ID 原文 译文
19405 实验结果表明，在计算任务量很大的情况下，提出的计算卸载及资源分配机制能够有效降低移动终端能耗，并在任务执行时延方面较局部计算与计算卸载机制分别降低最高60%与10%，提高系统性能。 Simulations are conducted to evaluate the proposed mechanism. Compared with local computing and computation offloading mechanism, the proposed mechanism can effectively reduce the energy consumption of mobile device and the delay of system by up to 60% and 10%, respectively.
19406 网络功能虚拟化(NFV)的引入大幅降低了互联网业务的运营成本。 With the introduction of Network Function Virtualization (NFV), the operating costs of operatorscan be greatly reduced.
19407 针对现有的服务功能链(SFC)编排方法无法在优化底层资源的同时保证业务时延性能的问题，该文提出一种基于重叠网络结构的SFC时空优化编排策略。 However, most existing Service Function Chain (SFC) orchestration researches can notoptimize the resources utilization while guaranteeing the performance of service delay. A spatial and temporal optimal method of Service Function Chain (SFC) orchestration based on an overlay network structure is proposed.
19408 在将计算、网络资源与细粒度时延约束纳入考虑的基础上，该策略通过建立重叠网络模型实现了计算与网络资源的分离，将构建SFC所需的资源开销与相关时延共同抽象化为重叠网络链路权重,从而使SFC编排问题转化为易于求解的最短路径问题。 Based on the consideration of the restrictions such as computing resource, network resource and fine-grained end to end delay, this method separates the computing resource and network resource. The resourcescost and related delay of SFC can be abstracted into the links weight of overlay network, which can help toconvert the SFC orchestration problem into the shortest path problem that can be easily solved.
19409 对于需要批量处理的SFC集合设计了基于重叠网络的模拟退火迭代优化编排算法(ONSA)。 As for theSFC requests set requiring batch processing, an Overlay Network based Simulated Annealing iterative optimalorchestration algorithm(ONSA) is designed.
19410 通过对比实验证明了该策略下编排方案的平均端到端时延、链路资源占用率与运营开销相对其他方案分别降低29.5%, 12.4%与15.2%，请求接受率提高22.3%，虚拟网络功能(VNF)负载均衡性能得到显著提升。 The simulation results demonstrate that the proposed orchestrationscheme can reduce the end-to-end delay, the utilization ratio of link bandwidth resource and the operationalexpenditure by 29.5%, 12.4% and 15.2%, and the acceptance ratio of requests set can be improved by 22.3%.The performance of Virtual Network Function (VNF) load balancing can be significantly improved.
19411 矩阵补全(MC)作为压缩感知(CS)的推广，已广泛应用于不同领域。 As an extension of Compressed Sensing(CS), Matrix Completion(MC) is widely applied to different fields.
19412 近年来，基于黎曼优化的MC算法因重构精度高、计算速度快的特点，引起了广泛关注。 Recently, the Riemannian optimization based MC algorithm attracts a lot of attention from researchers due to its high accuracy in reconstruction and computational efficiency.
19413 针对基于黎曼优化的MC算法需假设原矩阵秩固定已知，且随机选择迭代起点的特点，该文提出一种基于自动秩估计的黎曼优化MC算法。 Considering that the Riemannian optimization based MC algorithm assumes a fixed rank of the original matrix, and selects a random initial point for iteration, a novel algorithm is proposed, namely automatic rank estimation based Riemannian optimization matrix completion algorithm.
19414 该算法通过优化包含秩正则项的目标函数，迭代获取秩估计值和预重构矩阵。在估计所得秩对应的矩阵空间上以预重构矩阵为迭代起点，利用基于黎曼流形的共轭梯度法进行矩阵补全，从而提高重构精度。 In the proposed algorithm, the estimate of rank is obtained minimizing the objective function that involving the rank regulation, in addition, the iterative starting point is optimized based on Riemannian manifold. The Riemannian manifold based conjugate gradient method is then used to completethe matrix, thereby improving the reconstruction precision.