Deep Learning-Based Task Offloading for Vehicular Edge Computing

Zeng, Feng; Liu, Chengsheng; Tangjiang, Junzhe; Li, Wenjia

doi:10.1007/978-3-030-86137-7_32

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…Proper utilization of resources is directly proportional to the success rate of tasks and is the most effective performance comparison criterion. 28,39,42,43 Task success rate is seen as an important success criterion that depends on the efficient use of network tools. According to the edgeCloudSim 39 simulator, the most effective tool for identifying failed tasks is based on accurately detecting incorrect transmissions.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…36 This proposed method is a study conducted to increase the success of offloading operations using the ML-based DT algorithm along with the LR algorithm. 27,28,31,37,38 Based on these studies, we recommend the ML-based DT algorithm. The proposed method uses the edgeCloudSim 39 simulation tool to determine whether the computing device to which the task will be sent has been successful before or not.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…This proposed method is a study conducted to increase the success of offloading operations using the ML‐based DT algorithm along with the LR algorithm 27,28,31,37,38 . Based on these studies, we recommend the ML‐based DT algorithm.…”

Section: System Model and Proposed Methodsmentioning

confidence: 99%

“…Simulation results are thoroughly analyzed in the comparison algorithm section. Another study is proposed by Zeng et al, 28 which suggests a deep learning‐based offloading strategy. The authors propose using a convolutional neural network (CNN)‐based approach to determine which computation device should be offloaded.…”

Section: Related Workmentioning

confidence: 99%

“…Proper utilization of resources is directly proportional to the success rate of tasks and is the most effective performance comparison criterion 28,39,42,43 . Task success rate is seen as an important success criterion that depends on the efficient use of network tools.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Decision tree‐based task offloading in vehicle edge computing

Tay,

Şentürk

2024

Concurrency and Computation

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SummaryThere are significant developments in the Internet of Vehicles (IoV) field, and the requirements needed in this area are increasing rapidly. When these needs are examined in the near future, it appears that the demand for connected, autonomous, shared, and electric vehicles will increase. Therefore, fundamental problems such as big data flow and storage will arise in the IoV field. Another problem is the delay sensitivity of IoVs and the need to minimize data loss. The use of edge computing (EC) tools can play an important role in obtaining effective solutions to overcome these problems. Delay, bandwidth, and energy consumption rate, which are important qualities in EC systems, emerge as a problem that needs to be improved for delay‐sensitive systems. These improvements belong to the category of nonlinear challenging problems. Effective optimization or machine learning methods can be used to improve these types of problems. In this study, a two‐stage machine learning method is proposed for a more efficient task completion rate and service time. According to the proposed method, in the first stage, the decision tree algorithm is used to select the computing tool to which the task will be sent, and the decision is made on which computing tool to send it to. In the second stage, a linear regression‐based classification method is used to select a delay‐sensitive computing tool. The performance analysis of the proposed method was made using the edgeCloudSim simulation tool, and according to the results obtained, the proposed method provides better results than other algorithms in the literature.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Section: System Model and Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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Decision tree‐based task offloading in vehicle edge computing

Tay,

Şentürk

2024

Concurrency and Computation

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Solving Multi-Task Offloading Problem in V2X with a Machine Learning-Based Online Algorithm

Zhou,

Li,

Chen

et al. 2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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Task-Offloading Strategy Based on Performance Prediction in Vehicular Edge Computing

et al. 2022

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In vehicular edge computing, network performance and computing resources dynamically change, and vehicles should find the optimal strategy for offloading their tasks to servers to achieve a rapid computing service. In this paper, we address the multi-layered vehicle edge-computing framework, where each vehicle can choose one of three strategies for task offloading. For the best offloading performance, we propose a prediction-based task-offloading scheme for the vehicles, in which a deep-learning model is designed to predict the task-offloading result (success/failure) and service delay, and then the predicted strategy with successful task offloading and minimum service delay is chosen as the final offloading strategy. In the proposed model, an automatic feature-generation model based on CNN is proposed to capture the intersection of features to generate new features, avoiding the performance instability caused by manually designed features. The simulation results demonstrate that each part of the proposed model has an important impact on the prediction accuracy, and the proposed scheme has the higher Area Under Curve (AUC) than other methods. Compared with SVM- and MLP-based methods, the proposed scheme has the average failure rate decreased by 21.2% and 6.3%, respectively. It can be seen that our prediction-based scheme can effectively deal with dynamic changes in network performance and computing resources.

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Deep Learning-Based Task Offloading for Vehicular Edge Computing

Cited by 3 publications

References 13 publications

Decision tree‐based task offloading in vehicle edge computing

Decision tree‐based task offloading in vehicle edge computing

Solving Multi-Task Offloading Problem in V2X with a Machine Learning-Based Online Algorithm

Task-Offloading Strategy Based on Performance Prediction in Vehicular Edge Computing

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