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    EDGE NETWORK COMPUTING SYSTEM WITH DEEP REINFORCEMENT LEARNING BASED TASK SCHEDULING
    1.
    发明公开
    EDGE NETWORK COMPUTING SYSTEM WITH DEEP REINFORCEMENT LEARNING BASED TASK SCHEDULING 审中-公开

    公开(公告)号：US20230153124A1

    公开(公告)日：2023-05-18

    申请号：US17490861

    申请日：2021-09-30

    Applicant: Intelligent Fusion Technology, Inc.

    Inventor： Qi ZHAO ,  Yi LI ,  Mingjie FENG ,  Li LI ,  Genshe CHEN

    IPC: G06F9/445 ,  G06F9/48

    CPC classification number: G06F9/44594 ,  G06F9/4806 ,  G06F2209/509

    Abstract: An edge network computing system includes: a plurality of terminal devices; a plurality of edge servers connected to the terminal device through an access network; and a plurality of cloud servers connected to the plurality of edge servers through a core network. Each edge server is configured to: receive a plurality of computing tasks originated from one of the plurality of terminal devices; use a deep Q-learning neural network (DQN) with experience replay to select one of the plurality of could servers to offload a portion of the plurality of computing tasks; and send the portion of the plurality of computing tasks to the selected cloud server and forward results of the portion of the plurality of computing tasks received from the selected cloud server to the originating terminal device.

    METHOD, DEVICE, AND SYSTEM FOR TCP PACKET TRANSMISSION OVER SATELLITE COMMUNICATION NETWORK
    2.
    发明申请
    METHOD, DEVICE, AND SYSTEM FOR TCP PACKET TRANSMISSION OVER SATELLITE COMMUNICATION NETWORK 有权

    公开(公告)号：US20220086261A1

    公开(公告)日：2022-03-17

    申请号：US17021674

    申请日：2020-09-15

    Applicant: INTELLIGENT FUSION TECHNOLOGY, INC.

    Inventor： Xin TIAN ,  Qi ZHAO ,  Yi LI ,  Genshe CHEN ,  Khanh PHAM

    IPC: H04L29/06 ,  H04L12/26 ,  H04L1/18 ,  H04L12/801

    Abstract: Various embodiments provide a method for transmission control protocol (TCP) packet transmission. The method includes receiving, by a receiver performance enhancing node (PEN), one or more TCP packets each with a timestamp and a sequence number from a sender PEN; evaluating a packet delivery time from the sender PEN to the receiver PEN; detecting whether any TCP packet is lost based on a packet sequence and determining a delay shaping time for each TCP packet based on a maximum number of retransmissions and an evaluated delivery time distribution; in response to a lost TCP packet being detected, determining whether the lost TCP packet needs to be retransmitted based on the maximum number of retransmissions; and in response to the determined delay shaping time, determining when a received TCP packet needs to be forwarded based on the determined delay shaping time and a timestamp associated with the received TCP packet.

    METHOD, SYSTEM, AND STORAGE MEDIUM OF MACHINE-LEARNINGBASED REAL-TIME TASK SCHEDULING FOR APACHE STORM CLUSTER
    3.
    发明申请
    METHOD, SYSTEM, AND STORAGE MEDIUM OF MACHINE-LEARNINGBASED REAL-TIME TASK SCHEDULING FOR APACHE STORM CLUSTER 有权

    公开(公告)号：US20240403117A1

    公开(公告)日：2024-12-05

    申请号：US18806392

    申请日：2024-08-15

    Applicant: Intelligent Fusion Technology, Inc.

    Inventor： Qi ZHAO ,  Genshe CHEN ,  Khanh PHAM ,  Erik BLASCH

    IPC: G06F9/48 ,  G06F9/54 ,  G06N20/00

    Abstract: The present disclosure provides a machine-learning-based real-time task scheduling method. The method includes, for a worker node, executing a training task distributed by a master node; collecting latency time lengths of each machine learning model under different CPU utilization and memory usage; calculating a mean squared error of the latency time lengths of each machine learning model; comparing machine learning models according to mean squared errors of latency time lengths to select a desirable machine learning model installing on the worker node; providing an API for the worker node; when receiving a task by the master node, requesting the worker node to predict a latency time length; and returning the predicted latency time length to the master node; and after the master node collects predicted latency time lengths of worker nodes, assigning the task to a corresponding worker node with a lowest predicted latency time length.

    SYSTEM, METHOD, AND STORAGE MEDIUM OF DISTRIBUTED EDGE COMPUTING FOR COOPERATIVE AUGMENTED REALITY WITH MOBILE SENSING CAPABILITY
    5.
    发明申请
    SYSTEM, METHOD, AND STORAGE MEDIUM OF DISTRIBUTED EDGE COMPUTING FOR COOPERATIVE AUGMENTED REALITY WITH MOBILE SENSING CAPABILITY 有权

    公开(公告)号：US20240406269A1

    公开(公告)日：2024-12-05

    申请号：US18806352

    申请日：2024-08-15

    Applicant: Intelligent Fusion Technology, Inc.

    Inventor： Qi ZHAO ,  Genshe CHEN ,  Khanh PHAM ,  Erik BLASCH

    IPC: H04L67/131 ,  H04L67/10

    Abstract: The present disclosure provides a system of distributed edge computing for cooperative augmented reality with mobile sensing capability. The system includes a plurality of nodes configured to generate a plurality of data streams; and a plurality of distributed edge servers configured to process one or more tasks using the plurality of data streams. An Apache Storm distributed stream processing platform is installed and properly configured on each distributed edge server; the plurality of distributed edge servers includes one or more service modules installed on each distributed edge server and configured to process the one or more tasks; and the plurality of distributed edge servers includes a master distributed edge server and a plurality of slave distributed edge servers; and a scheduler is installed on the master distributed edge server and configured to distribute the one or more tasks to the plurality of distributed edge servers.

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