李丹丹. 基于认知网络行为模型的资源分配策略[J]. 微电子学与计算机, 2015, 32(7): 62-67. DOI: 10.19304/j.cnki.issn1000-7180.2015.07.015
引用本文: 李丹丹. 基于认知网络行为模型的资源分配策略[J]. 微电子学与计算机, 2015, 32(7): 62-67. DOI: 10.19304/j.cnki.issn1000-7180.2015.07.015
LI Dan-dan. Resource Borrowing from Reservation Strategy for Cognitive Networks[J]. Microelectronics & Computer, 2015, 32(7): 62-67. DOI: 10.19304/j.cnki.issn1000-7180.2015.07.015
Citation: LI Dan-dan. Resource Borrowing from Reservation Strategy for Cognitive Networks[J]. Microelectronics & Computer, 2015, 32(7): 62-67. DOI: 10.19304/j.cnki.issn1000-7180.2015.07.015

基于认知网络行为模型的资源分配策略

Resource Borrowing from Reservation Strategy for Cognitive Networks

  • 摘要: 在分析了区分服务网络中的主动队列管理机制的基础上,根据认知网络的特点和优势,提出了闲置预留资源的自适应借用策略.该策略在节点增加了相应的功能模块,并为特殊的实时业务(如额外业务)预留一定比例的资源.新业务到来时,首先考虑新请求的类型,然后根据新请求提供参数的不同分配资源,如果现有的剩余资源不能满足接入要求,则实时业务可以适当借用其他节点的闲置预留资源,以满足其所需要的最小带宽.通过仿真表明,RBFR策略既保证了特殊业务的优先性,又在网络资源利用率,新申请接入率,提高用户QoS方面均具有良好的性能,具有很好的应用前景.

     

    Abstract: This paper analyzes the drawbacks of existing resource reservation mechanisms, and proposes a strategy borrowing resource from idle reservation (RBFR) according to the characteristics and advantages of cognitive networks. The proposed strategy adds function module in nodes. First, it considers the type of new requests, and then assigns resource according to their different parameters. If the available resource can not meet the requirements of the new request, real-time request is accesses of higher priority and borrows the idle reservation from non-real-time business appropriately. Comprehensive simulations show that, RBFR has good performance at packet loss rate, network resource utilization and the rejected rate of new requests.

     

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