文章从提升校园网接入带宽所面临的问题出发,提出了一种在多出口校园网环境中,利用策略路由技术(PBR)和网络地址翻译(NAT)技术解决校园网出口带宽瓶颈和多链路负载均衡的技术方案。该方案充分整合了CERNET(中国教育和科研计算机网)及本...文章从提升校园网接入带宽所面临的问题出发,提出了一种在多出口校园网环境中,利用策略路由技术(PBR)和网络地址翻译(NAT)技术解决校园网出口带宽瓶颈和多链路负载均衡的技术方案。该方案充分整合了CERNET(中国教育和科研计算机网)及本地ISP(Internet Service ProvNer)的优势资源,并作到多链路负载均衡,在高校校园网中进行了实际应用,是行之有效的校园网出口瓶颈及链路负载均衡的解决办法。展开更多
The increasing penetration of renewable energy sources introduces higher requirements for the operation flexibility of transmission system(TS) and connected active distribution systems(DSs). This paper presents an eff...The increasing penetration of renewable energy sources introduces higher requirements for the operation flexibility of transmission system(TS) and connected active distribution systems(DSs). This paper presents an efficient distributed framework for the TS and DSs to work cooperatively yet independently. In addition to conventional power interaction, upward and downward reserve capacities are exchanged to form the feasible access regions at the boundaries that apply to different system operation situations. A distributed robust energy and reserve dispatch approach is proposed under this framework. The approach utilizes the supply-and demand-side resources in different systems to handle various uncertainties and improve overall efficiency and reliability. In particular, integrated as aggregated virtual energy storage(AVES) devices, air-conditioning loads are incorporated into the optimal dispatch. In addition, a reserve model with charging/discharging-state elasticity is developed for AVESs to enhance system flexibility and provide additional reserve support. Different cases are compared to verify the effectiveness and superiority of the proposed approach.展开更多
The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support ...The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted.In addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time slots.It is resulting in TD missing potential offloading opportunities in the future.To fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic eviction.Long Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot.The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology.Simulation results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.展开更多
文摘文章从提升校园网接入带宽所面临的问题出发,提出了一种在多出口校园网环境中,利用策略路由技术(PBR)和网络地址翻译(NAT)技术解决校园网出口带宽瓶颈和多链路负载均衡的技术方案。该方案充分整合了CERNET(中国教育和科研计算机网)及本地ISP(Internet Service ProvNer)的优势资源,并作到多链路负载均衡,在高校校园网中进行了实际应用,是行之有效的校园网出口瓶颈及链路负载均衡的解决办法。
基金supported by the Scientific Research Startup Foundation of Recruiting Talents of Nanjing Institute of Technology (No. YKJ202225)。
文摘The increasing penetration of renewable energy sources introduces higher requirements for the operation flexibility of transmission system(TS) and connected active distribution systems(DSs). This paper presents an efficient distributed framework for the TS and DSs to work cooperatively yet independently. In addition to conventional power interaction, upward and downward reserve capacities are exchanged to form the feasible access regions at the boundaries that apply to different system operation situations. A distributed robust energy and reserve dispatch approach is proposed under this framework. The approach utilizes the supply-and demand-side resources in different systems to handle various uncertainties and improve overall efficiency and reliability. In particular, integrated as aggregated virtual energy storage(AVES) devices, air-conditioning loads are incorporated into the optimal dispatch. In addition, a reserve model with charging/discharging-state elasticity is developed for AVESs to enhance system flexibility and provide additional reserve support. Different cases are compared to verify the effectiveness and superiority of the proposed approach.
文摘The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted.In addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time slots.It is resulting in TD missing potential offloading opportunities in the future.To fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic eviction.Long Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot.The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology.Simulation results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.
