Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing.
翻译:计算机资源管理是一个极具挑战性的问题,需要先后作出决定。资源限制、资源差异、工作负荷的动态和不同性质以及隐蔽/隐蔽的计算环境的不可预测性,使得资源管理更加难以在雾中加以考虑。最近采用了人工智能(AI)和机器学习(ML)解决方案来解决这个问题。具有连续决策能力的AI/ML方法,如强化学习,对于这类类型的问题来说似乎最有希望。但这些算法本身也具有挑战性,如差异大、可解释性和在线培训。不断变化的雾/隐蔽环境动态需要在线学习、采用改变的计算环境的解决方案。在本文件中,我们使用标准审查方法来进行这一系统文学审查,分析AI/ML算法的作用以及这些算法在对雾/隐蔽计算机环境中资源管理的适用性方面的挑战。此外,还讨论了各种机器学习、深层次学习和强化学习技术,用于边缘AI/ML/Eog计算管理。此外,我们介绍了基于AI/ML公司/Edge计算系统的持续背景和现状的计算方法,在AI/MLA/MLA的拟议最新研究方向上,最后讨论了以透明技术的计算方法和基于透明/电子计算方法。