Mariusz Jacyno

Self-organizing agent communities for autonomic resource management

The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the runtime reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions. In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximized when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organizes into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimize any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes.

Autonomic Resource Management through Self-Organising Agent Communities

In this paper we describe a model of a decentralised system in which there may be no global information repository and the demand for particular resources changes over time. Agents must therefore organise the information that is available to them locally in order to adaptively respond to changes in demand in an efficient manner. Where previous studies have explored the role of system heterogeneity brought about by limiting knowledge or using decision procedures that diversify agent behaviour, here we focus specifically on how varying the amount of information available to agents affects the flow of information. By conducting a thorough evaluation of our model, the results demonstrated that when agents possessed only a limited awareness of their peers within their neighborhood, they exhibited self-organising behaviour resulting in the emergence of community structures that support locally shared information. By limiting the quantity of information shared by constraining an agents memory size, we found that a stable local community behavior emerged that was robust and efficient, and also adaptive when the whole agent population was exposed to global fluctuations in service demand.