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基于进化多目标优化的微服务组合部署与调度策略 被引量:10

Micro-service composition deployment and scheduling strategy based on evolutionary multi-objective optimization
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摘要 面向微服务实例在不同资源中心的组合部署与调度问题,构建微服务组合部署与调度最优化问题模型。以资源服务中心计算及存储资源利用率、负载均衡率和微服务实际使用率等为优化目标,以服务的完备性、资源与存储资源总量和微服务序列总量为约束条件,提出基于进化多目标优化算法(NSGA-Ⅲ,MOEA/D)求解方法,寻求微服务序列在不同资源中心的实例组合部署与调度策略。通过真实数据集实验对比,在全部满足用户服务请求的约束下,该策略比传统微服务组合调度策略的计算、存储资源平均空闲率和微服务实际空闲率要分别低13.21%、5.2%和16.67%。 For the combined deployment and scheduling of micro-service instances in different resource centers,the micro-service combination deployment and scheduling optimization problem model is built,and the resource service center computing and storage resource utilization,load balancing rate and service actual usage rate are optimized.With the completeness of service,the total amount of resources and storage resources and the total number of micro-service sequences,the evolutionary multi-objective optimization algorithm(NSGA-Ⅲ,MOEA/D)is used to solve the example combination deployment and scheduling strategy of micro-service sequences in different resource centers.Compared with the traditional data set in the some condition,the proposed strategy calculates the storage resources,the computing usage rate,and the actual service usage are reduced by 13.21%,5.2%and 16.67%.
作者 马武彬 王锐 王威超 吴亚辉 邓苏 黄宏斌 MA Wubin;WANG Rui;WANG Weichao;WU Yahui;DENG Su;HUANG Hongbin(Science and Technology on Information System Engineering Laboratory,National University of Defense Technology,Changsha 410073,China;Computer Science Department,Loughborough University,Loughborough LE113TU,UK)
出处 《系统工程与电子技术》 EI CSCD 北大核心 2020年第1期90-100,共11页 Systems Engineering and Electronics
基金 国家自然科学基金(61871388,61773390) 湖南省自然科学基金(2018JJ3619) 湖湘青年英才计划(2018RS3081)资助课题
关键词 微服务 服务组合优化 基于参考点非支配排序遗传算法 基于分解的多目标进化算法 多目标优化 micro-service service composition optimization non-dominated sorted genetic algorithm-Ⅲ(NSGA-Ⅲ) multi-objective evolutionary algorithm based on decomposition (MOEA/D) multi-objective optimization
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  • 1Zhang L J, Zhang J, Cai H. Services Computing. Beijing: Springer and Tsinghua University Press, 2007. 被引量:1
  • 2Li Y, Lin C. QoS-aware service composition for workflow- based data-intensive applieations//Proceedings of the 2011 IEEE International Conference on Web Services (ICWS 2011). Washington, USA, 2011:452-459. 被引量:1
  • 3Boyd S, Vandenberghe L. Convex Optimization. Cambridge, UK: Cambridge University Press, 2004. 被引量:1
  • 4Cormen T H, Leiserson C E, Rivest R L, Stein C. Introduction to Algorithms. MIT, USA: MIT Press, 2005. 被引量:1
  • 5Wada H, Champrasert P, Suzuki J, Oha K. Multiobjectrve optimization of SLA-aware service composition//Proceedings of the IEEE Congress on Services. Honolulu, USA, 2008: 368-375. 被引量:1
  • 6Zhou Z, Liu F, Jin H, et al. On arbitrating the power- performance tradeoff in SaaS clouds//Proceedings of the IEEE INFOCOM 2013. Turin, Italy, 2013:872-880. 被引量:1
  • 7Leitner P, Hummer W, Satzger B, et al. Cost-efficient and application SLA-aware client side request scheduling in an infrastructure-as-a-service cloud//Proceedings of the 2012 IEEE 5th International Conference on Cloud Computing (CLOUD 2012). Honolulu, USA, 2012:213-220. 被引量:1
  • 8Kong X, Lin C, Jiang Y, et al. Efficient dynamic task scheduling in virtualized data centers with fuzzy prediction. Journal of Network and Computer Applications, 2011, 34(4) : 1068-1077. 被引量:1
  • 9Bessai K, Youcef S, Oulamara A, et al. Bi-criteria workflow tasks allocation and scheduling in cloud computing environ- ments//Proceedings of the 2012 IEEE 5th International Conference on Cloud Computing (CLOUD 2012). Honolulu, USA, 2012:638-645. 被引量:1
  • 10Wagner F, Klein A, Klopper B, et al. Multi-objective service composition with time- and input-dependent QoS//Proceedings of the 2012 IEEE 19th International Conference on Web Services (ICWS 2012). Honolulu, USA, 2012:234-241. 被引量:1

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