ReMEC: Reliability-aware scheduling of mixed-criticality IoT tasks in DVFS-enabled Multi-tier Edge Computing

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-08-13 DOI:10.1016/j.future.2025.108074

Akhirul Islam , Suchetana Chakraborty , Manojit Ghose

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引用次数: 0

Abstract

The Internet of Things (IoT) has witnessed significant growth, driving innovation across a wide range of application domains. Many IoT applications are characterized by their high resource demands and stringent latency requirements. Multi-tier edge computing has emerged, addressing these needs, where the application is scheduled across IoT devices, edge servers, and the cloud. However, ensuring reliable application performance remains a key challenge, particularly in transient IoT device failures caused by electromagnetic interference, battery depletion, hardware failures, or software crashes. In this work, we consider task execution reliability by incorporating failure of the user device, while the previous work primarily focuses on server-side reliability and overlooks user-centric limitations. We also include the user budget constraint while enhancing the task execution reliability by task replication. Additionally, we consider mixed criticality tasks in our application model, reflecting real-world scenarios more accurately, an aspect largely overlooked in existing works. To achieve task execution reliability while ensuring user budget and task latency deadline, we introduce ReMEC, a fuzzy logic-based reliable hybrid task offloading framework that relies on a distributed message queuing strategy to preserve execution state during device failures, and a fixed-point iterative method for optimizing DVFS frequencies to improve energy efficiency without violating task deadlines or compromising reliability. Our comprehensive benchmarking, which rigorously compares ReMEC against two state-of-the-art strategies (RMEAC and FP-TOSM) and three baseline approaches (BR-greedy, LE-greedy, and Random-RR), demonstrates that ReMEC outperforms all of them, achieving average improvements of 26.19 % in latency, 31.49 % in energy consumption, and 72.16 % in application failure rate, thereby demonstrating its practical applicability in real-world IoT scenarios.

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ReMEC：支持dvfs的多层边缘计算中混合关键物联网任务的可靠性感知调度

物联网（IoT）见证了显着的增长，推动了广泛应用领域的创新。许多物联网应用的特点是高资源需求和严格的延迟要求。多层边缘计算已经出现，解决了这些需求，其中应用程序跨物联网设备、边缘服务器和云进行调度。然而，确保可靠的应用性能仍然是一个关键挑战，特别是在由电磁干扰、电池耗尽、硬件故障或软件崩溃引起的瞬态物联网设备故障中。在这项工作中，我们通过纳入用户设备的故障来考虑任务执行的可靠性，而以前的工作主要关注服务器端可靠性，而忽略了以用户为中心的限制。在通过任务复制增强任务执行可靠性的同时，我们还包含了用户预算约束。此外，我们在我们的应用模型中考虑了混合临界任务，更准确地反映了现实世界的场景，这在现有的工作中很大程度上被忽视了。为了在保证用户预算和任务延迟期限的同时实现任务执行可靠性，我们引入了基于模糊逻辑的可靠混合任务卸载框架ReMEC，该框架依赖于分布式消息队列策略在设备故障期间保持执行状态，并引入了优化DVFS频率的点迭代方法，在不违反任务期限或损害可靠性的情况下提高能源效率。我们的综合基准测试严格比较了ReMEC与两种最先进的策略（RMEAC和FP-TOSM）以及三种基线方法（BR-greedy， l- greedy和Random-RR），结果表明，ReMEC优于所有这些方法，延迟平均改善26.19%，能耗平均改善31.49%，应用故障率平均改善72.16%，从而证明了其在现实物联网场景中的实际适用性。

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.