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首页> 外文会议>International conference on distributed computer and communication networks >Evaluation and Prediction of an Optimal Control in a Processor Sharing Queueing System with Heterogeneous Servers
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Evaluation and Prediction of an Optimal Control in a Processor Sharing Queueing System with Heterogeneous Servers

机译：异构服务器的处理器共享排队系统的最佳控制评估与预测

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In this paper we study the problem of optimal controlling in a processor sharing (PS) M/M/2 queueing system with heterogeneous servers. The servers differ in the service intensities, operating and usage costs. The objective is to find the optimal policy to allocate the customers either to an idle or partially loaded server, or to the queue at each arrival and service completion epoch to minimize the long-run average cost per unit of time. We handle this optimization problem as Markov decision problem and study numerically structural properties of the optimal control policy. Using a policy-iteration algorithm we show that this policy for the current model is of threshold type. In this case the faster server handles customers with maximum capacity, while the number of simultaneously serviced customers at the slower server can be increased only when the number of waiting customers exceeds a certain threshold level. The data-sets generated by classic methodology of analyzing the controlled queues are used to explore predictions for optimal thresholds through artificial neural networks. The presented theoretical results are accompanied by heuristic solution and numerical examples.
机译：在本文中，我们研究了具有异构服务器的处理器共享（PS）M / M / 2排队系统中最佳控制的问题。服务器在服务强度，运营和使用成本中不同。目的是找到最佳策略来将客户分配给空闲或部分加载的服务器，或者在每个到达和服务完成时期的队列中，以最小化每单位时间的长期平均成本。我们处理此优化问题作为马尔可夫决策问题，研究了最优控制策略的数值结构特性。使用策略迭代算法，我们显示当前模型的此策略是阈值类型。在这种情况下，更快的服务器处理最大容量的客户，而当等待客户的数量超过某个阈值级别时，速度较慢的服务器上同时服务客户的数量才会增加。通过分析受控队列的经典方法生成的数据集用于探索通过人工神经网络的最佳阈值的预测。呈现的理论结果伴随着启发式解决方案和数值例子。

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