Fuzzy Logic-based Robust Failure Handling Mechanism for Fog Computing

TL;DR

Fog computing faces high resource churn and failures that threaten time-sensitive IoT workloads. The authors introduce a fuzzy-logic-based composite failure-handling mechanism (FLBFH) that combines proactive live migration, reactive checkpointing, and replication, guided by MRP and CPMNR scores and a mapping module to decide migration, checkpointing, replication, or recovery. Evaluations on LANL failure traces show improvements of $\56\%$ in average delay and $\48\%$ in total processing time over baselines, illustrating robust scheduling under dynamic Fog conditions. The work provides a practical, cost-efficient framework for fault-tolerant Fog-IoT scheduling with potential deployment in edge-cloud ecosystems.

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.

Figures (8)

• Figure 1: Proposed failure handling mechanism.
• Figure 2: Membership of different parameters for unpredicted failure.
• Figure 3: Membership for mrp score.
• Figure 4: Membership of different parameters for predicted failure and cpmnr score.
• Figure 5: Failure handling process in the proposed method.