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作 者:孙日明[1] 胡先浪[1] Sun Riming;Hu Xianlang(Jiangsu Automation Research Instisute,Lianyungang 222061,China)
出 处:《电子技术应用》2021年第9期59-63,68,共6页Application of Electronic Technique
基 金:国家重点研发计划项目(2018YFB1305900)。
摘 要:目前各类智能网络系统已被广泛地应用,但是由于节点众多、外部环境复杂,自我管理具有很大挑战。而自主计算系统(ACS)具有根据策略和目标实现自主管理的能力,在复杂的智能网络系统中具有广阔的应用前景。然而,目前自主计算的评价方法缺乏准确的量化来评估ACS的自我管理水平。首先提出了基于PEPA(Performance Evaluation Process Algbra)的自主计算评价模型。然后,根据自主计算的核心思想(较少或无人干预)提出了一种自我管理的评价指标。此外,为了避免ACS的巨大规模导致传统马尔可夫链的状态空间爆炸,采用连续状态空间近似方法从PEPA模型中生成ODEs(Ordinary Differential Equations)。实验结果表明,提高检测成功率和self-*变迁速率对提高自主计算具有重要意义,为自主计算提供了一种评价方法,可以自动测量自我管理的能力。At present,all kinds of intelligent network systems have been widely used,but due to the large number of nodes and complex external environment,its self-management has great challenges.The autonomous computing system(ACS)has the ability to manage itself according to the strategy and goal,and has broad application prospects in the complex intelligent network system.However,the current evaluation method of autonomic computing lacks accurate quantification to evaluate the self-management level of ACS.This paper firstly proposes an evaluation model of autonomic computing based on PEPA(performance evaluation process algbra).Then,according to the core idea of autonomic computing(less or no intervention),a self-management evaluation index is proposed.In addition,in order to avoid the state space explosion of traditional Markov chain caused by the huge scale of ACS,ODEs(ordinary differential equations)are generated from PEPA model by using continuous state space approximation method.The experimental results show that improving the detection success rate and self-*transition rate is of great significance to improve autonomic computing.This work provides an evaluation method for autonomic computing,which can automatically measure the self-management ability.
分 类 号:TN919.5[电子电信—通信与信息系统] TP393[电子电信—信息与通信工程]
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