An Integrated Process-Network Load Balancing in Edge-Assisted Autonomous Vehicles Using Multimodal Applications With Shared Workloads

Recently, as computing-intensive services such as object recognition for autonomous vehicles have increased, power consumption and computational loads of vehicles have been prominent. To tackle this issue, there have been growing interests in edge computing technology, which offloads workloads of se...

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Main Authors: Sinuk Choi, Pyeongjun Choi, Donghyeon Kim, Jeongho Kwak, Ji-Woong Choi
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/10758623/
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author Sinuk Choi
Pyeongjun Choi
Donghyeon Kim
Jeongho Kwak
Ji-Woong Choi
author_facet Sinuk Choi
Pyeongjun Choi
Donghyeon Kim
Jeongho Kwak
Ji-Woong Choi
author_sort Sinuk Choi
collection DOAJ
description Recently, as computing-intensive services such as object recognition for autonomous vehicles have increased, power consumption and computational loads of vehicles have been prominent. To tackle this issue, there have been growing interests in edge computing technology, which offloads workloads of services to nearby vehicle edge computing (VEC) servers. However, the existing offloading technologies in the VEC server made independent offloading decisions for each service, and they did not consider shared workloads of multimodal vision applications. In this paper, we first aim to capture the intricate workload relationships among multimodal applications in modeling an integrated process-network load balancing for a VEC-assisted autonomous vehicle system. To this end, we formulate an energy minimization problem of a vehicle constrained by outage probability of service requests where the decision variables are (i) dynamic offloading policy between vehicle and VEC server and (ii) onboard CPU clock frequency of a vehicle every time slot. To solve this problem, we leverage Lyapunov optimization to transform the long-term average problem into a slot-by-slot problem. Then, by minimizing the slot-by-slot objective function every time slot, we develop a latency-sensitive energy minimization (LEMON) algorithm. Finally, we evaluate the performance of the proposed algorithm in realistic vehicular network environment, and show that the proposed LEMON algorithm which captures the shared workloads reduces 57% of average queue backlog and 37% of average power consumption compared to the existing algorithm which does not consider shared workload characteristics.
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spelling doaj-art-7b55b79dfd824e4ca872b427ac4959212025-08-20T01:54:11ZengIEEEIEEE Access2169-35362024-01-011217465417466710.1109/ACCESS.2024.350329110758623An Integrated Process-Network Load Balancing in Edge-Assisted Autonomous Vehicles Using Multimodal Applications With Shared WorkloadsSinuk Choi0https://orcid.org/0009-0005-3595-9937Pyeongjun Choi1https://orcid.org/0000-0002-8581-8279Donghyeon Kim2Jeongho Kwak3https://orcid.org/0000-0002-5737-0665Ji-Woong Choi4https://orcid.org/0000-0001-9109-3860Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of KoreaDepartment of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of KoreaDepartment of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of KoreaDepartment of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of KoreaDepartment of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of KoreaRecently, as computing-intensive services such as object recognition for autonomous vehicles have increased, power consumption and computational loads of vehicles have been prominent. To tackle this issue, there have been growing interests in edge computing technology, which offloads workloads of services to nearby vehicle edge computing (VEC) servers. However, the existing offloading technologies in the VEC server made independent offloading decisions for each service, and they did not consider shared workloads of multimodal vision applications. In this paper, we first aim to capture the intricate workload relationships among multimodal applications in modeling an integrated process-network load balancing for a VEC-assisted autonomous vehicle system. To this end, we formulate an energy minimization problem of a vehicle constrained by outage probability of service requests where the decision variables are (i) dynamic offloading policy between vehicle and VEC server and (ii) onboard CPU clock frequency of a vehicle every time slot. To solve this problem, we leverage Lyapunov optimization to transform the long-term average problem into a slot-by-slot problem. Then, by minimizing the slot-by-slot objective function every time slot, we develop a latency-sensitive energy minimization (LEMON) algorithm. Finally, we evaluate the performance of the proposed algorithm in realistic vehicular network environment, and show that the proposed LEMON algorithm which captures the shared workloads reduces 57% of average queue backlog and 37% of average power consumption compared to the existing algorithm which does not consider shared workload characteristics.https://ieeexplore.ieee.org/document/10758623/Shared workloadmultimodal applicationsprocess-network load balancingvehicle edge computingLyapunov optimization
spellingShingle Sinuk Choi
Pyeongjun Choi
Donghyeon Kim
Jeongho Kwak
Ji-Woong Choi
An Integrated Process-Network Load Balancing in Edge-Assisted Autonomous Vehicles Using Multimodal Applications With Shared Workloads
IEEE Access
Shared workload
multimodal applications
process-network load balancing
vehicle edge computing
Lyapunov optimization
title An Integrated Process-Network Load Balancing in Edge-Assisted Autonomous Vehicles Using Multimodal Applications With Shared Workloads
title_full An Integrated Process-Network Load Balancing in Edge-Assisted Autonomous Vehicles Using Multimodal Applications With Shared Workloads
title_fullStr An Integrated Process-Network Load Balancing in Edge-Assisted Autonomous Vehicles Using Multimodal Applications With Shared Workloads
title_full_unstemmed An Integrated Process-Network Load Balancing in Edge-Assisted Autonomous Vehicles Using Multimodal Applications With Shared Workloads
title_short An Integrated Process-Network Load Balancing in Edge-Assisted Autonomous Vehicles Using Multimodal Applications With Shared Workloads
title_sort integrated process network load balancing in edge assisted autonomous vehicles using multimodal applications with shared workloads
topic Shared workload
multimodal applications
process-network load balancing
vehicle edge computing
Lyapunov optimization
url https://ieeexplore.ieee.org/document/10758623/
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