Comparison of Approaches for Self-Improvement in Self-Adaptive Systems
Comparison of Approaches for Self-Improvement in Self-Adaptive Systems
复制标题
自适应系统自我改进方法的比较
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
C. Becker
中科院分区：
文献类型：
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作者：
Christian Krupitzer;F. Roth;Martin Pfannemüller;C. Becker
设备移动性、云计算或网络物理系统等各种趋势导致了更高程度的分布。这些系统的系统需要整合。各种子系统的整合仍然是一个挑战。自适应系统中的自我改进可以帮助将集成任务从静态设计时间转移到运行时，这符合这些系统的动态需求。因此，它可以在运行时实现系统部件的集成。在本文中，我们将自我改进定义为自主计算系统的适应逻辑的适应。我们提出了一个自我改进的方法在自治计算和自适应系统的领域的概述。基于自适应的分类，我们比较的方法和分类。分类结果表明，这些方法要么侧重于结构自适应，要么侧重于参数自适应，但很少联合收割机两者结合。分类的基础上，我们阐述的挑战，需要解决的未来的方法提供自我完善的系统集成在运行时。
Various trends such as mobility of devices, Cloud Computing, or Cyber-Physical Systems lead to a higher degree of distribution. These systems-of-systems need to be integrated. The integration of various subsystems still remains a challenge. Self-improvement within self-adaptive systems can help to shift integration tasks from the static design time to the runtime, which fits the dynamic needs of these systems. Thus, it can enable the integration of system parts at runtime. In this paper, we define self-improvement as an adaptation of an Autonomic Computing system's adaptation logic. We present an overview of approaches for self-improvement in the domains of Autonomic Computing and self-adaptive systems. Based on a taxonomy for self-adaptation, we compare the approaches and categorize them. The categorization shows that the approaches focus either on structural or parameter adaptation but seldomly combine both. Based on the categorization, we elaborate challenges, that need to be addressed by future approaches for offering self-improving system integration at runtime.