Driven by the demands of diverse artificial intelligence(AI)-enabled application,Mobile Edge Computing(MEC)is considered one of the key technologies for 6G edge intelligence.In this paper,we consider a serial task mod...Driven by the demands of diverse artificial intelligence(AI)-enabled application,Mobile Edge Computing(MEC)is considered one of the key technologies for 6G edge intelligence.In this paper,we consider a serial task model and design a quality of service(QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems,which can mitigate the I/O interference brought by resource reuse among virtual machines.Then we construct the system utility measuring QoS based on application latency and user devices’energy consumption.We also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O interference.Simulation results demonstrate the proposed algorithm’s significant advantages in terms of task completion time,terminal energy consumption and system resource utilization.展开更多
文摘提出了一种2.5维(2.5D)系统封装高速输入/输出(I/O)全链路的信号/电源完整性(Signal integrity/power integrity,SI/PI)协同仿真方法。首先通过电磁全波仿真分析SiP内部“芯片I/O引脚-有源转接板-印刷电路板(即封装基板)-封装体I/O引脚”这一主要高速信号链路及相应的转接板/印刷电路板电源分配网络(Power distribution network,PDN)的结构特征和电学特性,在此基础上分别搭建对应有源转接板和印刷电路板两种组装层级的“信号链路+PDN”模型,并分别进行SI/PI协同仿真,提取出反映信号链路/PDN耦合特性的模块化集总电路模型,从而在电路仿真器中以级联模型实现快速的SI/PI协同仿真。与全链路的全波仿真结果的对比表明,模块化后的协同仿真有很好的可信度,而且仿真时间与资源开销大幅缩减,效率明显提升。同时总结了去耦电容的大小与布局密度对PDN电源完整性的影响及对信号完整性的潜在影响,提出了去耦电容布局优化的建议。
基金funded in part by the Open Research Fund of the Shaanxi Province Key Laboratory of Information Communication Network and Security under Grant No.ICNS202003in part supported by BUPT Excellent Ph.D.Students Foundation under Grant CX2022210。
文摘Driven by the demands of diverse artificial intelligence(AI)-enabled application,Mobile Edge Computing(MEC)is considered one of the key technologies for 6G edge intelligence.In this paper,we consider a serial task model and design a quality of service(QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems,which can mitigate the I/O interference brought by resource reuse among virtual machines.Then we construct the system utility measuring QoS based on application latency and user devices’energy consumption.We also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O interference.Simulation results demonstrate the proposed algorithm’s significant advantages in terms of task completion time,terminal energy consumption and system resource utilization.
