ToleranceZone - A Fault Tolerant Middleware Idioms based on Self-Stabilizing Techniques

自稳定算法(SSA)具有返回到稳定状态的固有属性，即使在发生非预期的错误时也是如此。这种隐式的健壮性对于容易出错的无线网络肯定是有益的。然而，这是有代价的：融合时间是不可预测的，甚至可能是无限的。此外，邻居节点需要定期交换状态信息，这可能导致无法容忍的能量消耗。容忍区项目研究如何实现无线自组织网络的自稳定通信和中间件平台。了解邻居之间的状态交换率与收敛时间之间的关系尤为重要。具体来说，我们想知道的是，自稳定算法(SSA)是否能够在合理的时间内以合理的能量消耗来应对典型的网络拓扑变化。我们的研究始于以自稳定的方式实现常见的通信模式，并通过真实网络的实验来评估我们的方法。我们能够证明，SSA在原理上是有效的，但我们发现了难以解释和难以重现的现象。糟糕的重复性是我们面临的主要挑战。因此，我们开发了一种方法，允许我们将在真实实验中收集的拓扑数据输入到可重现的、确定性的模拟中。这使我们能够详细研究拓扑变化对SSA的影响以及交换频率对收敛时间的影响。我们已经可以表明SSA对拓扑变化的敏感度比预期的要高得多。邻域中的更改不会像预期的那样隐式修复，但可能会导致较高软件层上的显著不稳定，从而导致收敛时间较短或根本不收敛。事实上，不同层上的多个SSA往往相互冲突，就像相互冲突的控制循环一样。这些跨层效应有很大的影响，但到目前为止还没有人对这种现象进行研究。因此，我们将在项目的最后阶段重点了解这些相互依赖关系。基于这些见解，我们将推导出跨层反馈机制，使我们能够根据邻居感受到的稳定性来调整邻居汇率。我们将使用上述方法评估我们的方法，并在实际网络中进行实验。最后，我们将把结果与无线网络中健壮性的最新方法进行比较。

Self-Stabilizing Algorithms (SSAs) have the inherent property to return into a stable state even when non-anticipated errors occur. This implicit robustness is certainly beneficial for error prone wireless networks. However, it comes at a price: convergence time is not predictable and possibly unbounded. Furthermore, neighboring nodes need to exchange state information periodically, which may lead to intolerable energy consumption.The ToleranceZone project investigates ways to implement self-stabilizing communication and middleware platforms for wireless ad hoc networks. Understanding the relationship between the rate of state exchange among neighbors and the convergence time is particularly important. Specifically we would like to know, whether self-stabilizing algorithms (SSAs) can cope with typical network topology changes in a reasonable time with a reasonable energy consumption.We started our research by implementing common communication patterns in a self-stabilizing way and evaluated our approach in experiments with real networks. We were able to show, that SSAs work in principle but we detected phenomenons that were both hard to explain and hard to reproduce. The bad reproducibility turned out to be our major challenge. Therefore we developed a methodology that allows us to feed topology data collected in real experiments into reproducible, deterministic simulations. This allows us to study the impact of topology changes on our SSAs as well as the impact of the exchange frequency to convergence time in detail.We could already show that SSAs are much more sensitive to topology changes than anticipated. Changes in the neighborhood are not implicitly repaired as expected, but may lead to significant instability on higher software layers resulting in either poor convergence time or no convergence at all. In fact, multiple SSAs on different layers tend to work against each other like contradicting control loops. These cross layer effects have a significant impact, but so far this phenomenon has not been studied. Therefore we will focus in the final phase of our project on understanding these interdependencies. Based on those insights we will derive cross layer feedback mechanisms that allow us to adapt the neighbor exchange rate to the perceived stability in the neighborhood. We will evaluate our approach with the aforementioned methodology as well as experiment with real networks. Finally we will compare the results against state of the art approaches to robustness in wireless networks.