Dynamic Q-Learning-Based Optimized Load Balancing Technique in Cloud

4区 计算机科学 Q4 Computer Science Mobile Information Systems Pub Date : 2023-11-06 DOI:10.1155/2023/7250267
Arvindhan Muthusamy, Rajesh Kumar Dhanaraj
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引用次数: 0

Abstract

Cloud computing provides on-demand access to a shared puddle of computing resources, containing applications, storage, services, and servers above the internet. This allows organizations to scale their IT infrastructure up or down as needed, reduce costs, and improve efficiency and flexibility. Improving professional guidelines for social media interactions is crucial to address the wide range of complex issues that arise in today’s digital age. It is imperative to enhance and update professional guidelines regarding social media interactions in order to effectively tackle the multitude of intricate issues that emerge. In this paper, we propose a reinforcement learning (RL) method for handling dynamic resource allocation (DRA) and load balancing (LB) activity in a cloud environment and achieve good scalability and a significant improvement in performance. To address this matter, we propose a dynamic load balancing technique based on Q-learning, a reinforcement learning algorithm. Our technique leverages Q-learning to acquire an optimal policy for resource allocation in real-time based on existing workload, resource accessibility, and user preferences. We introduce a reward function that takes into account performance metrics such as response time and resource consumption, as well as cost considerations. We evaluate our technique through simulations and show that it outperforms traditional load balancing techniques in expressions of response time and resource utilization while also reducing overall costs. The proposed model has been compared with previous work, and the consequences show the significance of the proposed work. Our model secures a 20% improvement in scalability services. The DCL algorithm offers significant advantages over genetic and min-max algorithms in terms of training time and effectiveness. Through simulations and analysis on various datasets from the machine learning dataset repository, it has been observed that the proposed DCL algorithm outperforms both genetic and min-max algorithms. The training time can be reduced by 10% to 45%, while effectiveness is enhanced by 30% to 55%. These improvements make the DCL algorithm a promising option for enhancing training time and effectiveness in machine learning applications. Further research can be conducted to investigate the potential of combining the DCL algorithm with a supervised training algorithm, which could potentially further improve its performance and apply in real-world application.
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云环境下基于动态q学习的优化负载均衡技术
云计算提供了对共享计算资源的按需访问,这些资源包括互联网之上的应用程序、存储、服务和服务器。这允许组织根据需要扩大或缩小其IT基础设施,降低成本,并提高效率和灵活性。改善社交媒体互动的专业指导方针对于解决当今数字时代出现的各种复杂问题至关重要。必须加强和更新有关社交媒体互动的专业指导方针,以便有效地解决出现的众多复杂问题。在本文中,我们提出了一种强化学习(RL)方法来处理云环境中的动态资源分配(DRA)和负载平衡(LB)活动,并获得了良好的可扩展性和显著的性能改进。为了解决这个问题,我们提出了一种基于Q-learning(一种强化学习算法)的动态负载平衡技术。我们的技术利用Q-learning来获取基于现有工作负载、资源可访问性和用户偏好的实时资源分配的最佳策略。我们引入了一个奖励函数,该函数考虑了响应时间和资源消耗等性能指标,以及成本考虑。我们通过模拟评估了我们的技术,并表明它在响应时间和资源利用率方面优于传统的负载平衡技术,同时还降低了总体成本。将所提出的模型与以往的工作进行了比较,结果表明了所提出的工作的重要性。我们的模型确保可伸缩性服务提高20%。与遗传算法和最小-最大算法相比,DCL算法在训练时间和效率方面具有显著的优势。通过对来自机器学习数据库的各种数据集的模拟和分析,观察到所提出的DCL算法优于遗传算法和最小-最大算法。培训时间可减少10% ~ 45%,效率提高30% ~ 55%。这些改进使DCL算法成为机器学习应用中提高训练时间和效率的有希望的选择。可以进一步研究将DCL算法与监督训练算法相结合的潜力,这可能会进一步提高其性能并应用于实际应用。
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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来源期刊
Mobile Information Systems
Mobile Information Systems 工程技术-电信学
自引率
0.00%
发文量
1797
审稿时长
3 months
期刊介绍: Mobile Information Systems is a peer-reviewed, open access journal that publishes original research articles as well as review articles related to all aspects of mobile information systems.
期刊最新文献
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