• ETRI Journal
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• 제42권5호
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• pp.658-668
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• 2020

In edge computing, most procedures, including data collection, data processing, and service provision, are handled at edge nodes and not in the central cloud. This decreases the processing burden on the central cloud, enabling fast responses to end-device service requests in addition to reducing bandwidth consumption. However, edge nodes have restricted computing, storage, and energy resources to support computation-intensive tasks such as processing deep neural network (DNN) inference. In this study, we analyze the effect of models with single and multiple local exits on DNN inference in an edge-computing environment. Our test results show that a single-exit model performs better with respect to the number of local exited samples, inference accuracy, and inference latency than a multi-exit model at all exit points. These results signify that higher accuracy can be achieved with less computation when a single-exit model is adopted. In edge computing infrastructure, it is therefore more efficient to adopt a DNN model with only one or a few exit points to provide a fast and reliable inference service.

• 한국컴퓨터정보학회논문지
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• 제24권5호
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• pp.49-58
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• 2019

In this paper, we propose an edge cloud platform architecture for implementing smart factory. The edge cloud platform is one of edge computing architecture which is mainly focusing on the efficient computing between IoT devices and central cloud. So far, edge computing has put emphasis on reducing latency, bandwidth and computing cost in areas like smart homes and self-driving cars. On the other hand, in this paper, we suggest not only common functional architecture of edge system but also light weight cloud based architecture to apply to the specialized requirements of smart factory. Cloud based edge architecture has many advantages in terms of scalability and reliability of resources and operation of various independent edge functions compare to typical edge system architecture. To make sure the availability of edge cloud platform in smart factory, we also analyze requirements of smart factory edge. We redefine requirements from a 4M1E(man, machine, material, method, element) perspective which are essentially needed to be digitalized and intelligent for physical operation of smart factory. Based on these requirements, we suggest layered(IoT Gateway, Edge Cloud, Central Cloud) application and data architecture. we also propose edge cloud platform architecture using lightweight container virtualization technology. Finally, we validate its implementation effects with case study. we apply proposed edge cloud architecture to the real manufacturing process and compare to existing equipment engineering system. As a result, we prove that the response performance of the proposed approach was improved by 84 to 92% better than existing method.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제12권11호
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• pp.319-328
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• 2023

에지 컴퓨팅 기술은 클라우드 컴퓨팅과 달리 기기와 사용자와 가까운 곳에서 데이터를 분석하고 판단하여 실시간 서비스, 민감한 데이터 보호, 네트워크 트래픽 감소와 같은 장점을 제공한다. 에지 컴퓨팅 플랫폼의 대표적인 오픈소스인 EdgeX Foundry는 현실 세계의 다양한 장치와 IT 시스템 사이에서 서비스를 제공하는 오픈소스 기반 엣지 미들웨어 플랫폼이다. EdgeX Foundry는 기존의 센싱된 데이터를 다루기 위한 서비스와 함께 카메라 장치를 다루기 위한 서비스를 제공하는데, 이 서비스는 단순 스트리밍 및 카메라 장치 관리만 지원할 뿐 EdgeX 내부에 장치에서 얻은 이미지 데이터를 저장하거나 처리하지 않는다. 본 논문에서는 EdgeX Foundry에서 제공하는 서비스 일부를 응용하여 EdgeX 내부에 이미지 데이터를 저장하고 처리할 수 있는 기법을 제시한다. 제시한 기법을 기반으로 실험 및 성능 평가를 위해 자율주행 분야에서 핵심적으로 사용되는 객체탐지 서비스를 위한 서비스 파이프라인을 만든 후 기존 방법과 비교 분석하였다. 이 실험을 통해 에지 컴퓨팅 플랫폼에서 이미지/동영상 데이터를 저장하고 처리하는 과정 등이 추가되었음에도 기존 방법에 비해 지연시간이 거의 없는 것을 확인할 수 있었다.

• 전자통신동향분석
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• 제39권3호
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• pp.69-78
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• 2024

Cloud computing technology based on centralized high-performance computing has brought about major changes across the information technology industry and led to new paradigms. However, with the rapid development of the industry and increasing need for mass generation and real-time processing of data across various fields, centralized cloud computing is lagging behind the demand. This is particularly critical in emerging technologies such as autonomous driving, the metaverse, and augmented/virtual reality that require the provision of services with ultralow latency for real-time performance. To address existing limitations, distributed and edge cloud computing technologies have recently gained attention. These technologies allow for data to be processed and analyzed closer to their point of generation, substantially reducing the response times and optimizing the network bandwidth usage. We describe distributed and edge cloud computing technologies and explore the latest trends in their standardization.

• 대한임베디드공학회논문지
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• 제15권6호
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• pp.299-305
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• 2020

We have developed a scheduler that additionally consider network performance by extending the Kubernetes developed to manage lots of containers in cloud computing nodes. The network delay adapt characteristics of the compute nodes were learned during server operation and the learned results were utilized to develop placement algorithm by considering the existing measurement units, CPU, memory, and volume together, and it was confirmed that the low delay network service was provided through placement algorithm.

• Journal of Information Processing Systems
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• 제20권3호
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.615-629
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• 2021

With the development of mobile edge computing, how to utilize the computing power of edge computing to effectively and efficiently offload data and to compute offloading is of great research value. This paper studies the computation offloading problem of multi-user and multi-server in mobile edge computing. Firstly, in order to minimize system energy consumption, the problem is modeled by considering the joint optimization of the offloading strategy and the wireless and computing resource allocation in a multi-user and multi-server scenario. Additionally, this paper explores the computation offloading scheme to optimize the overall cost. As the centralized optimization method is an NP problem, the game method is used to achieve effective computation offloading in a distributed manner. The decision problem of distributed computation offloading between the mobile equipment is modeled as a multi-user computation offloading game. There is a Nash equilibrium in this game, and it can be achieved by a limited number of iterations. Then, we propose a distributed computation offloading algorithm, which first calculates offloading weights, and then distributedly iterates by the time slot to update the computation offloading decision. Finally, the algorithm is verified by simulation experiments. Simulation results show that our proposed algorithm can achieve the balance by a limited number of iterations. At the same time, the algorithm outperforms several other advanced computation offloading algorithms in terms of the number of users and overall overheads for beneficial decision-making.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2018년도 제57차 동계학술대회논문집 26권1호
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• pp.89-92
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• 2018

본 논문에서는 Edge computing 환경에서 다수의 노드들로 구성된 네트워크의 디바이스를 효율적으로 관리하기 위한 방법을 제안한다. 기존의 클라이언트-서버 모델은 모든 데이터와 그에 대한 요청을 중심 서버에서 처리하기 때문에, 다수의 노드로부터 생성된 많은 양의 데이터를 처리하는 데 빠른 응답속도를 보장하지 못한다. Edge computing은 분담을 통해 네트워크의 부담을 줄일 수 있는 IoT 네트워크에 적합한 방법으로, 데이터를 전송하고 받는 과정에서 네트워크의 대역폭을 사용하는 대신 서로 연결된 노드들이 협력해서 데이터를 처리하고, 또한 네트워크 말단에서의 데이터 처리가 허용되어 데이터 센터의 부담을 줄일 수 있다. 여러병렬 기계학습 모델 중 본 연구에서는 Stale Synchronous Parallel(SSP) 모델을 이용하여 Edge 노드에서 분산기계 학습에 적용하였다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2018년도 제57차 동계학술대회논문집 26권1호
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• pp.95-96
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• 2018

Edge Computing은 Cloud Computing의 단점을 보완하기 위해 등장 하였으나, 자원 제한을 가지고 있는 Edge Node에서 데이터 분석 및 처리해야 하는 문제점이 있다. 이를 해결하기 위해 K-means clustering 알고리즘과 PCA 기법을 이용해 차원 추축을 이용한 계산비용과 처리시간을 줄이는 기법을 제안하였다. PCA란, 차원 축소 및 데이터 압축에 사용되는 기계학습 알고리즘 중 하나이며, 데이터에서 중요한 정보만 추출해 차원을 줄일 수 있다. 이를 통해 제안한 기법이 기존의 Reduction first clustering second(RFCS) 기법에 비해 성능이 우수한 것을 확인할 수 있었다.

• 한국항행학회논문지
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• 제27권6호
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• pp.770-775
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• 2023

최근 디지털 데이터 양의 기하급수적 증가는 데이터 처리 시스템의 중요성을 부각시켰다. 이 연구는 클라우드 컴퓨팅 (CC; cloud computing), 엣지 컴퓨팅 (EC; edge computing), 그리고 UAV (unmanned aerial vehicle) 기반 지능형 에지 컴퓨팅 (UEC; unmanned aerial vehicle-based intelligent edge computing) 간의 성능을 비교하였으며, 특히 회랑감시와 같은 실시간 대용량 데이터 처리 상황에 초점을 맞추었습니다. UAV 기반 지능형 에지 컴퓨팅은 이동성과 특수 환경에서의 대규모 데이터 처리 및 분석에 높은 효과성을 보인다. 이러한 연구 결과를 바탕으로 각 상황에 맞게 최적화된 시스템 선택 방법론을 제안한다.