面向军事物联网任务低时延需求的网内协同计算方法
2025,47(1):147-157
任继军
西安邮电大学 通信与信息工程学院, 陕西 西安 710121
李瑞彪
西安邮电大学 通信与信息工程学院, 陕西 西安 710121
马步云
西安电子科技大学 综合业务网理论及关键技术国家重点实验室, 陕西 西安 710071
任智源
西安电子科技大学 综合业务网理论及关键技术国家重点实验室, 陕西 西安 710071
西安邮电大学 通信与信息工程学院, 陕西 西安 710121
李瑞彪
西安邮电大学 通信与信息工程学院, 陕西 西安 710121
马步云
西安电子科技大学 综合业务网理论及关键技术国家重点实验室, 陕西 西安 710071
任智源
西安电子科技大学 综合业务网理论及关键技术国家重点实验室, 陕西 西安 710071
摘要：
为了解决军事物联网中传感数据在往返服务器应用层时的高通信时延消耗问题，提出一种面向低时延任务需求的多设备网内协同计算方法。该方法依托以P4交换机为核心的网络架构展开研究，采用基于P4程序的数据面编程策略来完成交换机内的数据包处理。设计了一种任务映射策略，将任务集映射至交换机网络拓扑，实现任务在网络拓扑路径上边传输边计算的协同作业模式。之后构建时延优化模型以找到最佳映射结果，并通过异构最早完成时间算法进一步对任务进行了最佳调度。实验结果表明，当单个数据包的数据大小为1 000 Byte时，该方法的输出时延与本地服务和云服务相比分别降低约54.2%和72.1%。因此，所提出的方法有效降低了时延，为满足任务的低时延需求提供了切实可行的解决方案。
为了解决军事物联网中传感数据在往返服务器应用层时的高通信时延消耗问题，提出一种面向低时延任务需求的多设备网内协同计算方法。该方法依托以P4交换机为核心的网络架构展开研究，采用基于P4程序的数据面编程策略来完成交换机内的数据包处理。设计了一种任务映射策略，将任务集映射至交换机网络拓扑，实现任务在网络拓扑路径上边传输边计算的协同作业模式。之后构建时延优化模型以找到最佳映射结果，并通过异构最早完成时间算法进一步对任务进行了最佳调度。实验结果表明，当单个数据包的数据大小为1 000 Byte时，该方法的输出时延与本地服务和云服务相比分别降低约54.2%和72.1%。因此，所提出的方法有效降低了时延，为满足任务的低时延需求提供了切实可行的解决方案。
基金项目：
陕西省重点研发计划资助项目（2021GY-100）
陕西省重点研发计划资助项目（2021GY-100）
In-network collaborative computing method for low-latency demand of military IoT tasks
REN Jijun
School of Communications and Information Engineering, Xi′an University of Posts and Telecommunications, Xi′an 710121 , China
LI Ruibiao
School of Communications and Information Engineering, Xi′an University of Posts and Telecommunications, Xi′an 710121 , China
MA Buyun
State Key Laboratory of Integrated Services Networks, Xidian University, Xi′an 710071 , China
REN Zhiyuan
State Key Laboratory of Integrated Services Networks, Xidian University, Xi′an 710071 , China
School of Communications and Information Engineering, Xi′an University of Posts and Telecommunications, Xi′an 710121 , China
LI Ruibiao
School of Communications and Information Engineering, Xi′an University of Posts and Telecommunications, Xi′an 710121 , China
MA Buyun
State Key Laboratory of Integrated Services Networks, Xidian University, Xi′an 710071 , China
REN Zhiyuan
State Key Laboratory of Integrated Services Networks, Xidian University, Xi′an 710071 , China
Abstract：
To solve the problem of high-communication latency consumption when sensor data during the round trip to the server application layer in the military IoT(Internet of Things), an in-network collaborative computing method for multiple devices with low-latency task demands was proposed. This method relied on a network architecture with P4 switches as the core and employed a data-plane programming strategy based on the P4 program to complete the packet processing within the switch. A task mapping strategy was designed to map the task set to a switch network topology, thus realizing a collaborative operation mode in which tasks were computed while being transferred on the network topology path. After that, a latency optimization model was built to find the best mapping result, and the task was further optimally scheduled through the heterogeneous earliest finish time algorithm. Experimental results show that when the data size of a single packet is 1 000 Byte, the output latency of this method is reduced by about 54.2% and 72.1% compared with the local service and cloud service, respectively. Therefore, the proposed method effectively reduces latency, offering a practical solution to meet the low-latency demands of tasks.
To solve the problem of high-communication latency consumption when sensor data during the round trip to the server application layer in the military IoT(Internet of Things), an in-network collaborative computing method for multiple devices with low-latency task demands was proposed. This method relied on a network architecture with P4 switches as the core and employed a data-plane programming strategy based on the P4 program to complete the packet processing within the switch. A task mapping strategy was designed to map the task set to a switch network topology, thus realizing a collaborative operation mode in which tasks were computed while being transferred on the network topology path. After that, a latency optimization model was built to find the best mapping result, and the task was further optimally scheduled through the heterogeneous earliest finish time algorithm. Experimental results show that when the data size of a single packet is 1 000 Byte, the output latency of this method is reduced by about 54.2% and 72.1% compared with the local service and cloud service, respectively. Therefore, the proposed method effectively reduces latency, offering a practical solution to meet the low-latency demands of tasks.
收稿日期：
2022-10-11
2022-10-11
