• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3892-3912
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• 2021

The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

• 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.

• 전자통신동향분석
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• 제37권4호
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• pp.46-59
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• 2022

The 5G MEC (Multi-access Edge Computing) technology offers network and computing functionalities that allow application services to improve in terms of network delay, bandwidth, and security, by locating the application servers closer to the users at the edge nodes within the 5G network. To offer its interoperability within various networks and user equipment, standardization of the 5G MEC technology has been advanced in ETSI, 3GPP, and ITU-T, primarily for the MEC platform, transport support, and MEC federation. This article offers a brief review of the standardization activities for 5G MEC technology and the details about the system architecture and functionalities developed accordingly.

• 인터넷정보학회논문지
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• 제21권4호
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• pp.1-8
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• 2020

5G 목표 중 하나인 초신뢰성 저지연 통신에 도달하기 위해 멀티액세스 엣지 컴퓨팅 패러다임이 탄생했다. 이 패러다임은 클라우드 컴퓨팅 기술을 네트워크 엣지에 더 가깝게 하며 서비스 지연 시간을 줄이기 위해서는 네트워크 엣지에 있는 여러 Edge Cloud에서 서비스 호스팅된다. 모바일 사용자의 경우 서비스 품질 유지를 위해 서비스를 가장 적합한 Edge Cloud로 마이그레이션하는 것은 중요하고 고이동성 시나리오에서는 서비스 마이그레이션 문제가 더욱 복잡해진다. 고정 이동 경로에서 사용자 이동성과 Edge Cloud 선택에 대한 어떤 영향을 미치는 건지 관찰하는 것이 이 연구의 목표다. Mobility-Aware Service Migration (MASM)은 고이동성 시나리오 동안 라우팅 비용과 서비스 마이그레이션 비용이라는 두 가지 주요 매개변수를 기반으로 서비스 마이그레이션을 최적화하기 위해 제안된다. 제안된 알고리즘을 기존의 그리디 알고리즘과 비교하여 평가한다.

• ETRI Journal
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• 제44권4호
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• pp.588-598
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• 2022

Since multi-access edge computing (MEC) was established as a key enabler of 5G, MEC based on 5G networks (5G MEC) has been perceived as a new business opportunity for many industry players, including telecom operators. Numerous 5G MEC cooperation announcements among companies playing their respective roles in the MEC ecosystem have been recently released. However, because of cooperative and competitive relationships among key players in the MEC ecosystem and the uncertainty of 5G MEC, the announcement of 5G MEC cooperation can negatively affect the telecom operators' firm value. This study investigates the market reaction to announcements of 5G MEC cooperation for telecom operators using an event study methodology. The empirical results show that announcements of 5G MEC cooperation have a negative impact on the telecom operators' firm value. The results also show that the early deployment of 5G networks may reduce the negative impact of 5G MEC cooperation announcements by reducing uncertainty.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.16-24
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• 2024

With an increase in the relevance of next-generation integrated networking environments, the need to effectively utilize advanced networking techniques also increases. Specifically, integrating Software-Defined Networking (SDN) with Multi-access Edge Computing (MEC) is critical for enhancing network flexibility and addressing challenges such as security vulnerabilities and complex network management. SDN enhances operational flexibility by separating the control and data planes, introducing management complexities. This paper proposes a reinforcement learning-based network path optimization strategy within SDN environments to maximize performance, minimize latency, and optimize resource usage in MEC settings. The proposed Enhanced Proximal Policy Optimization (PPO)-based scheme effectively selects optimal routing paths in dynamic conditions, reducing average delay times to about 60 ms and lowering energy consumption. As the proposed method outperforms conventional schemes, it poses significant practical applications.

• 인터넷정보학회논문지
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• 제24권2호
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• pp.1-10
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• 2023

The Multi-access Edge Computing (MEC) paradigm equips network edge telecommunication infrastructure with cloud computing resources. It seeks to transform the edge into an IT services platform for hosting resource-intensive and delay-stringent services for mobile users, thereby significantly enhancing perceived service quality of experience. However, erratic user mobility impedes seamless service continuity as well as satisfying delay-stringent service requirements, especially as users roam farther away from the serving MEC resource, which deteriorates quality of experience. This work proposes a deep reinforcement learning based service mobility management approach for ensuring seamless migration of service instances along user mobility. The proposed approach focuses on the problem of selecting the optimal MEC resource to host services for high mobility users, thereby reducing service migration rejection rate and enhancing service availability. Efficacy of the proposed approach is confirmed through simulation experiments, where results show that on average, the proposed scheme reduces service delay by 8%, task computing time by 36%, and migration rejection rate by more than 90%, when comparing to a baseline scheme.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.114-116
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• 2022

The advent of the Multi-access Edge Computing (MEC) paradigm allows mobile users to offload resource-intensive and delay-stringent services to nearby servers, thereby significantly enhancing the quality of experience. Due to erratic roaming of mobile users in the network environment, maintaining maximum quality of experience becomes challenging as they move farther away from the serving edge server, particularly due to the increased latency resulting from the extended distance. The services could be migrated, under policies obtained using Deep Reinforcement Learning (DRL) techniques, to an optimal edge server, however, this operation incurs significant costs in terms of service downtime, thereby adversely affecting service quality of experience. Thus, this study addresses the service mobility problem of deciding whether to migrate and where to migrate the service instance for maximized migration benefits and marginal service downtime.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1935-1950
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• 2023

The arrival of the Internet of Things and 5G technology enables users to rely on edge computing platforms to process massive data. Data sharing based on edge computing refines the efficiency of data collection and analysis, saves the communication cost of data transmission back and forth, but also causes the privacy leakage of a lot of user data. Based on attribute-based encryption and blockchain technology, we design a fine-grained access control scheme for data in edge computing, which has the characteristics of verifiability, support for outsourcing decryption and user attribute revocation. User attributes are authorized by multi-attribute authorization, and the calculation of outsourcing decryption in attribute encryption is completed by edge server, which reduces the computing cost of end users. Meanwhile, We implemented the user's attribute revocation process through the dual encryption process of attribute authority and blockchain. Compared with other schemes, our scheme can manage users' attributes more flexibly. Blockchain technology also ensures the verifiability in the process of outsourcing decryption, which reduces the space occupied by ciphertext compared with other schemes. Meanwhile, the user attribute revocation scheme realizes the dynamic management of user attribute and protects the privacy of user attribute.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.7-15
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• 2024

6G network technology represents the next generation of communications, supporting high-speed connectivity, ultra-low latency, and integration with cutting-edge technologies, such as the Internet of Things (IoT), virtual reality, and autonomous vehicles. These advancements promise to drive transformative changes in digital society. However, as technology progresses, the demand for efficient data transmission and energy management between smart devices and network equipment also intensifies. A significant challenge within 6G networks is the optimization of interactions between satellites and smart devices. This study addresses this issue by introducing a new game theory-based technique aimed at minimizing system-wide energy consumption and latency. The proposed technique reduces the processing load on smart devices and optimizes the offloading decision ratio to effectively utilize the resources of Low-Earth Orbit (LEO) satellites. Simulation results demonstrate that the proposed technique achieves a 30% reduction in energy consumption and a 40% improvement in latency compared to existing methods, thereby significantly enhancing performance.