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Task Dependency Aware Optimal Resource Allocation for URLLC Edge Network: A Digital Twin Approach Using Finite Blocklength

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Task Dependency Aware Optimal Resource Allocation for URLLC Edge Network: A Digital Twin Approach Using Finite Blocklength. / Awais, Muhammad; Pervaiz, Haris; Ni, Qiang et al.
In: IEEE Transactions on Green Communications and Networking, 12.07.2024, p. 2473-2400.

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Awais M, Pervaiz H, Ni Q, Yu W. Task Dependency Aware Optimal Resource Allocation for URLLC Edge Network: A Digital Twin Approach Using Finite Blocklength. IEEE Transactions on Green Communications and Networking. 2024 Jul 12;2473-2400. 10596126. Epub 2024 Jul 12. doi: 10.1109/tgcn.2024.3425442

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@article{d6380b88efd24fce8cc940ed1288790c,
title = "Task Dependency Aware Optimal Resource Allocation for URLLC Edge Network:: A Digital Twin Approach Using Finite Blocklength",
abstract = "Next-generation wireless networks envision ubiquitous access and computational capabilities by seamlessly integrating aerial and terrestrial networks. Digital twin (DT) technology emerges as a proactive and cost-effective approach for resourcelimited networks. Mobile edge computing (MEC) is pivotal in facilitating mobile offloading, particularly under the demanding constraints of ultra-reliable and low-latency communication (URLLC). This study proposes an advanced bisection samplingbased stochastic solution enhancement (BSSE) algorithm to minimize the systemfs overall energy-time cost by jointly optimizing task offloading and resource allocation strategies. The formulated problem is a mixed-integer nonlinear programming problem due to its inherently combinatorial linkage with task-offloading decisions and strong correlation with resource allocation. The proposed algorithm operates iteratively through the following steps: 1) narrowing the search space through a one-climb policy, 2) developing a closed-form solution for optimal CPU frequency and transmit power, and 3) implementing randomized task offloading, which updates it in the direction of reducing objective value. The scalability of the proposed algorithm is also analyzed for a two-device model, which is subsequently extended to multiple devices. Comparative analysis against benchmark schemes reveals that our approach reduces total energy-time cost by 15.35% to 33.12% when weighting parameter ∂ λ k2 k2 is increased from 0.1 to 0.3, respectively.",
author = "Muhammad Awais and Haris Pervaiz and Qiang Ni and Wenjuan Yu",
year = "2024",
month = jul,
day = "12",
doi = "10.1109/tgcn.2024.3425442",
language = "English",
pages = "2473--2400",
journal = "IEEE Transactions on Green Communications and Networking",
issn = "2473-2400",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Task Dependency Aware Optimal Resource Allocation for URLLC Edge Network:

T2 - A Digital Twin Approach Using Finite Blocklength

AU - Awais, Muhammad

AU - Pervaiz, Haris

AU - Ni, Qiang

AU - Yu, Wenjuan

PY - 2024/7/12

Y1 - 2024/7/12

N2 - Next-generation wireless networks envision ubiquitous access and computational capabilities by seamlessly integrating aerial and terrestrial networks. Digital twin (DT) technology emerges as a proactive and cost-effective approach for resourcelimited networks. Mobile edge computing (MEC) is pivotal in facilitating mobile offloading, particularly under the demanding constraints of ultra-reliable and low-latency communication (URLLC). This study proposes an advanced bisection samplingbased stochastic solution enhancement (BSSE) algorithm to minimize the systemfs overall energy-time cost by jointly optimizing task offloading and resource allocation strategies. The formulated problem is a mixed-integer nonlinear programming problem due to its inherently combinatorial linkage with task-offloading decisions and strong correlation with resource allocation. The proposed algorithm operates iteratively through the following steps: 1) narrowing the search space through a one-climb policy, 2) developing a closed-form solution for optimal CPU frequency and transmit power, and 3) implementing randomized task offloading, which updates it in the direction of reducing objective value. The scalability of the proposed algorithm is also analyzed for a two-device model, which is subsequently extended to multiple devices. Comparative analysis against benchmark schemes reveals that our approach reduces total energy-time cost by 15.35% to 33.12% when weighting parameter ∂ λ k2 k2 is increased from 0.1 to 0.3, respectively.

AB - Next-generation wireless networks envision ubiquitous access and computational capabilities by seamlessly integrating aerial and terrestrial networks. Digital twin (DT) technology emerges as a proactive and cost-effective approach for resourcelimited networks. Mobile edge computing (MEC) is pivotal in facilitating mobile offloading, particularly under the demanding constraints of ultra-reliable and low-latency communication (URLLC). This study proposes an advanced bisection samplingbased stochastic solution enhancement (BSSE) algorithm to minimize the systemfs overall energy-time cost by jointly optimizing task offloading and resource allocation strategies. The formulated problem is a mixed-integer nonlinear programming problem due to its inherently combinatorial linkage with task-offloading decisions and strong correlation with resource allocation. The proposed algorithm operates iteratively through the following steps: 1) narrowing the search space through a one-climb policy, 2) developing a closed-form solution for optimal CPU frequency and transmit power, and 3) implementing randomized task offloading, which updates it in the direction of reducing objective value. The scalability of the proposed algorithm is also analyzed for a two-device model, which is subsequently extended to multiple devices. Comparative analysis against benchmark schemes reveals that our approach reduces total energy-time cost by 15.35% to 33.12% when weighting parameter ∂ λ k2 k2 is increased from 0.1 to 0.3, respectively.

U2 - 10.1109/tgcn.2024.3425442

DO - 10.1109/tgcn.2024.3425442

M3 - Journal article

SP - 2473

EP - 2400

JO - IEEE Transactions on Green Communications and Networking

JF - IEEE Transactions on Green Communications and Networking

SN - 2473-2400

M1 - 10596126

ER -