首页|基于声誉机制的算力网络资源利用率和用户满意度联合优化

基于声誉机制的算力网络资源利用率和用户满意度联合优化

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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.
Reputation-based joint optimization of user satisfaction and resource utilization in a computing force network
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.

Computing force networkResource schedulingPerformance-based reputationUser satisfaction

付月霞、王晶、陆璐、唐琴琴、张晟

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中国移动通信有限公司研究院,中国 北京市,100053

紫金山实验室,中国南京市,211111

中国移动通信集团有限公司,中国 北京市,100033

算力网络 资源调度 基于性能的声誉 用户满意度

国家自然科学基金Beijing Outstanding Young Engineers Innovation Studio,China(2023)Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Foundation

2022ZD0115303CMYJY-202200536

2024

10.1631/FITEE.2300156

信息与电子工程前沿(英文)
浙江大学

信息与电子工程前沿(英文)

CSTPCD
影响因子：0.371
ISSN：2095-9184
年,卷(期)：2024.25(5)
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