Noshape: A Bio-Inspired Model for Metaphorically Modeling Complex Agent Organizations as Amoebas

Noshape: A Bio-Inspired Model for Metaphorically Modeling Complex Agent Organizations as Amoebas

Hosny Abbas (Assiut University, Assuit, Egypt) and Samir Shaheen (Cairo University, Giza, Egypt)
DOI: 10.4018/IJOCI.2019010102

Abstract

This article presents a bio-inspired paradigm for metaphorically modeling agent organizations as adaptive virtual amoebas for the development of large-scale complex multi-agent systems. The presented model is called Noshape inspired from the amoeba, which is a unicellular micro-organism that does not have a definite shape. This article aims to test the performance of Noshape MAS with applications contain higher numbers of agents up to 8000 agents; this number of agents is very huge compared to the current state of the practice of MAS. The performance evaluation results show that Noshape MAS have better long-term performance in terms of service response time compared to present organizational approaches (i.e., federation). In Noshape MAS, the response times of remote agents' interactions will seem to be as those of local interactions thanks to the transparently provided dynamic adaptation behavior which arises from the dynamic overlapping of agent organizations. Further research is recommended to give the focus to performance, resiliency, security, and agent mobility within Noshape MAS.
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1. Introduction

Scale is the new frontier (Northrop et al., 2006) As humanity goes online, it's becoming an extremely advanced, large-scale processing unit (Von Ahn, Blum, Hopper, & Langford, 2003). The complexity of the future real-world applications comes from variety of aspects such as the large number of components they contain, their distributed knowledge and control, their open and heterogeneous nature, their dynamic and uncertain work environments (Gasser, 2001; Serugendo, Gleizes, and Karageorgos, 2011; Gleizes, Camps, George, & Capera, 2008). Large-scale systems are becoming crucial for future computing applications because of the increased evolution of modern civilization and the forcing trend towards globalization, decentralization, international business competition, and cyber connectedness. That in turn would lead to an observable increase in the heterogeneity, openness, and complexity of modern software applications which have reached a point that imposes new demands on their development technologies and approaches. From the other hand and as scale changes everything (Easterbrook, 2007), new software engineering approaches are critically needed, where concepts such as decomposition, autonomy, modularity, and adaptivity, scalability, and many other quality attributes can be collectively combined in one application (Frei & Di Marzo Sergendo, 2011). Existing software development approaches and techniques can’t manage the complex interactions and emergent behaviors usually exist in complex systems where the interactions between system components may occur at unpredictable times, for unpredictable reasons, and between unpredictable components (Zhang & Zhang, 2004).

One of the promising software engineering paradigms is Multi-Agent Systems (MAS) (Wooldridge & Jennings, 1995; Lind, 2001), which currently represent a mainstream engineering approach for developing complex real-life applications. MAS have been adopted and used in a wide range of application domains such as traffic control, manufacturing, power systems, factory automation, e-commerce, healthcare, etc. They model a certain application as a number (few or more) of autonomous agents cooperatively interact with each other and reactively act upon their environment. The key properties of modern complex distributed applications are mainly related to quality attributes such as autonomy, adaptivity, robustness, flexibility, scalability and so on, that is why the MAS technology seems to be the adequate paradigm for engineering them because these properties are exactly the properties that have been agreed to characterize them (Weyns, Helleboogh, & Holvoet, 2009). However, despite their adequateness for developing highly distributed, large-scale, complex, and open applications, the agent-based approach is currently used with small or at most medium-scale (hundreds of agents) applications and using it with complex and large-scale applications comprise large numbers of agents is still limited (Wijngaards, Van Steen, & Brazier, 2001). The reason might be related to the limited mechanisms normally used for agents’ coordination and location. In other words, the currently adopted coordination and location mechanisms can’t support MAS with higher numbers of agents (thousands or millions) distributed at large distances.

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