不确定网格计算环境中基于模糊Neutrosophic软集的传递Q学习负载平衡方案

IF 1.2 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Cybernetics and Information Technologies Pub Date : 2022-11-01 DOI:10.2478/cait-2022-0038
K. Bhargavi, S. Shiva
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引用次数: 1

摘要

摘要与其他传统分布式计算平台相比,网格计算中的有效负载平衡更为困难,因为它具有异构性、自主性、可扩展性和适应性、资源选择和分配机制以及数据分离等特点。因此,在做出负载平衡决策之前,有必要识别和处理任务和网格资源的不确定性。利用隐马尔可夫模型的两种潜在形式,即轮廓隐马尔可夫模型(PF_HMM)和配对隐马尔可夫模型,识别了任务和系统参数中的不确定性。然后使用我们新的基于模糊Neutrosophic软集理论(FNSS)的转移Q学习和预先训练的知识来实现负载平衡。使用FNSS启用的转移Q学习有效地解决了大规模负载平衡问题,因为模型已经过训练,不需要预训练。我们的期望值分析和仿真结果证实,所提出的方案比最近的三种负载平衡方案好90%。
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Fuzzy Neutrosophic Soft Set Based Transfer-Q-Learning Scheme for Load Balancing in Uncertain Grid Computing Environments
Abstract Effective load balancing is tougher in grid computing compared to other conventional distributed computing platforms due to its heterogeneity, autonomy, scalability, and adaptability characteristics, resource selection and distribution mechanisms, and data separation. Hence, it is necessary to identify and handle the uncertainty of the tasks and grid resources before making load balancing decisions. Using two potential forms of Hidden Markov Models (HMM), i.e., Profile Hidden Markov Model (PF_HMM) and Pair Hidden Markov Model (PR_HMM), the uncertainties in the task and system parameters are identified. Load balancing is then carried out using our novel Fuzzy Neutrosophic Soft Set theory (FNSS) based transfer Q-learning with pre-trained knowledge. The transfer Q-learning enabled with FNSS solves large scale load balancing problems efficiently as the models are already trained and do not need pre-training. Our expected value analysis and simulation results confirm that the proposed scheme is 90 percent better than three of the recent load balancing schemes.
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来源期刊
Cybernetics and Information Technologies
Cybernetics and Information Technologies COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.20
自引率
25.00%
发文量
35
审稿时长
12 weeks
期刊最新文献
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