🤖 AI Summary
Fog computing environments suffer from frequent dynamic failures due to high device autonomy and low specialization, rendering existing fault-tolerance mechanisms inadequate in coping with strong uncertainty and high system dynamics. To address this, this paper introduces fuzzy logic—novelly applied to fog fault handling—for the first time. We propose a synergistic fault-tolerance framework integrating task checkpointing, dynamic migration, and replication, enabling adaptive responses to uncertain factors such as failure probability and resource availability. The resulting robust scheduling strategy is evaluated via simulations driven by real-world failure traces. Experimental results demonstrate substantial improvements over state-of-the-art approaches: average task latency decreases by 56%, and total processing time is reduced by 48%. These gains significantly enhance both reliability and execution efficiency for real-time IoT applications in fog environments.
📝 Abstract
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.
