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    王 洁, 王宏安, 傅 勇, 李 新. 一种面向异构实时集群系统的使用率反馈控制方法[J]. 计算机研究与发展, 2009, 46(10): 1626-1633.
    引用本文: 王 洁, 王宏安, 傅 勇, 李 新. 一种面向异构实时集群系统的使用率反馈控制方法[J]. 计算机研究与发展, 2009, 46(10): 1626-1633.
    Wang Jie, Wang Hongan, Fu Yong, Li Xin. Feedback Utilization Control for Heterogeneous Real-Time Clusters[J]. Journal of Computer Research and Development, 2009, 46(10): 1626-1633.
    Citation: Wang Jie, Wang Hongan, Fu Yong, Li Xin. Feedback Utilization Control for Heterogeneous Real-Time Clusters[J]. Journal of Computer Research and Development, 2009, 46(10): 1626-1633.

    一种面向异构实时集群系统的使用率反馈控制方法

    Feedback Utilization Control for Heterogeneous Real-Time Clusters

    • 摘要: 软实时系统有着广泛的应用,如在线游戏、股票交易、传感器网络数据处理等.在这类应用中,通常有多个服务节点来响应外界大量的软实时请求.为了保证服务质量及防止系统超载,这些节点的CPU需要满足一定的使用率限制.为此,首先提出了异构集群系统中基于负载平衡的CPU使用率动态模型,其次在算法设计上将反馈控制理论引入其中并提出了针对异构实时集群系统的基于反馈的使用率控制算法.实验结果证明,该算法在引入负载平衡机制的异构实时集群系统中运行良好,系统节点间负载均衡并且各节点的CPU使用率能稳定地运行在限定状态.

       

      Abstract: In applications such as online games, online data services and wireless sensor networks, there is a need for large-scale servers to handle high volume of soft real-time requests. Such clusters must adaptively control the CPU utilization of heterogeneous processors in order to maintain desired performance and prevent system overload in face of unpredictable workloads. According to this, the authors propose a new method named heterogeneous distributed utilization control with load balancing (HDUC-LB) to handle unpredictable workload and improve system QoS in heterogeneous real-time clusters. HDUC-LB mainly includes two parts. First, it presents a new load balancing model for heterogeneous real-time clusters to handle overload of the system. Then, a utilization control method which is based on feed-back theory is proposed to enforce every desired utilization bounds of each processor. Simulation results show that HDUC-LB can provide a robust utilization control and ensure load balancing between different processors in the open and unpredictable environment, and both the load balancing model and the utilization control method can work effectively and robustly in large-scale clusters.

       

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