A Probabilistic Deadline-aware Application Offloading in a Multi-Queueing Fog System: A Max Entropy Framework

IF 4.3 3区材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-02-22 DOI:10.1007/s10723-024-09753-7

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Abstract

Cloud computing and its derivatives, such as fog and edge computing, have propelled the IoT era, integrating AI and deep learning for process automation. Despite transformative growth in healthcare, education, and automation domains, challenges persist, particularly in addressing the impact of multi-hopping public networks on data upload time, affecting response time, failure rates, and security. Existing scheduling algorithms, designed for multiple parameters like deadline, priority, rate of arrival, and arrival pattern, can minimize execution time for high-priority applications. However, the difficulty lies in simultaneously minimizing overall application execution time while mitigating resource depletion issues for low-priority applications. This paper introduces a cloud-fog-based computing architecture to tackle fog node resource starvation, incorporating joint probability, loss probability, and maximum entropy concepts. The proposed model utilizes a probabilistic application scheduling algorithm, considering priority and deadline and employing expected loss probability for task offloading. Additionally, a second algorithm focuses on resource starvation, optimizing task sequence for minimal response time and improved quality of service in a multi-Queueing fog system. The paper demonstrates that the proposed model outperforms state-of-the-art models, achieving a 3.43-5.71% quality of service improvement and a 99.75-267.68 msec reduction in response time through efficient resource allocation.

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多队列雾系统中的概率截止时间感知应用卸载：最大熵框架

摘要云计算及其衍生产品（如雾计算和边缘计算）推动了物联网时代的到来，并将人工智能和深度学习整合到流程自动化中。尽管在医疗保健、教育和自动化领域取得了变革性增长，但挑战依然存在，特别是在解决多跳公共网络对数据上传时间的影响方面，影响响应时间、故障率和安全性。现有的调度算法针对截止日期、优先级、到达率和到达模式等多个参数进行设计，可以最大限度地缩短高优先级应用的执行时间。然而，难点在于如何在减少低优先级应用的资源耗尽问题的同时，最大限度地缩短整体应用的执行时间。本文结合联合概率、损失概率和最大熵概念，介绍了一种基于云雾的计算架构，以解决雾节点资源匮乏问题。建议的模型采用概率应用调度算法，考虑优先级和截止日期，并利用预期损失概率进行任务卸载。此外，第二种算法重点关注资源饥饿问题，优化任务序列，以实现最短响应时间，提高多队列雾系统的服务质量。论文表明，所提出的模型优于最先进的模型，通过有效的资源分配，服务质量提高了 3.43-5.71%，响应时间缩短了 99.75-267.68 毫秒。

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