Abstract
Under the development of computing and network convergence, considering the computing and network resources of multiple providers as a whole in a computing force network (CFN) has gradually become a new trend. However, since each computing and network resource provider (CNRP) considers only its own interest and competes with other CNRPs, introducing multiple CNRPs will result in a lack of trust and difficulty in unified scheduling. In addition, concurrent users have different requirements, so there is an urgent need to study how to optimally match users and CNRPs on a many-to-many basis, to improve user satisfaction and ensure the utilization of limited resources. In this paper, we adopt a reputation model based on the beta distribution function to measure the credibility of CNRPs and propose a performance-based reputation update model. Then, we formalize the problem into a constrained multi-objective optimization problem and find feasible solutions using a modified fast and elitist non-dominated sorting genetic algorithm (NSGA-II). We conduct extensive simulations to evaluate the proposed algorithm. Simulation results demonstrate that the proposed model and the problem formulation are valid, and the NSGA-II is effective and can find the Pareto set of CFN, which increases user satisfaction and resource utilization. Moreover, a set of solutions provided by the Pareto set give us more choices of the many-to-many matching of users and CNRPs according to the actual situation.
摘要
随着算力和网络融合的发展, 在算力网络(CFN)中统筹考虑多个提供商的算力资源和网络资源逐渐成为一种新趋势. 然而, 由于每个算网资源提供商(CNRP)只考虑自身利益, 与其他CNRP存在竞争关系, 因此引入多个CNRP会造成缺乏信任和难以统一调度的问题. 此外, 多个并发用户的需求各不相同, 因此迫切需要研究如何在多对多的基础上优化匹配用户和CNRP, 从而提高用户满意度, 保证和提高有限资源的利用率. 首先采用基于贝塔分布函数的声誉模型衡量CNRP可信度, 并提出基于性能的声誉更新模型. 其次, 将问题形式化为一个约束多目标优化问题, 并使用改进的快速精英非支配排序遗传算法(NSGA-II)找到可行解. 本文进行大量仿真实验评估所提算法. 仿真结果表明, 所提模型、 问题表述、和NSGA-II是有效的, NSGA-II可以找到CFN的帕累托集, 提高用户满意度和资源利用率. 此外, 帕累托集所提供的一组解决方案根据实际情况为用户和CNRP的多对多匹配问题提供更多选择.
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Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Yuexia FU and Lu LU designed the research. Yuexia FU and Qinqin TANG designed and conducted the simulations. Yuexia FU and Lu LU drafted the paper. Jing WANG helped organize the paper. Yuexia FU, Lu LU, and Sheng ZHANG revised and finalized the paper.
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Project supported by the National Natural Science Foundation of China (No. 2022ZD0115303), the 2023 Beijing Outstanding Young Engineers Innovation Studio, China, and the Beijing University of Posts and Telecommunications–China Mobile Research Institute Joint Innovation Foundation (No. CMYJY-202200536)
List of supplementary materials
1 Reputation-based joint optimization of user satisfaction and resource utilization
2 Problem solution based on NSGA-II Fig. S1 Initial population
Fig. S2 Schematic of the Pareto rank of the solution space
Algorithm S1 NSGA-II
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Fu, Y., Wang, J., Lu, L. et al. Reputation-based joint optimization of user satisfaction and resource utilization in a computing force network. Front Inform Technol Electron Eng 25, 685–700 (2024). https://doi.org/10.1631/FITEE.2300156
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DOI: https://doi.org/10.1631/FITEE.2300156