<link rel="stylesheet" href="styles.cf390c6bfbe7555f.css">

學術出版;;Publications

Permanent URI for this communityhdl:11536/1

收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

Browse

🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Lai, Yuan-ChengSubject: search.filters.subject.Delays

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item type:Item,
    Smoothed Graphic User Interaction on Smartphones With Motion Prediction
    (2020-04-01)
    Lin, Ying-Dar
    ;
    Chu, Edward T-H
    ;
    Chang, Evan
    ;
    Lai, Yuan-Cheng
    ;
    資訊工程學系
    The smoothness of human-smartphone interaction directly influences users experience and affects their purchase decisions. A commonly used method to improve user interaction of smartphones is to optimize the CPU scheduler. However, optimizing the CPU scheduler requires a modification of operating system. In addition, the improvement of the smoothness of human-smartphone interaction may be limited because the display subsystem is not optimized. Therefore, in this paper, we design a motion prediction queuing system, named MPQS, to improve the smoothness of human-smartphone interaction. For this, we use the information of vector, speed, movement, provided by the queuing mechanism of Android, to predict the movement of user-smartphone interaction. Based on the prediction, we then utilize available execution time between frames to perform image processing. We conducted a set of experiments on beagleboard-xM to evaluate the performance of MPQS. Our experiment results show that the proposed method can reduce the number of jank by up to 21.75%.
  • Thumbnail Image
    Item type:Item,
    Workload and Capacity Optimization for Cloud-Edge Computing Systems with Vertical and Horizontal Offloading
    (2020-03-01)
    Thai, Minh-Tuan
    ;
    Lin, Ying-Dar
    ;
    Lai, Yuan-Cheng
    ;
    Chien, Hsu-Tung
    ;
    交大名義發表
    A collaborative integration between cloud and edge computing is proposed to be able to exploit the advantages of both technologies. However, most of the existing studies have only considered two-tier cloud-edge computing systems which merely support vertical offloading between local edge nodes and remote cloud servers. This paper thus proposes a generic architecture of cloud-edge computing with the aim of providing both vertical and horizontal offloading between service nodes. To investigate the effectiveness of the design for different operational scenarios, we formulate it as a workload and capacity optimization problem with the objective of minimizing the system computation and communication costs. Because such a mixed-integer nonlinear programming (MINLP) problem is NP-hard, we further develop an approximation algorithm which applies a branch-and-bound method to obtain optimal solutions iteratively. Experimental results show that such a cloud-edge computing architecture can significantly reduce total system costs by about 34%, compared to traditional designs which only support vertical offloading. Our results also indicate that, to accommodate the same number of input workloads, a heterogeneous service allocation scenario requires about a 23% higher system costs than a homogeneous scenario.
  • Thumbnail Image
    Item type:Item,
    Toward UAV-Based Airborne Computing
    (2019-12-01)
    Lin, Kate Ching-Ju
    ;
    Wang, Hao-Chen
    ;
    Lai, Yuan-Cheng
    ;
    Lin, Ying-Dar
    ;
    交大名義發表
    The next generation of mobile networks, 5G, aims at supporting lower end-to-end latency, higher reliability and higher throughput, which can be improved by MEC and multi-RAT offloading, respectively. With MEC, a base station in 5G can be equipped with computing power, which can be called as an edge in MEC. With the assistance of the edges, traffic with computational tasks can be directly executed in local servers, without forwarding the tasks to the cloud or core network. Edge computing hence reduces the latency and the traffic load significantly. Conventional multi- RAT offloading decides based only on either communication resources or computing resources. In this work, we argue that, to better utilize the communication and computing resources, neighboring edges should share their resources and cooperatively offload the requests from their clients. To this end, we introduced a double offloading mechanism, called LCCOP, to offload incoming traffic to the best pair of radio and edge subject to the endto- end latency of the requesting connections. We conduct simulations to compare LCCOP with the conventional offloading schemes. The results show that LCCOP's double offloading can significantly improve the satisfaction ratio by up to 83 percent and 143 percent, respectively, as compared to pure computation offloading and pure communication offloading.