Self-Adaptive Systems in Organic Computing: Strategies for Self-Improvement

TL;DR

This paper explores self-adaptive systems in Organic Computing, focusing on their structure and four strategies for self-improvement that enable systems to adapt and enhance themselves during run-time.

Contribution

It introduces four novel strategies for self-improvement in self-adaptive systems within the Organic Computing framework.

Findings

Four strategies for self-improvement are proposed.

Self-adaptive systems can enhance their adaptation logic during run-time.

The structure of self-adaptive systems is analyzed from an Organic Computing perspective.

Abstract

With the intensified use of intelligent things, the demands on the technological systems are increasing permanently. A possible approach to meet the continuously changing challenges is to shift the system integration from design to run-time by using adaptive systems. Diverse adaptivity properties, so-called self-* properties, form the basis of these systems and one of the properties is self-improvement. It describes the ability of a system not only to adapt to a changing environment according to a predefined model, but also the capability to adapt the adaptation logic of the whole system. In this paper, a closer look is taken at the structure of self-adaptive systems. Additionally, the systems' ability to improve themselves during run-time is described from the perspective of Organic Computing. Furthermore, four different strategies for self-improvement are presented, following the taxonomy of self-adaptation suggested by Christian Krupitzer et al.