Adaptive Services in a Distributed Environment

Benkô, Borbála Katalin; Höfig, Edzard; Brgulja, Nermin; Kusber, Rico

doi:10.1109/aswn.2008.8

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Nevertheless, none of the frameworks we analyzed offered a built-in functionality for making meaningful and resource efficient deployment decisions. They rather provide the ground for applying our DDM approach by claiming to enable the incorporation of legacy code [11].…”

Section: Groundworkmentioning

confidence: 99%

“…Up until now, various frameworks have been developed which should help to implement software components and to equip them with intelligent abilities [11], [12], [13]. Those frameworks usually intend to enable autonomic behavior or fulfill a subset of self-* features.…”

Section: Groundworkmentioning

confidence: 99%

“…Starting with the initial idea of applying Artificial Intelligence techniques to achieve self-management, the related scientific community defined a clear terminology of what autonomic computing means and investigated numerous research questions that arose [2], [3]. Today, a variety of frameworks [11], [12], [13] is available to enable the creation of several autonomic features. Mechanisms for assessing the degree of autonomicity of software under well-defined circumstances are being developed [14].…”

Section: Introductionmentioning

confidence: 99%

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Parameter Selection for Autonomic Deployment Decision Making

Kusber

David

Brgulja

2009

2009 IEEE Globecom Workshops

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Today's computing networks are built to have a very high degree of heterogeneity, complexity, and interrelation. To be able to interact efficiently with those networks, scientists and computing system developers equip soft-and hardware with the ability to solve subtasks in an autonomic fashion. Especially with regard to easily accessible, content-rich, and increasingly pervasive networks, the question arises how best to select among multiple alternatives to deploy services or to obtain data in an efficient and preferably autonomic way. In this paper we address this question and present a deployment decision making (DDM) system accordingly. In particular, we concentrate on the performance-optimizing effects of selecting parameters and parameter subsets for the decision making process. These effects are deduced analytically and illustrated by an experimental evaluation.

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Section: Groundworkmentioning

confidence: 99%

Section: Groundworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parameter Selection for Autonomic Deployment Decision Making

Kusber

David

Brgulja

2009

2009 IEEE Globecom Workshops

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“…In other words, preconceptions that make the adaptation fast and effective in some cases form an impenetrable obstacle in others. The adaptation potential of the system is highly restricted by these hard preconceptions [3].…”

Section: Introductionmentioning

confidence: 99%

An open learning system

Benkô¹,

Lányi²

2011

Per. Pol. Elec. Eng.

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In this paper we discuss an approach for achieving high adaptivity in complex dynamic environments through learning. Unlike in traditional approaches, where adaptive systems need to be strongly equipped with knowledge about their environment and possible adaptation strategies, we propose a direction where the level of original preconceptions is kept low. In our model, both the knowledge and the strategies emerge dynamically and flexibly, during the system's normal operation. The proposed model also includes a mechanism that systematically replaces old preconceptions with more accurate ones (e.g. to observe new features). The proposed principles were evaluated in a theoretical world via simulation; where the adaptive system was challenged to win an amoeba game against opponents with different strategies, and changing game rules (3-7 long series required to win).

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