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.

中文翻译：

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多队列雾系统中的概率截止日期感知应用程序卸载：最大熵框架

摘要

云计算及其衍生产品，例如雾计算和边缘计算，推动了物联网时代的发展，将人工智能和深度学习集成到流程自动化中。尽管医疗保健、教育和自动化领域发生了变革性增长，但挑战仍然存在，特别是在解决多跳公共网络对数据上传时间的影响、影响响应时间、故障率和安全性方面。现有的调度算法是针对截止时间、优先级、到达率和到达模式等多个参数而设计的，可以最大限度地减少高优先级应用程序的执行时间。然而，困难在于同时最小化整体应用程序执行时间，同时减轻低优先级应用程序的资源耗尽问题。本文介绍了一种基于云雾的计算架构，结合联合概率、丢失概率和最大熵概念来解决雾节点资源匮乏问题。该模型利用概率应用程序调度算法，考虑优先级和截止日期，并采用预期丢失概率进行任务卸载。此外，第二种算法侧重于资源匮乏，优化任务序列以最小化响应时间并提高多队列雾系统中的服务质量。该论文表明，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。