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A cache placement algorithm based on comprehensive utility in big data multi-access edge computing
( Yanpei Liu ),( Wei Huang ),( Li Han ),( Liping Wang ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.11
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.
멀티 액세스 에지 컴퓨팅에서 퍼지 로직을 사용한 효율적인 부하 관리 기법
호씬 엠디 딜로와르,턴지나 솔탄아,호씬 엠디 알럼길,이가원,허의남 한국정보과학회 2020 정보과학회 컴퓨팅의 실제 논문지 Vol.26 No.11
Multi-Access Edge Computing (MEC) is a new leading technology that enhances the performance of the 5G networks. However, it faces some challenges in determining the processing location of the offloaded task, because it is very difficult to predict in advance the demand of end users. Also, without the load management an MEC server is overloaded for managing too many service requests and applications. As a result, the quality-of-services (QoS) is deteriorated because of the long execution time and task failure rate. In this study, we have used the fuzzy orchestrator based load balancing (FOLB) technique to resolve the above challenges. Our efficient FOLB approach selects a target server for the task computing between the MEC server and the remote cloud server by using the fuzzy rules. The simulation results corroborate that our proposed FOLB scheme can significantly reduce the average service time, task failure rate, and WAN delay compared with the three reference schemes. 멀티 액세스 에지 컴퓨팅(Multi-Access Edge Computing, MEC)은 5G 네트워크에 대한 성능 향상을 위한 기술로 각광받고 있다. 그러나 이 환경에서는 적재 적소에 테스크를 배정하지 못할 경우 오히려 서비스 실행 시간이 길어지고 작업 실패율이 올라가 서비스 품질(QoS)이 악화될 수 있으며, 최종 사용자의 요구를 예측하기 힘들기 때문에 MEC에서 어느 에지로 작업을 배정할 것인지를 결정하는 것이 가장 중요한 문제로 대두되고 있다. 게다가 부하 분산 없이는 한 MEC에만 부하가 집중되어 병목 현상이 일어날 수 있다. 본 논문에서는 이러한 문제를 해결하기 위해 오케스트레이터 기반 부하 분산 기법(FOLB)을 제안해 이를 해결하고자 한다. 제안하는 FOLB 기법은 퍼지 규칙을 적용하여 로컬 MEC와 원격 클라우드 서버 중 테스크를 처리할 타겟 서버를 선택한다. 시뮬레이션을 통해 제안하는 기법이 다른 관련연구들에 비해 평균 서비스 시간, 테스크 실패율, WAN 지연에 있어 성능 개선을 보여 우수함을 확인하였다.
엣지 컴퓨팅(MEC) 사용의도에 영향을 미치는 주요 요인에 관한 연구
이선주,한경석 한국디지털콘텐츠학회 2019 한국디지털콘텐츠학회논문지 Vol.20 No.3
본 연구는 엣지 컴퓨팅(MEC)의 사용의도를 알아보고자 실증 분석하여 결과를 도출하였다. MEC의 독립변수들로 가용성, 보안성, 다양성, 신속성, 이동성을 선정하였으며, TAM을 활용하여 지각된 사용용이성, 지각된 유용성을 매개변수로 최종적으로 사용의도를 종속변수로 선정하였다. 가설검증은 AMOS와 SPSS 통계프로그램을 사용하였으며, MEC 서비스를 사용 또는 검토 중인 IT업계 종사자들을 대상으로 설문지를 배포하여 총 185부를 분석하였다. 분석한 결과 가용성, 보안성, 신속성, 이동성은 지각된 사용용이성에 긍정적 영향을 주는 것으로 분석되었으며, 가용성, 보안성, 신속성은 지각된 유용성에 유의미한 영향을 주지 못하는 것으로 나타났다. 최종적으로 지각된 사용용이성, 지각된 유용성은 사용의도에 유의미한 영향이 나타나는 것으로 검증이 되었다. The purpose of this study is to empirical analysis the intention of using of Multi-Access Edge Computing. Independent variables of the Multi-Access Edge Computing have been selected as Availability, Security, Diversity, Speed and Mobility, which have been selected by utilizing TAM theory. After selecting the Perceived Ease of Use, Perceived Usefulness as a parameter, Use Intention was been finally selected as a dependent variable. Hypothesis testing was conducted using AMOS and SPSS statistical programs and analyzed a total of 185 parts by distributing a questionnaire to IT industry workers using or reviewing MEC services. The results showed that Availability, Security, Speed and Mobility had a positive effect on Perceived Ease of Use. In addition, Availability, Security and Mobility Perceived Usefulness to use positively. Finally, perceived usefulness and perceived ease of use were found to have significant effects on intention to use.
Video Communication Optimization Using Distributed Edge Computing
Kouichi Genda,Mitsuru Abe,Shohei Kamamura 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
We proposes a backbone network resource optimization algorithm for video communications that use edge computing. In the current video communication architecture, the key component of video communication, called the multi-point control unit (MCU), is deployed in the central cloud server, and its bandwidth consumption in the backbone network becomes enormous as the video resolution and the frequency of use increase. By implementing edge computing, the MCU can be deployed at the entrance node of the backbone network. This allows (i) a local loopback of video traffic at an edge, and (ii) traffic compression (e.g., thumbnailing) between edge nodes. Though these characteristics can reduce the resource consumption of the backbone network, the edge deployment and routing (EDR) problem, classified as NP-hard, should be solved to sufficiently reduce the bandwidth. To solve the NP-hard EDR problem within a feasible period, we propose a divide and merge algorithm based on the linear programming approach. With our algorithm, bandwidth consumption using edge computing is reduced by approximately 30% compared with the current video communication architecture in the world-wide network.
Mobility-Aware Service Migration (MASM) Algorithms for Multi-Access Edge Computing
하지크,리 덕 타이,김문성,추현승 한국인터넷정보학회 2020 인터넷정보학회논문지 Vol.21 No.4
In order to reach Ultra-Reliable Low-Latency communication, one of 5G aims, Multi-access Edge Computing paradigm was born. The idea of this paradigm is to bring cloud computing technologies closer to the network edge. User services are hosted in multiple Edge Clouds, deployed at the edge of the network distributedly, to reduce the service latency. For mobile users, migrating their services to the most proper Edge Clouds for maintaining a Quality of Service is a non-convex problem. The service migration problem becomes more complex in high mobility scenarios. The goal of the study is to observe how user mobility affects the selection of Edge Cloud during a fixed mobility path. Mobility-Aware Service Migration (MASM) is proposed to optimize service migration based on two main parameters: routing cost and service migration cost, during a high mobility scenario. The performance of the proposed algorithm is compared with an existing greedy algorithm.
A reinforcement learning-based network path planning scheme for SDN in multi-access edge computing
MinJung Kim,Ducsun Lim The Institute of Internet 2024 International journal of advanced smart convergenc Vol.13 No.2
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.
A Transport Theoretic Approach for Computational Task Migration in Multi-Access Edge Computing
Sarder Fakhrul Abedin(사르더 파쿠룰 아베딘),Md. Shirajum Munir(엠디 시라줌 무니르),SeokWon Kang(강석원),Choong Seon Hong(홍충선) Korean Institute of Information Scientists and Eng 2019 정보과학회논문지 Vol.46 No.10
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.