Alharthi Saleha, Alshamsi Afra, Alseiari Anoud, Alwarafy Abdulmalik
Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates.
Sensors (Basel). 2024 Aug 28;24(17):5551. doi: 10.3390/s24175551.
In the dynamic world of cloud computing, auto-scaling stands as a beacon of efficiency, dynamically aligning resources with fluctuating demands. This paper presents a comprehensive review of auto-scaling techniques, highlighting significant advancements and persisting challenges in the field. First, we overview the fundamental principles and mechanisms of auto-scaling, including its role in improving cost efficiency, performance, and energy consumption in cloud services. We then discuss various strategies employed in auto-scaling, ranging from threshold-based rules and queuing theory to sophisticated machine learning and time series analysis approaches. After that, we explore the critical issues in auto-scaling practices and review several studies that demonstrate how these challenges can be addressed. We then conclude by offering insights into several promising research directions, emphasizing the development of predictive scaling mechanisms and the integration of advanced machine learning techniques to achieve more effective and efficient auto-scaling solutions.
在云计算这个充满活力的世界中，自动缩放是效率的灯塔，能根据不断变化的需求动态调整资源。本文全面回顾了自动缩放技术，突出了该领域的重大进展和持续存在的挑战。首先，我们概述自动缩放的基本原理和机制，包括其在提高云服务成本效率、性能和能源消耗方面的作用。然后，我们讨论自动缩放中采用的各种策略，从基于阈值的规则和排队论到复杂的机器学习和时间序列分析方法。之后，我们探讨自动缩放实践中的关键问题，并回顾一些展示如何应对这些挑战的研究。最后，我们通过对几个有前景的研究方向提供见解来得出结论，强调预测性缩放机制的发展以及先进机器学习技术的整合，以实现更有效和高效的自动缩放解决方案。
