Today,Internet of Things(IoT)is a technology paradigm which convinces many researchers for the purpose of achieving high performance of packets delivery in IoT applications such as smart cities.Interconnecting various...Today,Internet of Things(IoT)is a technology paradigm which convinces many researchers for the purpose of achieving high performance of packets delivery in IoT applications such as smart cities.Interconnecting various physical devices such as sensors or actuators with the Internet may causes different constraints on the network resources such as packets delivery ratio,energy efficiency,end-to-end delays etc.However,traditional scheduling methodologies in large-scale environments such as big data smart cities cannot meet the requirements for high performance network metrics.In big data smart cities applications which need fast packets transmission ratio such as sending priority packets to hospitals for an emergency case,an efficient schedulingmechanism ismandatory which is the main concern of this paper.In this paper,we overcome the shortcoming issues of the traditional scheduling algorithms that are utilized in big data smart cities emergency applications.Transmission information about the priority packets between the source nodes(i.e.,people with emergency cases)and the destination nodes(i.e.,hospitals)is performed before sending the packets in order to reserve transmission channels and prepare the sequence of transmission of theses priority packets between the two parties.In our proposed mechanism,Software Defined Networking(SDN)with centralized communication controller will be responsible for determining the scheduling and processing sequences for priority packets in big data smart cities environments.In this paper,we compare between our proposed Priority Packets Deadline First scheduling scheme(PPDF)with existing and traditional scheduling algorithms that can be used in urgent smart cities applications in order to illustrate the outstanding network performance parameters of our scheme such as the average waiting time,packets loss rates,priority packets end-to-end delay,and efficient energy consumption.展开更多
传统网络依赖人工配置,在应对规模激增、需求复杂化及实时性要求提升的现代网络环境时,效率低下且成本高昂.大语言模型(Large Language Model,LLM)凭借其出色的自然语言理解能力,在网络自动化配置中展现出巨大的潜力.面向软件定义网络(S...传统网络依赖人工配置,在应对规模激增、需求复杂化及实时性要求提升的现代网络环境时,效率低下且成本高昂.大语言模型(Large Language Model,LLM)凭借其出色的自然语言理解能力,在网络自动化配置中展现出巨大的潜力.面向软件定义网络(Software Defined Networking,SDN),本文提出了一种基于LLM的轻量级自动化配置方法.在数据平面,提出了一种基于检索增强生成(Retrieval-Augmented Generation,RAG)技术的代码自动生成方法RetroP4,支持基于用户意图生成P4代码;在控制平面,提出了一种基于任务分解的流表自动生成方法CtrlSynth,支持基于用户意图和数据平面P4代码生成流表配置.实验结果表明:相较于通用大模型,RetroP4生成的P4代码的语法正确性提高了25%,语义正确性提高了87.5%;CtrlSynth能够准确生成与P4代码匹配的流表信息,在流量意图不超过300条时,准确率可达100%.展开更多
基金This study is supported through Taif University Researchers Supporting Project Number(TURSP-2020/150),Taif University,Taif,Saudi Arabia.
文摘Today,Internet of Things(IoT)is a technology paradigm which convinces many researchers for the purpose of achieving high performance of packets delivery in IoT applications such as smart cities.Interconnecting various physical devices such as sensors or actuators with the Internet may causes different constraints on the network resources such as packets delivery ratio,energy efficiency,end-to-end delays etc.However,traditional scheduling methodologies in large-scale environments such as big data smart cities cannot meet the requirements for high performance network metrics.In big data smart cities applications which need fast packets transmission ratio such as sending priority packets to hospitals for an emergency case,an efficient schedulingmechanism ismandatory which is the main concern of this paper.In this paper,we overcome the shortcoming issues of the traditional scheduling algorithms that are utilized in big data smart cities emergency applications.Transmission information about the priority packets between the source nodes(i.e.,people with emergency cases)and the destination nodes(i.e.,hospitals)is performed before sending the packets in order to reserve transmission channels and prepare the sequence of transmission of theses priority packets between the two parties.In our proposed mechanism,Software Defined Networking(SDN)with centralized communication controller will be responsible for determining the scheduling and processing sequences for priority packets in big data smart cities environments.In this paper,we compare between our proposed Priority Packets Deadline First scheduling scheme(PPDF)with existing and traditional scheduling algorithms that can be used in urgent smart cities applications in order to illustrate the outstanding network performance parameters of our scheme such as the average waiting time,packets loss rates,priority packets end-to-end delay,and efficient energy consumption.
文摘传统网络依赖人工配置,在应对规模激增、需求复杂化及实时性要求提升的现代网络环境时,效率低下且成本高昂.大语言模型(Large Language Model,LLM)凭借其出色的自然语言理解能力,在网络自动化配置中展现出巨大的潜力.面向软件定义网络(Software Defined Networking,SDN),本文提出了一种基于LLM的轻量级自动化配置方法.在数据平面,提出了一种基于检索增强生成(Retrieval-Augmented Generation,RAG)技术的代码自动生成方法RetroP4,支持基于用户意图生成P4代码;在控制平面,提出了一种基于任务分解的流表自动生成方法CtrlSynth,支持基于用户意图和数据平面P4代码生成流表配置.实验结果表明:相较于通用大模型,RetroP4生成的P4代码的语法正确性提高了25%,语义正确性提高了87.5%;CtrlSynth能够准确生成与P4代码匹配的流表信息,在流量意图不超过300条时,准确率可达100%.
