M2M业务批量到达排队系统性能分析

摘要

The large-scale applications of M2M( Machine to Machine) service will have great impacts on the current network in QoS,IBP(Interrupt Bernoulli Process) is used to model M2M services with batch arrival,then a discrete time queue modeHBP/Geom/1/K is established and solved- Different from typical IBP model,the number of arrival packets each time is a batch,instead of one. Mathematical models with different burstmess arc used to describe the batch, then we obtain the steady state probability of queue length in the probability domain , furthermore the throughout and packet loss ratio and other performance metrics are derived. System performance with single arrival and with batch arrival under the same queuing intensity are compared. The results indicate that the system performances decrease as the burstiness of the number of arrival packets every batch increases and the queuing performances with batch arrival arc worse than that with single arrival under the same queuing intensity. For M2M small data services with delay-tolerance characteristics,we can increase cache at the cost of increasing delay to improve the system throughput and decrease the blocking rate.%针对M2M(Machine to Machine)业务的大规模应用给当前移动通信网络的QoS带来的冲击和影响问题,采用IBP(Interrupt Bernoulli Process)建模M2M业务的到达过程,业务以批量的形式到达,建立并求解了离散时间系统排队模型IBP/Geom/1/K.区别于传统的IBP模型,该模型每次到达的不是一个,而是一批.采用具有不同突发度的数学模型表征M2M业务每批到达的数量,在概率空间上求解队长的稳态概率,进而得到系统的吞吐量和丢包率等性能指标,并与相同排队强度下M2M业务单个到达时的性能进行对比.实验结果表明,每批到达包数的突发度越大,系统的性能越差；在相同排队强度下,批量到达排队模型的性能对比单个到达情况下的系统性能差；对时延容忍的M2M小数据业务,以时延增加为代价增大缓存可以有效提高吞吐量、降低阻塞率.