论文标题
基于动态约束的影响框架及其在负载平衡的随机建模中的应用
Dynamic Constraint-based Influence Framework and its Application in Stochastic Modeling of Load Balancing
论文作者
论文摘要
通过网络连接的组件相互影响并以各种方式进行交互。此类系统的示例是计算节点网络，例如，用于负载平衡目的，节点通过交换工作负载进行交互。在本文中，我们首先研究了一个网络Markov链框架的影响模型，用于建模网络交互并讨论该模型的两个关键局限性，这使其在网络中基于约束的基于约束和动态的交互作用中缺乏。接下来，我们提出基于动态和约束的影响模型（DCIM）来减轻局限性。 DCIM将影响模型的应用扩展到更通用的网络交互情况。在本文中，提出的DCIM成功地应用于计算节点网络中负载平衡的随机建模，允许预测系统中的负载分布，这是影响模型的新应用。 DCIM进一步用于确定网络计算系统中负载平衡的最佳工作负载分配策略。
Components connected over a network influence each other and interact in various ways. Examples of such systems are networks of computing nodes, which the nodes interact by exchanging workload, for instance, for load balancing purposes. In this paper, we first study the Influence Model, a networked Markov chain framework, for modeling network interactions and discuss two key limitations of this model, which cause it to fall short in modeling constraint-based and dynamic interactions in networks. Next, we propose the Dynamic and Constraint-based Influence Model (DCIM) to alleviate the limitations. The DCIM extends the application of the Influence Model to more general network interaction scenarios. In this paper, the proposed DCIM is successfully applied to stochastic modeling of load balancing in networks of computing nodes allowing for prediction of the load distribution in the system, which is a novel application for the Influence Model. The DCIM is further used to identify the optimum workload distribution policy for load balancing in networked computing systems.
