雾计算和万物互联环境中的资源分配和任务调度:分类、回顾和未来方向

Bushra Jamil, H. Ijaz, M. Shojafar, K. Munir, R. Buyya
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引用次数: 44

摘要

在智能农业、智慧城市、大数据流等不同领域的应用开发中,万物互联(Internet of Everything)范式正被迅速采用。这些物联网应用程序正在利用云计算资源进行执行。雾计算作为云计算的延伸而出现,它支持移动性、异构性、地理分布、上下文感知以及附近雾节点上的存储、处理、网络和分析等服务。资源有限、异构、动态、不确定的雾环境使任务调度成为一个需要研究的巨大挑战。本文正是出于这一考虑,通过讨论雾计算和物联网环境中不同调度算法、重点优化指标和评估工具的优缺点,进行了系统、全面和详细的比较研究。这篇调查文章的目标有五个方面。首先,我们回顾了雾计算和IoE范式。其次,我们描述了雾计算和物联网环境下的优化度量。第三,我们通过一些例子回顾、分类和比较了处理雾计算和IoE环境范例的现有调度算法。第四,对调度算法进行了合理化,并指出了调查的经验教训。第五,讨论了在雾计算和物联网环境下改进调度的开放性问题和未来的研究方向。
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Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions
The Internet of Everything paradigm is being rapidly adopted in developing applications for different domains like smart agriculture, smart city, big data streaming, and so on. These IoE applications are leveraging cloud computing resources for execution. Fog computing, which emerged as an extension of cloud computing, supports mobility, heterogeneity, geographical distribution, context awareness, and services such as storage, processing, networking, and analytics on nearby fog nodes. The resource-limited, heterogeneous, dynamic, and uncertain fog environment makes task scheduling a great challenge that needs to be investigated. The article is motivated by this consideration and presents a systematic, comprehensive, and detailed comparative study by discussing the merits and demerits of different scheduling algorithms, focused optimization metrics, and evaluation tools in the fog computing and IoE environment. The goal of this survey article is fivefold. First, we review the fog computing and IoE paradigms. Second, we delineate the optimization metric engaged with fog computing and IoE environment. Third, we review, classify, and compare existing scheduling algorithms dealing with fog computing and IoE environment paradigms by leveraging some examples. Fourth, we rationalize the scheduling algorithms and point out the lesson learned from the survey. Fifth, we discuss the open issues and future research directions to improve scheduling in fog computing and the IoE environment.
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