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College of Computer and Information Science, Chongqing Normal University, Chongqing, China

机译：重庆师范大学计算机与信息科学学院，重庆

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In dynamic service-oriented environment, service monitoring could provide reliability improvement to service composition as well as cost increase. To reduce the overall cost brought by monitoring, existing literatures proposed to decrease the number of monitors through monitoring the most reliability-sensitive services. However, the optimal monitoring rate for those monitors was not taken into account at the same time. Aiming at choosing optimal monitoring rate for minimal number of monitors, this paper proposed to search appropriate monitoring rate to minimize multi kinds of resources cost by monitoring under reliability constraints. Firstly, two multi-objective optimization problems were presented with the reliability and cost models of service composition under monitoring analyzed through Markov chain. Then a multi-objective memetic algorithm (MOMA) was used to search the near-optimal solutions of monitoring rate for services. This algorithm employed nondominated sorting strategy as the global search method and used random walk with direction exploitation method as local search operator. Experimental studies results showed that multi-objective approach for service monitoring rate optimization could provide solutions with a variety of trade-offs between the system reliability and cost comparing with existing greedy sensitivity-based method. Comparison with other multi-objective evolutionary algorithms showed that, in terms of both the coverage rate and hypervolume indicator, MOMA searched more effectively than several state-of-art algorithms including NSGA II, PHC-NSGA-II and HaD-MOEA.
机译：在面向服务的动态环境中，服务监视可以提高服务组合的可靠性并增加成本。为了降低监视带来的总成本，现有文献提出通过监视对可靠性最敏感的服务来减少监视器的数量。但是，没有同时考虑这些监视器的最佳监视率。为了针对最少的监视器选择最优的监视速率，本文提出了在可靠性约束下通过监视来寻找适当的监视速率，以使多种资源成本最小化的方法。首先，提出了两个多目标优化问题，通过马尔可夫链分析了服务组合的可靠性和成本模型。然后，采用多目标模因算法（MOMA）搜索服务监控率的近似最优解。该算法采用非支配排序策略作为全局搜索方法，并采用随机游走与方向挖掘方法作为局部搜索算子。实验研究结果表明，与基于贪婪敏感度的现有方法相比，多目标服务监测速率优化方法可以为系统可靠性和成本之间的折衷提供解决方案。与其他多目标进化算法的比较表明，就覆盖率和超量指标而言，MOMA的搜索比包括NSGA II，PHC-NSGA-II和HaD-MOEA在内的几种最新算法更有效。

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