Resource Allocation for Multi-User MEC System: Machine Learning Approaches

Zhang, Yong; Xiao-min, Zhou; Teng, Yinglei; Fang, Jinghui; Zheng, Wei

doi:10.1109/csci46756.2018.00159

Cited by 10 publications

(2 citation statements)

References 18 publications

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“…The dataset used in the study was represented by data collected from mobile devices. Zhang et al 23 conducted a study based on artificial neural network (ANN) to improve the energy consumption rate and task delay. Effective resource allocation was targeted in the scenario of a multi‐user MEC server, and a more efficient solution was found in terms of delay and energy consumption, and the dataset was randomly generated.…”

Section: Related Workmentioning

confidence: 99%

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: Related Workmentioning

confidence: 99%

Decision tree‐based task offloading in vehicle edge computing

Tay,

Şentürk

2024

Concurrency and Computation

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“…The CPU utilization of mobile devices is idle or lower, resulting in inefficient usage and wasted resources to the specific range. Device-to-device (D2D) transmission is a novel technique, which enables the terminal device to directly interact via shared resources controlled by the base station (BS) [13,14]. D2D transmission might decrease BS and transmission delay; however, it saves energy consumption and expands the transmission range.…”

Section: Introductionmentioning

confidence: 99%

Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

Abdullaev,

Prodanova,

Aruna Bhaskar

et al. 2023

Computers, Materials &Amp; Continua

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Recently, computation offloading has become an effective method for overcoming the constraint of a mobile device (MD) using computationintensive mobile and offloading delay-sensitive application tasks to the remote cloud-based data center. Smart city benefitted from offloading to edge point. Consider a mobile edge computing (MEC) network in multiple regions. They comprise N MDs and many access points, in which every MD has M independent real-time tasks. This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization (TORA-DLSGO) algorithm. The proposed TORA-DLSGO technique addresses the resource management issue in the MEC server, which enables an optimum offloading decision to minimize the system cost. In addition, an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted resources. The TORA-DLSGO technique uses the deep belief network (DBN) model for optimum offloading decision-making. Finally, the SGO algorithm is used for the parameter tuning of the DBN model. The simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

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