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Hui Huang - IEEE Xplore Author Profile
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Hui Huang

Publications
8
Citations
66
Publications by Year
20202024
Co-Authors:
Show All Co-Authors (8)
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Hui Huang

Affiliation

Faculty of Computer Science
Dalhousie University
Halifax, NS, Canada

Publication Topics

Biography

Hui Huang (Member, IEEE) is currently the Ph.D. student with the Faculty of Computer Science, Dalhousie University, Canada.(Based on document published on 24 September 2021).
Publications
8
Citations
66
Publications by Year
20202024
Co-Authors:
Show All Co-Authors (8)
Author's Published Works

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DAS: A DRL-Based Scheme for Workload Allocation and Worker Selection in Distributed Coded Machine Learning

Yitong Zhou;Qiang Ye;Hui Huang

IEEE Internet of Things Journal
Year: 2024 | Volume: 11, Issue: 15 | Journal Article |
Cited by: Papers (1)
Machine learning (ML) has been widely applied to successfully address a variety of different problems across diverse domains, such as robotics, healthcare, and finance. However, high-complexity ML algorithms often require overlong computation time, which significantly impacts their feasibility. Distributed ML (DML) has been used to tackle the slow computation problem with high-complexity ML algori...Show More

DAS: A DRL-Based Scheme for Workload Allocation and Worker Selection in Distributed Coded Machine Learning

Yitong Zhou;Qiang Ye;Hui Huang

Year: 2024 | Volume: 11, Issue: 15 | Journal Article |

RLC: A Reinforcement Learning Based Charging Scheme for Battery Swap Stations

Yutao Xu;Qiang Ye;Yujie Tang;Hui Huang;Kamran Sattar Awaisi

GLOBECOM 2023 - 2023 IEEE Global Communications Conference
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Over the past decade, the Electric Vehicle (EV) market has witnessed remarkable expansion. Nevertheless, apprehensions regarding prolonged charging durations frequently contribute to range anxiety. Battery Swap Station (BSS) is a promising solution to the range anxiety problem. Typically, when an EV arrives at a BSS, the depleted battery in the EV is replaced with a fully charged one, then the dep...Show More

RLC: A Reinforcement Learning Based Charging Scheme for Battery Swap Stations

Yutao Xu;Qiang Ye;Yujie Tang;Hui Huang;Kamran Sattar Awaisi

Year: 2023 | Conference Paper |

DRL-Based Workload Allocation for Distributed Coded Machine Learning

Yitong Zhou;Qiang Ye;Hui Huang

ICC 2023 - IEEE International Conference on Communications
Year: 2023 | Conference Paper |
Cited by: Papers (5)
Over the past years, Distributed Machine Learning (DML) has been employed to tackle the high complexity problem with many Machine Learning (ML) algorithms. With DML, the original computation task involved in an ML algorithm is first split into multiple subtasks, which are forwarded to a group of computing devices in a distributed environment. Thereafter, the subtask results are collected in order ...Show More

DRL-Based Workload Allocation for Distributed Coded Machine Learning

Yitong Zhou;Qiang Ye;Hui Huang

Year: 2023 | Conference Paper |

On the Placement of Edge Servers in Mobile Edge Computing

Haotian Liu;Shiyun Wang;Hui Huang;Qiang Ye

2023 International Conference on Computing, Networking and Communications (ICNC)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
With the wide deployment of mobile devices, a variety of different computation-intensive applications for mobile platforms, such as online games and virtual augmented reality, have emerged. However, due to the limited computation resources, mobile devices struggle to complete the required computation in time. One potential solution to the problem is mobile edge computing. Over the past years, ther...Show More

On the Placement of Edge Servers in Mobile Edge Computing

Haotian Liu;Shiyun Wang;Hui Huang;Qiang Ye

Year: 2023 | Conference Paper |

6G-Empowered Offloading for Realtime Applications in Multi-Access Edge Computing

Hui Huang;Qiang Ye;Yitong Zhou

IEEE Transactions on Network Science and Engineering
Year: 2023 | Volume: 10, Issue: 3 | Journal Article |
Cited by: Papers (14)
Multi-access Edge Computing (MEC) is a promising solution to the resource shortage problem on mobile devices. With MEC, a fraction of the computational tasks on mobile devices could be offloaded to edge servers. Over the past years, a series of machine learning based offloading methods for MEC have been proposed to reduce the completion time of computational tasks. However, most of the existing me...Show More

6G-Empowered Offloading for Realtime Applications in Multi-Access Edge Computing

Hui Huang;Qiang Ye;Yitong Zhou

Year: 2023 | Volume: 10, Issue: 3 | Journal Article |

Deadline-Aware Task Offloading With Partially-Observable Deep Reinforcement Learning for Multi-Access Edge Computing

Hui Huang;Qiang Ye;Yitong Zhou

IEEE Transactions on Network Science and Engineering
Year: 2022 | Volume: 9, Issue: 6 | Journal Article |
Cited by: Papers (24)
Over the past years, computationally-intensive mobile applications, such as interactive games and augmented reality, have gained enormous popularity. This phenomenon has placed a serious burden on mobile devices with limited computation resources and constrained battery capacity. Multi-access Edge Computing (MEC) is proposed to solve the problem by offloading part of the computation tasks from mob...Show More

Deadline-Aware Task Offloading With Partially-Observable Deep Reinforcement Learning for Multi-Access Edge Computing

Hui Huang;Qiang Ye;Yitong Zhou

Year: 2022 | Volume: 9, Issue: 6 | Journal Article |

Deep Reinforcement Learning Based Admission Control for Throughput Maximization in Mobile Edge Computing

Yitong Zhou;Qiang Ye;Hui Huang;Hongwei Du

2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
With the development of wireless network technologies, such as LTE/5G, Mobile Cloud Computing (MCC) has been proposed as a solution for mobile devices that need to carry out high-complexity computation with limited resources. Technically, with MCC, high-complexity computation tasks are offloaded from mobile devices to cloud servers. However, MCC does not work well for time-sensitive mobile applica...Show More

Deep Reinforcement Learning Based Admission Control for Throughput Maximization in Mobile Edge Computing

Yitong Zhou;Qiang Ye;Hui Huang;Hongwei Du

Year: 2021 | Conference Paper |

Reinforcement Learning Based Offloading for Realtime Applications in Mobile Edge Computing

Hui Huang;Qiang Ye;Hongwei Du

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
Year: 2020 | Conference Paper |
Cited by: Papers (15)
Energy consumption is one of the most important issues for mobile devices such as smartphones and laptops. For mobile devices that execute multiple computation-intensive or delay-sensitive applications simultaneously, Mobile Edge Computing (MEC) based offloading provides a promising solution to the energy problem. However, blindly offloading all tasks to MEC servers is not the best choice because ...Show More

Reinforcement Learning Based Offloading for Realtime Applications in Mobile Edge Computing

Hui Huang;Qiang Ye;Hongwei Du

Year: 2020 | Conference Paper |

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