A Research Overview and Evaluation of Performance Metrics for Self-Organization Algorithms

2015 IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops Pub Date : 2015-09-21 DOI:10.1109/SASOW.2015.25

Benedikt Eberhardinger, Gerrit Anders, H. Seebach, Florian Siefert, W. Reif

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引用次数: 14

Abstract

Self-organization (SO) algorithms are supposed to restructure and reconfigure the system at run-time in order to empower it to fulfill its requirements under uncertain environmental conditions. For this purpose, information about the state of the environment and the system is used in feedback loops to establish a flexible, powerful system. Consequently, the performance of the SO algorithms has a significant effect on the overall performance of the system. Indeed, it is hard to design high-performing SO algorithms, because the environmental conditions the system has to operate in are partially unpredictable at design time. A crucial aid for the development of SO algorithms are tools that enable the evaluation of the algorithms' performance at design time. These tools could also be used to select the best-fitting algorithm and parametrization for a specific application, among others. We show how existing performance metrics can be applied to SO algorithms by evaluating different partition-based algorithms. Based on these results, we discuss the advantages and limitations of the existing metrics and deduce requirements for performance metrics for SO algorithms.

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自组织算法性能指标的研究综述与评价

自组织(SO)算法被认为是在运行时重构和重新配置系统，以使系统能够在不确定的环境条件下满足其需求。为此，在反馈回路中使用关于环境和系统状态的信息来建立一个灵活、强大的系统。因此，SO算法的性能对系统的整体性能有着重要的影响。事实上，设计高性能SO算法是很困难的，因为系统运行的环境条件在设计时部分是不可预测的。SO算法开发的一个关键辅助工具是能够在设计时评估算法性能的工具。这些工具还可用于为特定应用选择最佳拟合算法和参数化等。我们通过评估不同的基于分区的算法来展示如何将现有的性能指标应用于SO算法。基于这些结果，我们讨论了现有度量的优点和局限性，并推导了SO算法对性能度量的要求。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops

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