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Review of QoS Research Considering Mobility in Mobile Edge Computing
Junseong Nam, Jaehyuk Lee, Duksan Ryu
http://doi.org/10.5626/JOK.2025.52.2.108
Mobile Edge Computing (MEC) is a technology that can enhance performance and reduce latency by performing data processing and cloud computing services not centrally, but at the edge of the network. Mobility is a key characteristic of MEC and a crucial factor in determining the Quality of Service (QoS) for users. The objective of this study is to review current state of research related to providing optimal services in MEC environments by predicting QoS, considering mobility. This study identified research areas related to MEC that aimed to provide optimal services to users while considering mobility, analyzed types of techniques used to address specific problem types, and examined characteristics and improvements made in each category. Based on analysis results, research was categorized into three main areas: security, QoS monitoring, and edge server placement. In the security domain, security techniques have been applied to data preprocessing, while the QoS monitoring domain has utilized collaborative filtering techniques considering data dependencies. The edge server placement domain has employed multi-objective optimization techniques. Through this study, follow-up researchers could better understand mobility-aware QoS research in MEC environments, thereby promoting further research on QoS improvement.
R-FLHE: Robust Federated Learning Framework Against Untargeted Model Poisoning Attacks in Hierarchical Edge Computing
http://doi.org/10.5626/JOK.2023.50.1.94
Federated learning is a server-client based distributed learning strategy that collects only trained model to guarantee data privacy and reduce communication costs. Recently, research is being conducted to prepare for the future IoT ecosystem by combining edge computing and federated learning. However, research considering vulnerabilities and threat is insufficient. In this paper, we propose Robust Federated Learning in Hierarchical Edge computing (R-FLHE), a federated learning framework for robust global model from untargeted model poisoning attacks. R-FLHE can aggregate models learned from clients, evaluate them on the edge server, and score them based on the calculated model’s loss. R-FLHE can maintain robustness of the global model by sending only the model of the edge server with the best score to the cloud server. The R-FLHE proposed in this paper shows robustness in maintaining constant performance for each federated learning round, with performance drop of only 0.81% and 1.88% on average even if attacks occur.
Parallel Optimization of Deep Learning Computation Offloading in Edge Computing Environment
Kwang Yong Shin, Soo-Mook Moon
http://doi.org/10.5626/JOK.2022.49.3.256
Computation offloading to edge servers has been proposed as a solution to performing computation-intensive deep learning applications on devices with low hardware capabilities. However, the deep learning model has to be uploaded to the edge server before computation offloading is possible, a non-trivial assumption in the edge server environment. Incremental offloading of neural networks was proposed as a solution as it can simultaneously upload model and offload computation [1]. Although it reduced the model upload time required for computation offloading, it did not properly handle the model creation overhead, increasing the time required to upload the entire model. This work solves this problem by parallel optimization of model uploading and creation, decreasing the model upload time by up to 30% compared to the previous system.
Service Migration Based on Reinforcement Learning in Vehicular Edge Computing
http://doi.org/10.5626/JOK.2021.48.2.243
As edge computing can provide low latency and real-time services, it is emerging as a promising technology that can lead the Internet of things(IoT). However, user mobility and limited coverage of edge computing result in service interruption and reduce Quality of Service(QoS). Thus, service migration is considered an important issue to guarantee seamless service. In this paper, a migration decision algorithm is proposed using Q-learning, as a reinforcement learning method in the vehicular edge computing environment. The proposed algorithm decides whether or not to migrate and where to migrate in order to meet delay constraint and minimize system cost. In the performance evaluation, we compared propsed algorithm with other algorithms in terms of deciding whether or not to migrate and where to migrate, and our proposed algorithm shows better performance compared to the other algorithms.
The Method Using Reduced Classification Models for Distributed Processing of CNN Models in Multiple Edge Devices
Junyoung Kim, Jongho Jeon, Minkwan Kee, Gi-Ho Park
http://doi.org/10.5626/JOK.2020.47.8.787
Recently, there have been increasing demands for edge computing that processes data at the end of the network wherein data is collected because of various problems such as network load caused by a large amount of data transfer to a cloud server. However, it is difficult for edge devices to use deep learning applications used in cloud servers because most edge devices at the end of the network have limited performance. To overcome these problems, this paper proposes a distributed processing method that uses reduced classification models to jointly perform inferences on multiple edge devices. The reduced classification models have compressed model weights, and perform inferences for some parts of the total classification labels. The experimental results confirmed that the accuracy of the result of the proposed distributed processing method is similar to the accuracy of the result of the original model, even if the proposed reduced classification models had much less parameters than those of the original model.
A Transport Theoretic Approach for Computational Task Migration in Multi-Access Edge Computing
Sarder Fakhrul Abedin, Md. Shirajum Munir, SeokWon Kang, Choong Seon Hong
http://doi.org/10.5626/JOK.2019.46.10.1061
In the present work, the problem of computational task migration in the Multi-Access Edge Computing (MEC) Network has been addressed and the goal is to minimize the computational cost including the task migration cost of the MEC network. Apparently, at first, we have formulated a Hitchcock-Koopmans transportation problem, which corresponds to the task migration from the over-utilized MEC servers to the under-utilized MEC server. Second, we have solved the transportation problem using the Vogel’s Approximation Algorithm (VAM), where the optimal task migration was achieved. Finally, in the simulation, we have demonstrated that the proposed approach significantly outperforms the baseline approach in terms of the task migration cost, average response time, and average queuing delay in the MEC network.
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