QoS-Aware Joint Task Scheduling and Resource Allocation in Vehicular Edge Computing
Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU), aiming to thereby reduce the processing delay and resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to a...
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| Abstract | Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU), aiming to thereby reduce the processing delay and resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications, resulting in suboptimal offloading policies. In this paper we present FEVEC, a Fast and Energy-efficient VEC framework, with the objective of realizing an optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirements. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decisions and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC. First, vehicle prejudgment is proposed to meet the requirements of different applications by considering the maximum tolerance delay related to the current vehicle speed. Second, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Finally, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on real and simulated vehicle trajectories verify that the average QoS value of MOV is improved by 20% compared with the state-of-the-art VEC mechanism. |
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| AbstractList | Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU), aiming to thereby reduce the processing delay and resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications, resulting in suboptimal offloading policies. In this paper we present FEVEC, a Fast and Energy-efficient VEC framework, with the objective of realizing an optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirements. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decisions and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC. First, vehicle prejudgment is proposed to meet the requirements of different applications by considering the maximum tolerance delay related to the current vehicle speed. Second, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Finally, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on real and simulated vehicle trajectories verify that the average QoS value of MOV is improved by 20% compared with the state-of-the-art VEC mechanism.Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU), aiming to thereby reduce the processing delay and resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications, resulting in suboptimal offloading policies. In this paper we present FEVEC, a Fast and Energy-efficient VEC framework, with the objective of realizing an optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirements. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decisions and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC. First, vehicle prejudgment is proposed to meet the requirements of different applications by considering the maximum tolerance delay related to the current vehicle speed. Second, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Finally, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on real and simulated vehicle trajectories verify that the average QoS value of MOV is improved by 20% compared with the state-of-the-art VEC mechanism. Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU), aiming to thereby reduce the processing delay and resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications, resulting in suboptimal offloading policies. In this paper we present FEVEC, a Fast and Energy-efficient VEC framework, with the objective of realizing an optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirements. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decisions and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC. First, vehicle prejudgment is proposed to meet the requirements of different applications by considering the maximum tolerance delay related to the current vehicle speed. Second, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Finally, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on real and simulated vehicle trajectories verify that the average QoS value of MOV is improved by 20% compared with the state-of-the-art VEC mechanism. |
| Audience | Academic |
| Author | Su, Meijia Li, Jiangtao Cao, Chenhong Dai, Miaoling Duan, Shengyu Li, Yufeng |
| AuthorAffiliation | 1 School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China 2 Purple Mountain Laboratories, Nanjing 211111, China |
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| Cites_doi | 10.1109/TSC.2021.3064579 10.1016/j.comcom.2022.04.006 10.1109/ICPADS56603.2022.00090 10.1109/ICCC49849.2020.9238970 10.3390/s21082628 10.1109/TVT.2017.2714704 10.1016/j.phycom.2022.101867 10.1016/j.comcom.2019.11.019 10.1109/TITS.2020.2997832 10.1109/TVT.2019.2917890 10.1109/TITS.2016.2529000 10.1109/MVT.2017.2668838 10.1109/JIOT.2021.3116108 10.1007/s11036-020-01624-1 10.1109/JIOT.2018.2876298 10.1109/4235.996017 10.1109/MVT.2018.2882873 10.1109/OJITS.2022.3142065 10.1109/TVT.2022.3174530 10.1007/s11227-019-03011-4 10.1109/INFOCOM42981.2021.9488886 10.1109/TVT.2020.2999617 10.1109/MNET.2019.1900120 10.1109/TNSE.2021.3106955 10.1109/TVT.2019.2935450 10.1109/TVT.2019.2905432 10.1080/01621459.1970.10481112 |
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| References | Huang (ref_17) 2021; 9 Pang (ref_30) 2021; 8 Liu (ref_6) 2019; 68 Zhou (ref_14) 2019; 68 Zhao (ref_12) 2016; 17 White (ref_18) 1970; 65 ref_19 Zhao (ref_5) 2019; 68 Karimi (ref_28) 2022; 189 Liu (ref_2) 2021; 26 Wan (ref_8) 2020; 76 Deb (ref_15) 2002; 6 Lin (ref_25) 2022; 3 Dai (ref_10) 2018; 6 Zhang (ref_29) 2022; 71 Zhang (ref_1) 2017; 12 ref_24 ref_23 ref_22 Yaqoob (ref_4) 2020; 34 ref_20 Wu (ref_21) 2022; 55 Feng (ref_16) 2017; 66 Ning (ref_26) 2020; 22 ref_27 ref_9 Luo (ref_3) 2021; 15 Wu (ref_11) 2020; 150 Ning (ref_13) 2018; 14 Yuan (ref_31) 2020; 69 ref_7 |
| References_xml | – volume: 15 start-page: 2897 year: 2021 ident: ref_3 article-title: Minimizing the Delay and Cost of Computation Offloading for Vehicular Edge Computing publication-title: IEEE Trans. Serv. Comput. doi: 10.1109/TSC.2021.3064579 – volume: 189 start-page: 193 year: 2022 ident: ref_28 article-title: Task offloading in vehicular edge computing networks via deep reinforcement learning publication-title: Comput. Commun. doi: 10.1016/j.comcom.2022.04.006 – ident: ref_23 doi: 10.1109/ICPADS56603.2022.00090 – ident: ref_24 doi: 10.1109/ICCC49849.2020.9238970 – ident: ref_9 doi: 10.3390/s21082628 – volume: 66 start-page: 10660 year: 2017 ident: ref_16 article-title: AVE: Autonomous vehicular edge computing framework with ACO-based scheduling publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2017.2714704 – volume: 55 start-page: 101867 year: 2022 ident: ref_21 article-title: Task offloading for vehicular edge computing with imperfect CSI: A deep reinforcement approach publication-title: Phys. Commun. doi: 10.1016/j.phycom.2022.101867 – volume: 150 start-page: 245 year: 2020 ident: ref_11 article-title: Efficient task scheduling for servers with dynamic states in vehicular edge computing publication-title: Comput. Commun. doi: 10.1016/j.comcom.2019.11.019 – volume: 22 start-page: 2212 year: 2020 ident: ref_26 article-title: Intelligent edge computing in internet of vehicles: A joint computation offloading and caching solution publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2020.2997832 – volume: 68 start-page: 7944 year: 2019 ident: ref_5 article-title: Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2019.2917890 – volume: 17 start-page: 2854 year: 2016 ident: ref_12 article-title: High-Precision Vehicle Navigation in Urban Environments using a MEM’s IMU and Single-frequency GPS Receiver publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2016.2529000 – volume: 12 start-page: 36 year: 2017 ident: ref_1 article-title: Mobile-edge computing for vehicular networks: A promising network paradigm with predictive off-loading publication-title: IEEE Veh. Technol. Mag. doi: 10.1109/MVT.2017.2668838 – volume: 9 start-page: 8852 year: 2021 ident: ref_17 article-title: Revenue and energy efficiency-driven delay constrained computing task offloading and resource allocation in a vehicular edge computing network: A deep reinforcement learning approach publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2021.3116108 – ident: ref_27 – volume: 26 start-page: 1145 year: 2021 ident: ref_2 article-title: Vehicular edge computing and networking: A survey publication-title: Mob. Netw. Appl. doi: 10.1007/s11036-020-01624-1 – volume: 6 start-page: 4377 year: 2018 ident: ref_10 article-title: Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2018.2876298 – volume: 6 start-page: 182 year: 2002 ident: ref_15 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.996017 – volume: 14 start-page: 54 year: 2018 ident: ref_13 article-title: Mobile edge computing-enabled 5G vehicular networks: Toward the integration of communication and computing publication-title: IEEE Veh. Technol. Mag. doi: 10.1109/MVT.2018.2882873 – volume: 3 start-page: 7 year: 2022 ident: ref_25 article-title: Multi-Access Edge Computing-Based Vehicle-Vehicle-RSU Data Offloading Over the Multi-RSU-Overlapped Environment publication-title: IEEE Open J. Intell. Transp. Syst. doi: 10.1109/OJITS.2022.3142065 – volume: 71 start-page: 8877 year: 2022 ident: ref_29 article-title: Joint Resource Allocation and Multi-Part Collaborative Task Offloading in MEC Systems publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2022.3174530 – volume: 76 start-page: 2518 year: 2020 ident: ref_8 article-title: Efficient computation offloading for Internet of Vehicles in edge computing-assisted 5G networks publication-title: J. Supercomput. doi: 10.1007/s11227-019-03011-4 – ident: ref_7 doi: 10.1109/INFOCOM42981.2021.9488886 – volume: 69 start-page: 9041 year: 2020 ident: ref_31 article-title: A Joint Service Migration and Mobility Optimization Approach for Vehicular Edge Computing publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2020.2999617 – volume: 34 start-page: 174 year: 2020 ident: ref_4 article-title: Autonomous Driving Cars in Smart Cities: Recent Advances, Requirements, and Challenges publication-title: IEEE Netw. doi: 10.1109/MNET.2019.1900120 – ident: ref_19 – ident: ref_22 – ident: ref_20 – volume: 8 start-page: 3179 year: 2021 ident: ref_30 article-title: A Smart Network Resource Management System for High Mobility Edge Computing in 5G Internet of Vehicles publication-title: IEEE Trans. Netw. Sci. Eng. doi: 10.1109/TNSE.2021.3106955 – volume: 68 start-page: 11158 year: 2019 ident: ref_6 article-title: Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2019.2935450 – volume: 68 start-page: 5087 year: 2019 ident: ref_14 article-title: Energy-Efficient Edge Computing Service Provisioning for Vehicular Networks: A Consensus ADMM Approach publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2019.2905432 – volume: 65 start-page: 635 year: 1970 ident: ref_18 article-title: Tables of normal percentile points publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1970.10481112 |
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| Title | QoS-Aware Joint Task Scheduling and Resource Allocation in Vehicular Edge Computing |
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