An intelligent decision system for virtual machine migration based on specific Q-learning

Xinying Zhu, Ran Xia, Hang Zhou, Shuo Zhou, Haoran Liu
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Abstract

Due to the convenience of virtualization, the live migration of virtual machines is widely used to fulfill optimization objectives in cloud/edge computing. However, live migration may lead to side effects and performance degradation when migration is overused or an unreasonable migration process is carried out. One pressing challenge is how to capture the best opportunity for virtual machine migration. Leveraging rough sets and AI, this paper provides an innovative strategy based on Q-learning that is designed for migration decisions. The highlight of our strategy is the harmonious mechanism for applying rough sets and Q-learning. For the ABDS (adaptive boundary decision system) strategy in this paper, the exploration space of Q learning is confined by the boundary region of rough sets, while the thresholds of the boundary region can be dynamically adjusted by the reaction results from the computing cluster. The structure and mechanism of the ABDS strategy are described in this paper. The corresponding experiments show a firm advantage for the cooperation of rough sets and reinforcement learning algorithms. Considering both the energy consumption and application performance, the ABDS strategy in this paper outperforms the benchmark strategies in comprehensive performance.
基于特定 Q 学习的虚拟机迁移智能决策系统
由于虚拟化的便利性,虚拟机的实时迁移被广泛用于实现云计算/边缘计算的优化目标。然而,当过度使用迁移或执行不合理的迁移过程时,实时迁移可能会导致副作用和性能下降。如何抓住虚拟机迁移的最佳时机是一个亟待解决的难题。本文利用粗糙集和人工智能,提供了一种基于 Q-learning 的创新策略,专为迁移决策而设计。我们策略的亮点在于应用粗糙集和 Q-learning 的和谐机制。在本文的 ABDS(自适应边界决策系统)策略中,Q 学习的探索空间被限定在粗糙集的边界区域内,而边界区域的阈值可根据计算集群的反应结果进行动态调整。本文介绍了 ABDS 策略的结构和机制。相应的实验表明,粗糙集和强化学习算法的合作具有坚实的优势。考虑到能耗和应用性能,本文的 ABDS 策略在综合性能上优于基准策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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