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

自稳定算法（SSA）具有即使在非预期错误发生时也能返回到稳定状态的固有属性。这种隐含的鲁棒性对于易于出错的无线网络当然是有益的。然而，这是有代价的：收敛时间是不可预测的，可能是无限的。此外，相邻节点需要定期交换状态信息，这可能会导致无法容忍的能源consumption.The ToleranceZone项目研究如何实现自稳定的无线ad hoc网络的通信和中间件平台。理解邻居之间的状态交换速率与收敛时间之间的关系尤为重要。具体来说，我们想知道，自稳定算法（SSA）是否可以科普典型的网络拓扑结构的变化，在合理的时间与合理的能源consumption.We开始我们的研究，通过实现常见的通信模式，在自稳定的方式和评估我们的方法在实验中与真实的网络。我们能够证明，SSA原则上起作用，但我们发现了难以解释和难以重现的现象。事实证明，可重复性差是我们面临的主要挑战。因此，我们开发了一种方法，使我们能够饲料拓扑数据收集到的真实的实验到可重复的，确定性的模拟。这使我们能够研究拓扑变化对SSA的影响，以及交换频率对收敛时间的影响，我们已经可以证明SSA对拓扑变化的敏感性比预期的要高得多。邻域中的变化不会像预期的那样隐式修复，但可能会导致更高软件层的显著不稳定性，从而导致收敛时间差或根本不收敛。事实上，不同层上的多个SSA往往会像矛盾的控制循环一样相互对抗。这些跨层效应具有显著的影响，但到目前为止尚未对这种现象进行研究。因此，我们将在项目的最后阶段集中精力了解这些相互依赖性。基于这些见解，我们将得出跨层反馈机制，使我们能够适应邻居的汇率在附近的感知稳定。我们将使用上述方法评估我们的方法，并使用真实的网络进行实验。最后，我们将比较结果对最先进的方法在无线网络中的鲁棒性。

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.