, even the time for self‐configuration is shorter than for the AHS. We present evaluations that show that the HAHS performs better for applications with large numbers of PEs than the AHS. Copyright © 2015 John Wiley & Sons, Ltd.