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In Organic Computing (OC) applications, we often face mutual influences between the entities of the system. These influences can be either explicit, i.e., directly visible for the designer, or implicit, i.e., they are not visible on first sight. In previous work, we developed a methodology to make these implicit influences measurable. In this work, we present a taxonomy that classifies OC applications regarding their nature of influence within the system. This taxonomy is helpful for the selection of suitable methods for the detection of hidden mutual influences.
This article presents novel approaches to automatically learn the best combination of forecasts computed by several individual forecast methods. Ideas from the machine learning domain, such as Artificial Neural Networks and Learning Classifier Systems are adapted for this task. The combined forecast serves as basis for a pro-active adaptation of the control strategy in Organic Traffic Control (OTC). OTC is a decentralised, self-organised urban traffic control system that has the ability to optimise the signalisation, to establish progressive signal systems, and to offer route guidance recommendations. Besides analysing the success of the prediction strategy, we demonstrate the positive effect for OTC in terms of a simulation-based evaluation of an urban area situated in Hamburg, Germany. It reflects the actual topology, traffic data from a census, and the actual control strategy performed as reference. As a result, important figures, such as the average waiting times at red lights and the emission values can be decreased significantly. Our findings support the hypothesis that the use of forecasts is beneficial for traffic control.
This report aims at investigating forecast-based control of Organic Computing (OC) systems, especially the Organic Traffic Control (OTC) system. OTC is a self-organising traffic management system for urban road networks.
Making forecasts of future system states can make complex technical systems more robust against failures. We present concepts for the creation of forecasts at runtime and how these forecasts can be integrated in OC systems and OTC, and discuss how this can lead to higher resilience.
The Trusted Desktop Grid (TDG) is a self-organised, agent-based organisation, where agents perform computational tasks for others to increase their performance. In order to establish a fair distribution and provide counter-measures against egoistic or malicious elements, technical trust is used. A fully self-organised approach can run into disturbed states such as a trust breakdown of the system that lead to unsatisfying system performance although the majority of participants is still behaving well. We previously introduced an additional system-wide control loop to detect and alleviate disturbed situations. Therefore, we describe an Observer/Controller loop at system level that monitors the system status and intervenes if necessary. This paper focuses on the controller part which instantiates norms as reaction to observed suspicious situations. We demonstrate the benefit of our approach within a Repast-based simulation of the TDG. Therein, the impact of disturbances on the system performance is decreased significantly and the time to recover is shortened.
Grid Computing Systems are examples for open systems with heterogeneous and potentially malicious entities. Such systems can be controlled by system-wide intelligent control mechanisms working on trust relationships between these entities. Trust relationships are based on ratings among individual entities and represent system-wide information. In this paper, we propose to utilise a normative approach for the system-level control loop working on basis of these trust values. Thereby, a normative approach does not interfere with the entities’ autonomy and handles each system as black box. Implicit rules already existing in the system are turned into explicit norms – which in turn are becoming mandatory for all entities. This allows the distributed systems to derive the desired behaviour and cooperate in reaction to disturbed situations such as attacks.
The utilisation of cell phone networks increases continuously, especially driven by the introduction of new mobile services and smart phones. Network operators can follow two directions to deal with the problem: either install new hardware or increase the efficiency of the existing infrastructure. This paper presents a novel algorithm to improve the efficiency of current networks by allowing for a self-organised load-dependent reconfiguration of antennas. The algorithm is capable of identifying hotspot traffic, assigning this to a neighbouring cell, and learning the best strategy at runtime. This leads to a self-improving intelligent control mechanism. The simulation-based evaluation results demonstrate the potential benefit, while simultaneously keeping the hardware’s deterioration at a comparable level.
Desktop Computing Grids provide a framework for joining in and sharing resources with others. The result is a self-organised system that typically consists of numerous distributed autonomous entities. Openness and heterogeneity postulate severe challenges to the overall system’s stability and efficiency since uncooperative and even malicious participants are free to join. In this paper, we present a concept for identifying agents with exploitation strategies that works on a system-wide analysis of trust and work relationships. Afterwards, we introduce a system-wide control loop to isolate these malicious elements using a norm-based approach – due to the agents’ autonomy, we have to build on indirect control actions. Within simulations of a Desktop Computing Grid scenario, we show that the intelligent control loop works highly successful: these malicious elements are identified and isolated with a low error rate. We further demonstrate that the approach results in a significant increa se of utility for all participating benevolent agents.
