In data stream management systems (DSMSs), how to maintain the quality of queries is a difficult problem because both the processing cost and data arrival rates are highly unpredictable. When the system is overloaded,...In data stream management systems (DSMSs), how to maintain the quality of queries is a difficult problem because both the processing cost and data arrival rates are highly unpredictable. When the system is overloaded, quality degrades significantly and thus load shedding becomes necessary. Unlike processing overloading in the general way which is only by a feedback control (FB) loop to obtain a good and stable performance over data streams, a feedback plus feed-forward control (FFC) strategy is introduced in DSMSs, which have a good quality of service (QoS) in the aspects of miss ratio and processing delay. In this paper, a quality adaptation framework is proposed, in which the control-theory-based techniques are leveraged to adjust the application behavior with the considerations of the current system status. Compared to previous solutions, the FFC strategy achieves a good quality with a waste of fewer resources.展开更多
当前,新型网络应用不断涌现,用户对不同类型应用的通信需求也呈现出多样化和个性化的特点.面向用户频繁产生和变化的通信需求,网络服务提供商(Internet service provider,简称ISP)通常以不断地购买及部署大量新型的专用网络设备的方式...当前,新型网络应用不断涌现,用户对不同类型应用的通信需求也呈现出多样化和个性化的特点.面向用户频繁产生和变化的通信需求,网络服务提供商(Internet service provider,简称ISP)通常以不断地购买及部署大量新型的专用网络设备的方式来应对,导致其运营成本高昂,资源浪费严重,网络建设与发展的可持续性差.对此,从软件角度出发,考虑路由功能重用,通过选择合适的路由功能,在通信路径上为应用合成定制化的路由服务,满足用户差异化的需求.基于网络功能虚拟化(network function virtualization,简称NFV)和软件定义网络(software-defined networking,简称SDN),提出了一种自适应路由服务合成机制,运用软件产品线技术构建路由服务产品线,作为路由功能选择和路由服务优化的基础.基于机器学习,运用多层前馈神经网构建路由服务离线模式和在线模式两阶段学习模型,对路由功能选择及组合进行持续学习和优化,实现路由服务的定制化目标,以提高用户的服务体验.进行了仿真实现,研究结果表明,所提出的模型是可行和有效的.展开更多
基金Supported by the National Key R&D Program of China(2016YFC1401900)the National Science Foundation of China(61173029,61672144)
文摘In data stream management systems (DSMSs), how to maintain the quality of queries is a difficult problem because both the processing cost and data arrival rates are highly unpredictable. When the system is overloaded, quality degrades significantly and thus load shedding becomes necessary. Unlike processing overloading in the general way which is only by a feedback control (FB) loop to obtain a good and stable performance over data streams, a feedback plus feed-forward control (FFC) strategy is introduced in DSMSs, which have a good quality of service (QoS) in the aspects of miss ratio and processing delay. In this paper, a quality adaptation framework is proposed, in which the control-theory-based techniques are leveraged to adjust the application behavior with the considerations of the current system status. Compared to previous solutions, the FFC strategy achieves a good quality with a waste of fewer resources.
文摘当前,新型网络应用不断涌现,用户对不同类型应用的通信需求也呈现出多样化和个性化的特点.面向用户频繁产生和变化的通信需求,网络服务提供商(Internet service provider,简称ISP)通常以不断地购买及部署大量新型的专用网络设备的方式来应对,导致其运营成本高昂,资源浪费严重,网络建设与发展的可持续性差.对此,从软件角度出发,考虑路由功能重用,通过选择合适的路由功能,在通信路径上为应用合成定制化的路由服务,满足用户差异化的需求.基于网络功能虚拟化(network function virtualization,简称NFV)和软件定义网络(software-defined networking,简称SDN),提出了一种自适应路由服务合成机制,运用软件产品线技术构建路由服务产品线,作为路由功能选择和路由服务优化的基础.基于机器学习,运用多层前馈神经网构建路由服务离线模式和在线模式两阶段学习模型,对路由功能选择及组合进行持续学习和优化,实现路由服务的定制化目标,以提高用户的服务体验.进行了仿真实现,研究结果表明,所提出的模型是可行和有效的.
