基于模糊逻辑的雾计算鲁棒故障处理机制
Fuzzy Logic-based Robust Failure Handling Mechanism for Fog Computing
AI总结：
针对雾计算设备故障率高且现有方法不适配动态环境的问题，本文提出融合模糊逻辑检查点、任务迁移与任务复制的组合故障处理机制，并基于真实故障轨迹验证其将平均延迟和总处理时间分别降低56%和48%。
摘要：
雾计算是一种新兴的计算范式，主要适用于时间敏感和实时的物联网（IoT）应用。学术界和工业界正致力于探索雾计算各个方面的特性以推动市场采用。雾计算范式的核心思想是利用我们周围各种手持设备、移动设备、固定设备和网络设备的空闲计算资源，为雾-IoT环境中的应用请求提供服务。雾环境中的设备是自治的，并非专门用于雾应用处理。因此，与其他分布式计算范式相比，雾环境中设备发生故障的概率较高。解决雾中的故障问题至关重要，因为只有妥善处理故障，才能确保应用成功执行。为了处理故障，文献中已有多种技术，例如检查点设置和任务迁移，这些技术在主要处理静态或事务性数据的基于云的企业应用中均表现良好。但这些故障处理方法不适用于高度动态的雾环境。相比之下，本工作聚焦于解决雾环境中应用故障管理问题，提出了一种组合解决方案（将基于模糊逻辑的任务检查点设置和任务迁移技术与任务复制相结合）用于故障处理并生成鲁棒的调度方案。我们使用真实故障轨迹，从应用执行时间、延迟和成本方面对所提方法进行了评估。与现有故障处理方法相比，所提解决方案的平均延迟和总处理时间分别平均改善了56%和48%。
英文摘要：
Fog computing is an emerging computing paradigm which is mainly suitable for time-sensitive and real-time Internet of Things (IoT) applications. Academia and industries are focusing on the exploration of various aspects of Fog computing for market adoption. The key idea of the Fog computing paradigm is to use idle computation resources of various handheld, mobile, stationery and network devices around us, to serve the application requests in the Fog-IoT environment. The devices in the Fog environment are autonomous and not exclusively dedicated to Fog application processing. Due to that, the probability of device failure in the Fog environment is high compared with other distributed computing paradigms. Solving failure issues in Fog is crucial because successful application execution can only be ensured if failure can be handled carefully. To handle failure, there are several techniques available in the literature, such as checkpointing and task migration, each of which works well in cloud based enterprise applications that mostly deals with static or transactional data. These failure handling methods are not applicable to highly dynamic Fog environment. In contrast, this work focuses on solving the problem of managing application failure in the Fog environment by proposing a composite solution (combining fuzzy logic-based task checkpointing and task migration techniques with task replication) for failure handling and generating a robust schedule. We evaluated the proposed methods using real failure traces in terms of application execution time, delay and cost. Average delay and total processing time improved by 56% and 48% respectively, on an average for the proposed solution, compared with the existing failure handling approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
