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1
Peng, Kai, Victor C. M. Leung, Xiaolong Xu, Lixin Zheng, Jiabin Wang, and Qingjia Huang. "A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision." Wireless Communications and Mobile Computing 2018 (October 10, 2018): 1–16. http://dx.doi.org/10.1155/2018/8267838.
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Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.
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Cui, Mengmeng, Yiming Fei, and Yin Liu. "A Survey on Secure Deployment of Mobile Services in Edge Computing." Security and Communication Networks 2021 (January 2, 2021): 1–8. http://dx.doi.org/10.1155/2021/8846239.
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Mobile edge computing (MEC) is an emerging technology that is recognized as a key to 5G networks. Because MEC provides an IT service environment and cloud-computing services at the edge of the mobile network, researchers hope to use MEC for secure service deployment, such as Internet of vehicles, Internet of Things (IoT), and autonomous vehicles. Because of the characteristics of MEC which do not have terminal servers, it tends to be deployed on the edge of networks. However, there are few related works that systematically introduce the deployment of MEC. Also, secure service deployment frameworks with MEC are even rare. For this reason, we have conducted a comprehensive and concrete survey of recent research studies on secure deployment. Although numerous research studies and experiments about MEC service deployment have been conducted, there are few systematic summaries that conclude basic concepts and development strategies about secure service deployment of commercial MEC. To make up for the gap, a detailed and complete survey about relative achievements is presented.
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Niewolski, Wojciech, Tomasz W. Nowak, Mariusz Sepczuk, and Zbigniew Kotulski. "Token-Based Authentication Framework for 5G MEC Mobile Networks." Electronics 10, no. 14 (July 18, 2021): 1724. http://dx.doi.org/10.3390/electronics10141724.
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MEC technology provides a distributed computing environment in 5G mobile networks for application and service hosting. It allows customers with different requirements and professional competencies to use the services offered by external suppliers. We consider a service access control framework on 5G MEC networks that is efficient, flexible, and user-friendly. Its central element is the MEC Enabler, which handles AAA requests for stakeholders accessing services hosted on the edge servers. The JSON Web Token (JWT) open standard is a suitable tool for the MEC Enabler to manage access control credentials and transfer them securely between parties. In this paper, in the context of access control, we propose the token reference pattern called JSON MEC Access Token (JMAT) and analyze the effectiveness of its available protection methods in compliance with the standard requirements of MEC-hosted services in 5G networks.
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Lee, Juyong, Jeong-Weon Kim, and Jihoon Lee. "Mobile Personal Multi-Access Edge Computing Architecture Composed of Individual User Devices." Applied Sciences 10, no. 13 (July 5, 2020): 4643. http://dx.doi.org/10.3390/app10134643.
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The Multi-Access Edge Computing (MEC) paradigm provides a promising solution to solve the resource-insufficiency problem in user mobile devices by offloading computation-intensive and delay-sensitive computing services to nearby edge nodes. However, there is a lack of research on the efficient task offloading and mobility support when mobile users frequently move in the MEC environment. In this paper, we propose the mobile personal MEC architecture that utilizes a user’s mobile device as an MEC server (MECS) so that mobile users can receive fast response and continuous service delivery. The results show that the proposed scheme reduces the average service delay and provides efficient task offloading compared to the existing MEC scheme. In addition, the proposed scheme outperforms the existing MEC scheme because the existing mobile user devices are used as MECS, enabling low-latency service and continuous service delivery, even as the mobile user requests and task sizes increase.
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Cao, Qian, Qilin Wu, Bo Liu, Shaowei Zhang, and Yiwen Zhang. "An Optimization Method for Mobile Edge Service Migration in Cyberphysical Power System." Wireless Communications and Mobile Computing 2021 (February 13, 2021): 1–12. http://dx.doi.org/10.1155/2021/6610654.
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To relieve the pressure of processing computation-intensive applications on mobile devices and avoid high latency during data transmission, edge computing is proposed to solve this problem. Mobile edge computing (MEC) allows the deployment of MEC servers at the edge of the network to interact with users on the premise of low transmission delay, thereby improving the quality of service (QoS) for users. However, due to the high mobility of users, with the continuous change of geographical location, when users exceed the signal range of the MEC server, the services they request on the MEC server will also be migrated to other MEC servers. The handoff process may involve high response delays caused by service forwarding, thereby greatly degrading QoS. For the above problems, in this paper, a service migration optimization method based on transmission power control is proposed. First, according to the transmission power of the MEC server, the user’s activity range is divided into multiple subregions based on a Voronoi diagram. Therefore, there is one MEC server in each subregion, and the size of each subregion is adjusted by controlling the transmission power of the MEC server to minimize the number of wireless handoffs and the energy consumption of the MEC server. Then, the particle swarm optimization (PSO) is adopted to solve the above multiobjective optimization problem. Finally, the effectiveness of the proposed method is verified through extensive experiments.
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Pham, Xuan-Qui, Tien-Dung Nguyen, VanDung Nguyen, and Eui-Nam Huh. "Utility-Centric Service Provisioning in Multi-Access Edge Computing." Applied Sciences 9, no. 18 (September 9, 2019): 3776. http://dx.doi.org/10.3390/app9183776.
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Recently, multi-access edge computing (MEC) is a promising paradigm to offer resource-intensive and latency-sensitive services for IoT devices by pushing computing functionalities away from the core cloud to the edge of networks. Most existing research has focused on effectively improving the use of computing resources for computation offloading while neglecting non-trivial amounts of data, which need to be pre-stored to enable service execution (e.g., virtual/augmented reality, video analytics, etc.). In this paper, we, therefore, investigate service provisioning in MEC consisting of two sub-problems: (i) service placement determining services to be placed in each MEC node under its storage capacity constraint, and (ii) request scheduling determining where to schedule each request considering network delay and computation limitation of each MEC node. The main objective is proposed to ensure the quality of experience (QoE) of users, which is also yet to be studied extensively. A utility function modeling user perception of service latency is used to evaluate QoE. We formulate the problem of service provisioning in MEC as an Integer Nonlinear Programming (INLP), aiming at maximizing the total utility of all users. We then propose a Nested-Genetic Algorithm (Nested-GA) consisting of two genetic algorithms, each of whom solves a sub-problem regarding service placement or request scheduling decisions. Finally, simulation results demonstrate that our proposal outperforms conventional methods in terms of the total utility and achieves close-to-optimal solutions.
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Kim, Dongkyun, and Yong-Hwan Kim. "Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN." Algorithms 13, no. 10 (September 27, 2020): 245. http://dx.doi.org/10.3390/a13100245.
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Multi-access edge computing (MEC) has become an essential technology for collecting, analyzing, and processing data generated by widely distributed user equipment (UE), wireless end-hosts, Internet of things (IoT) sensors, etc., providing real-time and high-quality networking services with ultralow end-to-end latency guaranteed between various user devices and edge cloud computing nodes. However, the cloud resources at the MEC on-site (access point) and edge site are restricted and insufficient mainly because of the operation and management constraints (e.g., limited space and capacity), particularly in the case of on-demand and dynamic service resource deployment. In this regard, we propose a selective MEC resource allocation scheme adopting a multitier architecture over a wide-area software-defined network (SDN) on the basis of our recent research work on virtual network slicing and resource orchestration. The proposed scheme provides an optimized MEC selection model considering end-to-end latency and efficient service resource utilization on the basis of the hierarchical MEC service architecture.
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Li, Jing, Weifa Liang, Zichuan Xu, Xiaohua Jia, and Wanlei Zhou. "Service Provisioning for Multi-source IoT Applications in Mobile Edge Computing." ACM Transactions on Sensor Networks 18, no. 2 (May 31, 2022): 1–25. http://dx.doi.org/10.1145/3484200.
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We are embracing an era of Internet of Things (IoT). The latency brought by unstable wireless networks caused by limited resources of IoT devices seriously impacts the quality of services of users, particularly the service delay they experienced. Mobile Edge Computing (MEC) technology provides promising solutions to delay-sensitive IoT applications, where cloudlets (edge servers) are co-located with wireless access points in the proximity of IoT devices. The service response latency for IoT applications can be significantly shortened due to that their data processing can be performed in a local MEC network. Meanwhile, most IoT applications usually impose Service Function Chain (SFC) enforcement on their data transmission, where each data packet from its source gateway of an IoT device to the destination (a cloudlet) of the IoT application must pass through each Virtual Network Function (VNF) in the SFC in an MEC network. However, little attention has been paid on such a service provisioning of multi-source IoT applications in an MEC network with SFC enforcement. In this article, we study service provisioning in an MEC network for multi-source IoT applications with SFC requirements and aiming at minimizing the cost of such service provisioning, where each IoT application has multiple data streams from different sources to be uploaded to a location (cloudlet) in the MEC network for aggregation, processing, and storage purposes. To this end, we first formulate two novel optimization problems: the cost minimization problem of service provisioning for a single multi-source IoT application, and the service provisioning problem for a set of multi-source IoT applications, respectively, and show that both problems are NP-hard. Second, we propose a service provisioning framework in the MEC network for multi-source IoT applications that consists of uploading stream data from multiple sources of the IoT application to the MEC network, data stream aggregation and routing through the VNF instance placement and sharing, and workload balancing among cloudlets. Third, we devise an efficient algorithm for the cost minimization problem built upon the proposed service provisioning framework, and further extend the solution for the service provisioning problem of a set of multi-source IoT applications. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.
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Zhang, Xiangjun, Weiguo Wu, Shiyuan Yang, and Xiong Wang. "Falcon: A Blockchain-Based Edge Service Migration Framework in MEC." Mobile Information Systems 2020 (October 16, 2020): 1–17. http://dx.doi.org/10.1155/2020/8820507.
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Driven by advanced 5G cellular systems, mobile edge computing (MEC) has emerged as a promising technology that can meet the energy efficiency and latency requirements of IoT applications. Edge service migration in the MEC environment plays an important role in ensuring user service quality and enhancing terminal computing capabilities. Application services on the edge side should be migrated from different edge servers to edge nodes closer to users, so that services follow users and ensure high-quality services. In addition, during the migration process, edge services face security challenges in an edge network environment without centralized management. To tackle this challenge, this paper innovatively proposes a blockchain-based security edge service migration framework, Falcon, which uses mobile agents different from VM and container as edge service carriers, making migration more flexible. Furthermore, we considered the dependencies between agents and designed a service migration algorithm to maximize the migration benefits and obtain better service quality. In order to ensure the migration of edge services in a safe and reliable environment, Falcon maintains an immutable alliance chain among multiple edge clouds. Finally, the experimental results show that “Falcon” has lower energy consumption and higher service quality.
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E, Xuefei, Zhonggui Ma, and JunFeng Huang. "Cluster Design and Optimization of SWIPT-Based MEC Networks with UAV Assistance." Wireless Communications and Mobile Computing 2021 (November 3, 2021): 1–14. http://dx.doi.org/10.1155/2021/6745460.
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In recent years, service isolation and service miniaturization have become very popular. The large services are dismantled into multiple low-cost and simple small services to improve the scalability and disaster tolerance of the entire services. A service network composed of unmanned aerial vehicles (UAVs) and MEC servers is proposed in this paper, which aims at decoupling multiple services of the SWIPT-MEC network. In this network, UAVs take charge of energy transmission and calculation task scheduling and MEC servers are focused on task calculation. To meet the resource requirements of the ground nodes (GNs) in the network, we designed a distributed iterative algorithm to solve the resource allocation decision problem of GNs and used the modified expert bat algorithm to complete the UAV’s trajectory planning in a two-dimensional space. The results show that the algorithm can formulate a more fair resource allocation strategy, and its performance is improved by 7% compared with the traditional bat algorithm. In addition, the algorithm in this paper can also adapt to changes in task length and has a certain degree of stability.
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Yeonjoo Lim, Yeonjoo Lim, and Jong-Hyouk Lee Yeonjoo Lim. "Container-based Service Relocation for Beyond 5G Networks." 網際網路技術學刊 23, no. 4 (July 2022): 911–18. http://dx.doi.org/10.53106/160792642022072304026.
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<p>With the advent of 5G networks, various research on Multi-access Edge Computing (MEC) to provide high-reliability and ultra-low latency services are being actively conducted. MEC is an intelligent service distributed cloud technology that provides a high level of personal services by deploying cloud servers to edge networks physically closed to users. However, there is a technical issue to be solved, e.g., the service being used by a user does not exist in the new edge network, and there may even be situations in which the service cannot be provided in the new edge network. To address this, the service application must be relocated according to the location of the user’s movement. Various research works are underway to solve this service relocation issue, e.g., cold/live migration studies have been carried in legacy cloud environments. In this paper, we propose a container migration technique that guarantees a smooth service application relocation for mobile users. We design scenarios for adaptive handoff and describe the detailed operation process. In addition, we present our MEC testbed, which has been used to experiment our container migration technique.</p> <p> </p>
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Wu, Hongchen, Huaxiang Zhang, Lizhen Cui, and Xinjun Wang. "CEPTM: A Cross-Edge Model for Diverse Personalization Service and Topic Migration in MEC." Wireless Communications and Mobile Computing 2018 (August 12, 2018): 1–12. http://dx.doi.org/10.1155/2018/8056195.
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For several reasons, the cloud computing paradigm, e.g., mobile edge computing (MEC), is suffering from the problem of privacy issues. MEC servers provide personalization services to mobile users for better QoE qualities, but the ongoing migrated data from the source edge server to the destination edge server cause users to have privacy concerns and unwillingness of self-disclosure, which further leads to a sparsity problem. As a result, personalization services ignore valuable user profiles across edges where users have accounts in and tend to predict users’ potential purchases with insufficient sources, thereby limiting further improvement of QoE through personalization of the contents. This paper proposes a novel model, called CEPTM, which (1) collects mobile user data across multiple MEC edge servers, (2) improves the users’ experience in personalization services by loading collected diverse data, and (3) lowers their privacy concern with the improved personalization. This model also reveals that famous topics in one edge server can migrate into several other edge servers with users’ favorite content tags and that the diverse types of items could increase the possibility of users accepting the personalization service. In the experiment section, we use exploratory factor analysis to mathematically evaluate the correlations among those factors that influence users’ information disclosure in the MEC network, and the results indicate that CEPTM (1) achieves a high rate of personalization acceptance due to the availability of more data as input and highly diverse personalization as output and (2) gains the users’ trust because it collects user data while respecting individual privacy concerns and providing better personalization. It outperforms a traditional personalization service that runs on a single-edge server. This paper provides new insights into MEC diverse personalization services and privacy problems, and researchers and personalization providers can apply this model to merge popular users’ like trends throughout the MEC edge servers and generate better data management strategies.
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Pencheva, Evelina, and Ivaylo Atanasov. "Mobile Edge Services for Quality of Service Control and Access to Terminal Status." Cybernetics and Information Technologies 18, no. 2 (June 1, 2018): 133–50. http://dx.doi.org/10.2478/cait-2018-0034.
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Abstract An important ingredient of fifth generation (5G) networks will be Multi-access Edge Computing (MEC). MEC brings the computational intelligence of the cloud within the Radio Access Network (RAN). The virtualized functionality is accessible through Application Programming Interfaces (APIs). In this paper, we study capabilities of reuse existing time-tested Web Services to provide mobile edge middleware services. The focus is on mobile edge services that can be used by applications for bandwidth management and access to user contextual information. An extension of Web Service functionality is proposed. Implementation issues of Web Services in RAN are considered.
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Takagi, Shiori, Shin'ichi Arakawa, and Masayuki Murata. "Design, Implementation and Evaluation of Core/Periphery-based Network-oriented Mixed Reality Services." Journal of Internet Services and Applications 12, no. 1 (February 23, 2022): 1–10. http://dx.doi.org/10.5753/jisa.2022.2371.
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Many new network-oriented services have been developed in recent years, and Multi-access Edge Computing (MEC) has been standardized to improve the responsiveness of services. When deploying services in a MEC environment, it is necessary to consider a service structure that can flexibly switch service behaviors to meet various user requests and that can change service behaviors according to the real-world environment at a low implementation cost. In this paper, we introduce a core/periphery structure for service components, which is known as a model for flexible behavior in biological systems, and design and implement a network-oriented mixed reality service based on this structure. We investigate what kinds of functions should be developed to accommodate user requests in conjunction with various types of devices and real-world environments in which users and devices are located. To utilize the flexibility of a core/periphery structure, we regard core functions as those whose behaviors remain unchanged even when there are changes in user requests or the environment. In contrast, peripheral functions are those whose behaviors can change under such circumstances. Experiments reveal that implementation costs are reduced while retaining increases in service response time to less than 31 ms. These results show that taking advantage of a core/periphery structure allows appropriate division of service functions and placement of functions in a MEC environment, with only small penalties on latency and at a low implementation cost.
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Lee, Juyong, Daeyoub Kim, and Jihoon Lee. "ZONE-Based Multi-Access Edge Computing Scheme for User Device Mobility Management." Applied Sciences 9, no. 11 (June 5, 2019): 2308. http://dx.doi.org/10.3390/app9112308.
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Recently, new mobile applications and services have appeared thanks to the rapid development of mobile devices and mobile network technology. Cloud computing has played an important role over the past decades, providing powerful computing capabilities and high-capacity storage space to efficiently deliver these mobile services to mobile users. Nevertheless, existing cloud computing delegates computing to a cloud server located at a relatively long distance, resulting in significant delays due to additional time to return processing results from a cloud server. These unnecessary delays are inconvenient for mobile users because they are not suitable for applications that require a real-time service environment. To cope with these problems, a new computing concept called Multi-Access Edge Computing (MEC) has emerged. Instead of sending all requests to the central cloud to handle mobile users’ requests, the MEC brings computing power and storage resources to the edge of the mobile network. It enables the mobile user device to run the real-time applications that are sensitive to latency to meet the strict requirements. However, there is a lack of research on the efficient utilization of computing resources and mobility support when mobile users move in the MEC environment. In this paper, we propose the MEC-based mobility management scheme that arranges MEC server (MECS) as the concept of Zone so that mobile users can continue to receive content and use server resources efficiently even when they move. The results show that the proposed scheme reduce the average service delay compared to the existing MEC scheme. In addition, the proposed scheme outperforms the existing MEC scheme because mobile users can continuously receive services, even when they move frequently.
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Yang, Run, Hui He, and Weizhe Zhang. "Multitier Service Migration Framework Based on Mobility Prediction in Mobile Edge Computing." Wireless Communications and Mobile Computing 2021 (April 23, 2021): 1–13. http://dx.doi.org/10.1155/2021/6638730.
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Mobile edge computing (MEC) pushes computing resources to the edge of the network and distributes them at the edge of the mobile network. Offloading computing tasks to the edge instead of the cloud can reduce computing latency and backhaul load simultaneously. However, new challenges incurred by user mobility and limited coverage of MEC server service arise. Services should be dynamically migrated between multiple MEC servers to maintain service performance due to user movement. Tackling this problem is nontrivial because it is arduous to predict user movement, and service migration will generate service interruptions and redundant network traffic. Service interruption time must be minimized, and redundant network traffic should be reduced to ensure service quality. In this paper, the container live migration technology based on prediction is studied, and an online prediction method based on map data that does not rely on prior knowledge such as user trajectories is proposed to address this challenge in terms of mobility prediction accuracy. A multitier framework and scheduling algorithm are designed to select MEC servers according to moving speeds of users and latency requirements of offloading tasks to reduce redundant network traffic. Based on the map of Beijing, extensive experiments are conducted using simulation platforms and real-world data trace. Experimental results show that our online prediction methods perform better than the common strategy. Our system reduces network traffic by 65% while meeting task delay requirements. Moreover, it can flexibly respond to changes in the user’s moving speed and environment to ensure the stability of offload service.
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Pereira, Rickson S., Douglas D. Lieira, Marco A. C. da Silva, Adinovam H. M. Pimenta, Joahannes B. D. da Costa, Denis Rosário, Leandro Villas, and Rodolfo I. Meneguette. "RELIABLE: Resource Allocation Mechanism for 5G Network using Mobile Edge Computing." Sensors 20, no. 19 (September 23, 2020): 5449. http://dx.doi.org/10.3390/s20195449.
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Technological advancement is currently focused on the miniaturization of devices, and integrated circuits allow us to observe the increase in the number of Internet of Things (IoT) devices. Most IoT services and devices require an Internet connection, which needs to provide the minimum processing, storage and networking requirements to best serve a requested service. One of the main goals of 5G networks is to comply with the user’s various Quality of Service (QoS) requirements in different application scenarios. Fifth-generation networks use Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) concepts to achieve these QoS requirements. However, the computational resource allocation mechanisms required by the services are considered very complex. Thus, in this paper, we propose an allocation and management resources mechanism for 5G networks that uses MEC and simple mathematical methods to reduce the model complexity. The mechanism decides to allocate the resource in MEC to meet the requirements requested by the user. The simulation results show that the proposed mechanism provides a larger amount of services, leading to a reduction in the service lock number and as a reduction in the blocking ratio of services due to the accuracy of the approach and its load balancing in the process of resource allocation.
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Zhao, Xuhui, Jianghui Liu, Baofeng Ji, and Lin Wang. "Service Migration Policy Optimization considering User Mobility for E-Healthcare Applications." Journal of Healthcare Engineering 2021 (June 19, 2021): 1–13. http://dx.doi.org/10.1155/2021/9922876.
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Mobile edge computing (MEC) is an emerging technology that provides cloud services at the edge of network to enable latency-critical and resource-intensive E-healthcare applications. User mobility is common in MEC. User mobility can result in an interruption of ongoing edge services and a dramatic drop in quality of service. Service migration has a great potential to address the issues and brings inevitable cost for the system. In this paper, we propose a service migration solution based on migration zone and formulate service migration cost with a comprehensive model that captures the key challenges. Then, we formulate service migration problem into Markov decision process to obtain optimal service migration policies that decide where to migrate in a limited area. We propose three algorithms to resolve the optimization problem given by the formulated model. Finally, we demonstrate the performance of our proposed algorithms by carrying out extensive experiments. We show that the proposed service migration approach reduces the total cost by up to 3 times compared to no migration and outperforms the general solution in terms of the total expected reward.
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Niewolski, Wojciech, Tomasz W. Nowak, Mariusz Sepczuk, Zbigniew Kotulski, Rafal Artych, Krzysztof Bocianiak, and Jean-Philippe Wary. "Security Context Migration in MEC: Challenges and Use Cases." Electronics 11, no. 21 (October 28, 2022): 3512. http://dx.doi.org/10.3390/electronics11213512.
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Modern and future services require ultra-reliable mobile connections with high bandwidth parameters and proper security protection. It is possible to ensure such conditions by provisioning services in the Multi-Access Edge Computing system integrated with fifth-generation mobile networks. However, the main challenge in the mentioned architecture is providing a secure service migration with all related data and security requirements to another edge computing host area when the user changes its physical location. This article aims to present the state of research on the migration of the security context between service instances in Edge/MEC servers, specify steps of the migration procedure, and identify new security challenges inspired by use cases of 5G vertical industries. For this purpose, the analysis of the security context’s structure and basic concept of the Security Service Level Agreement was performed and presented in the document. Next, a further investigation of the security context was conducted, including requirements for its reliable migration between edge serves instances. The study mainly focused on crucial migration challenges and possible solutions to resolve them. Finally, the authors presented how the proposed solution can be used to protect 5G vertical industries services based on several mobile use cases.
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Monir Mansour, Merrihan Badr, Tamer Abdelkader, Mohammed Hashem AbdelAziz, and El-Sayed Mohamed EI-Horbaty. "A trust evaluation scheme of service providers in mobile edge computing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (April 1, 2022): 2121. http://dx.doi.org/10.11591/ijece.v12i2.pp2121-2138.
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Mobile edge computing (MEC) is a new computing paradigm that brings cloud services to the network edge. Despite its great need in terms of computational services in daily life, service users may have several concerns while selecting a suitable service provider to fulfil their computational requirements. Such concerns are: with whom they are dealing with, where will their private data migrate to, service provider processing performance quality. Therefore, this paper presents a trust evaluation scheme that evaluates the processing performance of a service provider in the MEC environment. Processing performance of service providers is evaluated in terms of average processing success rate and processing throughput, thus allocating a service provider in a relevant trust status. Service provider processing incompliance and user termination ratio are also computed during provider’s interactions with users. This is in an attempt to help future service users to be acknowledged of service provider’s past interactions prior dealing with it. Thus, eliminating the probability of existing compromised service providers and raising the security and success of future interactions between service providers and users. Simulations results show service providers processing performance degree, processing incompliance and user termination ratio. A service provider is allocated to a trust status according to the evaluated processing performance trust degree.
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Peng, Shuping, Jose Oscar Fajardo, Pouria Sayyad Khodashenas, Begoña Blanco, Fidel Liberal, Cristina Ruiz, Charles Turyagyenda, Mick Wilson, and Sunil Vadgama. "QoE-Oriented Mobile Edge Service Management Leveraging SDN and NFV." Mobile Information Systems 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/3961689.
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5G envisages a “hyperconnected society” where trillions of diverse entities could communicate with each other anywhere and at any time, some of which will demand extremely challenging performance requirements such as submillisecond low latency. Mobile Edge Computing (MEC) concept where application computing resources are deployed at the edge of the mobile network in proximity of an end user is a promising solution to improve quality of online experience. To make MEC more flexible and cost-effective Network Functions Virtualisation (NFV) and Software-Defined Networking (SDN) technologies are widely adopted. It leads to significant CAPEX and OPEX reduction with the help of a joint radio-cloud management and orchestration logic. In this paper we discuss and develop a reference architecture for the orchestration and management of the MEC ecosystem. Along with the lifecycle management flows of MEC services, indicating the interactions among the functional modules inside the Orchestrator and with external elements, QoS management with a focus on the channel state information technique is presented.
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Pencheva, Evelina, Ivaylo Atanasov, Denitsa Kireva-Mihova, and Ventsislav Trifonov. "Mobile Edge Service for Intersystem Handover." International Journal of Engineering & Technology 7, no. 3.13 (July 27, 2018): 141. http://dx.doi.org/10.14419/ijet.v7i3.13.16341.
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Multi-access Edge Computing (MEC) appears to be an integrating technology for radio access networks to enhance the access capacity, optimize network performance and improve quality of experience for end users. MEC distributes cloud capabilities for storage and computing in the radio access network, close to the end users. As far as the development of advanced Application Programming Interfaces is a key area, we propose a new mobile edge service that enables 3rd party control on intersystem handover. The proposed service enables authorized applications to initiate intersystem handover following specific policy. The service is described by information flows illustrating the basic functionality, data models that provide mediation functions, and handover state models considering some implementation issues.
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Moon, Sungwon, and Yujin Lim. "Task Migration with Partitioning for Load Balancing in Collaborative Edge Computing." Applied Sciences 12, no. 3 (January 23, 2022): 1168. http://dx.doi.org/10.3390/app12031168.
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Multi-access edge computing (MEC) has emerged as a promising technology to facilitate efficient vehicular applications, such as autonomous driving, path planning and navigation. By offloading tasks from vehicles to MEC servers (MECSs), the MEC system can facilitate computation-intensive applications with hard latency constraints in vehicles with limited computing resources. However, owing to the mobility of vehicles, the vehicles are not evenly distributed across the MEC system. Therefore, some MECSs are heavily congested, whereas others are lightly loaded. If a task is offloaded to a congested MECS, it can be blocked or have high latency. Moreover, service interruption would occur because of the high mobility and limited coverage of the MECS. In this paper, we assume that the task can be divided into a set of subtasks and computed by multiple MECSs in parallel. Therefore, we propose a method of task migration with partitioning. To balance loads, the MEC system migrates the set of subtasks of tasks in an overloaded MECS to one or more underloaded MECSs according to the load difference. Simulations have indicated that, compared with conventional methods, the proposed method can increase the satisfaction of quality-of-service requirements, such as low latency, service reliability, and MEC system throughput by optimizing load balancing and task partitioning.
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Ray, Kaustabha, and Ansuman Banerjee. "Horizontal Auto-Scaling for Multi-Access Edge Computing Using Safe Reinforcement Learning." ACM Transactions on Embedded Computing Systems 20, no. 6 (November 30, 2021): 1–33. http://dx.doi.org/10.1145/3475991.
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Multi-Access Edge Computing (MEC) has emerged as a promising new paradigm allowing low latency access to services deployed on edge servers to avert network latencies often encountered in accessing cloud services. A key component of the MEC environment is an auto-scaling policy which is used to decide the overall management and scaling of container instances corresponding to individual services deployed on MEC servers to cater to traffic fluctuations. In this work, we propose a Safe Reinforcement Learning (RL)-based auto-scaling policy agent that can efficiently adapt to traffic variations to ensure adherence to service specific latency requirements. We model the MEC environment using a Markov Decision Process (MDP). We demonstrate how latency requirements can be formally expressed in Linear Temporal Logic (LTL). The LTL specification acts as a guide to the policy agent to automatically learn auto-scaling decisions that maximize the probability of satisfying the LTL formula. We introduce a quantitative reward mechanism based on the LTL formula to tailor service specific latency requirements. We prove that our reward mechanism ensures convergence of standard Safe-RL approaches. We present experimental results in practical scenarios on a test-bed setup with real-world benchmark applications to show the effectiveness of our approach in comparison to other state-of-the-art methods in literature. Furthermore, we perform extensive simulated experiments to demonstrate the effectiveness of our approach in large scale scenarios.
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Wang, Chuang, Yi Lv, Qiang Wang, Dongyu Yang, and Guanghui Zhou. "Service-Oriented Real-Time Smart Job Shop Symmetric CPS Based on Edge Computing." Symmetry 13, no. 10 (October 1, 2021): 1839. http://dx.doi.org/10.3390/sym13101839.
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Symmetry is one of the most important notions in the digital twins-driven manufacturing cyber–physical system (CPS). Real-time acquisition of production data and rapid response to changes in the external environment are the keys to ensuring the symmetry of the CPS. In the service-oriented production process, in order to solve the problem of the service response delay of the production nodes in a smart job shop, a CPS based on mobile edge computing (MEC) middleware is proposed. First, the CPS and MEC for a service-oriented production process are analyzed. Secondly, based on MEC middleware, a CPS architecture model of a smart job shop is established. Then, the implementation of MEC middleware and application layer function modules are introduced in detail. By designing an MEC middleware model and embedding function modules such as data cache management, redundant data filtering, and data preprocessing, the ability of data processing is sunk from the data center to the data source. Based on that, the network performances, such as network bandwidth, packet loss rate, and delay, are improved. Finally, an experiment platform of the smart job shop is used to verify different data processing modes by comparing the network performance data such as bandwidth, packet loss rate, and response delay.
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Alharbi, Abdullah, Hashem Alyami, Poongodi M, Hafiz Tayyab Rauf, and Seifedine Kadry. "Intelligent scaling for 6G IoE services for resource provisioning." PeerJ Computer Science 7 (October 26, 2021): e755. http://dx.doi.org/10.7717/peerj-cs.755.
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The proposed research motivates the 6G cellular networking for the Internet of Everything’s (IoE) usage empowerment that is currently not compatible with 5G. For 6G, more innovative technological resources are required to be handled by Mobile Edge Computing (MEC). Although the demand for change in service from different sectors, the increase in IoE, the limitation of available computing resources of MEC, and intelligent resource solutions are getting much more significant. This research used IScaler, an effective model for intelligent service placement solutions and resource scaling. IScaler is considered to be made for MEC in Deep Reinforcement Learning (DRL). The paper has considered several requirements for making service placement decisions. The research also highlights several challenges geared by architectonics that submerge an Intelligent Scaling and Placement module.
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Liu, Chunyu, Heli Zhang, Xi Li, and Hong Ji. "Dynamic Rendering-Aware VR Service Module Placement Strategy in MEC Networks." Wireless Communications and Mobile Computing 2022 (August 18, 2022): 1–17. http://dx.doi.org/10.1155/2022/1237619.
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Combining multiaccess edge computing (MEC) technology and wireless virtual reality (VR) game is a promising computing paradigm. Offloading the rendering tasks to the edge node can make up for the lack of computing resources of mobile devices. However, the current offloading works ignored that when rendering is enabled at the MEC server, the rendering operation depends heavily on the environment deployed on this MEC serve. In this paper, we propose a dynamically rendering-aware service module placement scheme for wireless VR games over the MEC networks. In this scheme, the rendering tasks of VR games are offloaded to the MEC server and closely coupled with service module placement. At the same time, to further optimize the end-to-end latency of VR video delivery, the routing delay of the rendered VR video stream and the costs of the service module migration are jointly considered with the proposed placement scheme. The goal of this scheme is to minimize the sum of the network costs over a long time under satisfying the delay constraint of each player. We model our strategy as a high-order, nonconvex, and time-varying function. To solve this problem, we transform the placement problem into the min-cut problem by constructing a series of auxiliary graphs. Then, we propose a two-stage iterative algorithm based on convex optimization and graphs theory to solve our object function. Finally, extensive simulation results show that our proposed algorithm can ensure low end-to-end latency for players and low network costs over the other baseline algorithms.
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Slamnik-Kriještorac, Nina, Erik de Britto e Silva, Esteban Municio, Henrique C. Carvalho de Resende, Seilendria A. Hadiwardoyo, and Johann M. Marquez-Barja. "Network Service and Resource Orchestration: A Feature and Performance Analysis within the MEC-Enhanced Vehicular Network Context." Sensors 20, no. 14 (July 10, 2020): 3852. http://dx.doi.org/10.3390/s20143852.
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By providing storage and computational resources at the network edge, which enables hosting applications closer to the mobile users, Multi-Access Edge Computing (MEC) uses the mobile backhaul, and the network core more efficiently, thereby reducing the overall latency. Fostering the synergy between 5G and MEC brings ultra-reliable low-latency in data transmission, and paves the way towards numerous latency-sensitive automotive use cases, with the ultimate goal of enabling autonomous driving. Despite the benefits of significant latency reduction, bringing MEC platforms into 5G-based vehicular networks imposes severe challenges towards poorly scalable network management, as MEC platforms usually represent a highly heterogeneous environment. Therefore, there is a strong need to perform network management and orchestration in an automated way, which, being supported by Software Defined Networking (SDN) and Network Function Virtualization (NFV), will further decrease the latency. With recent advances in SDN, along with NFV, which aim to facilitate management automation for tackling delay issues in vehicular communications, we study the closed-loop life-cycle management of network services, and map such cycle to the Management and Orchestration (MANO) systems, such as ETSI NFV MANO. In this paper, we provide a comprehensive overview of existing MANO solutions, studying their most important features to enable network service and resource orchestration in MEC-enhanced vehicular networks. Finally, using a real testbed setup, we conduct and present an extensive performance analysis of Open Baton and Open Source MANO that are, due to their lightweight resource footprint, and compliance to ETSI standards, suitable solutions for resource and service management and orchestration within the network edge.
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Choiriyah, Siti Nur, Vivin Maharani Ekowati, and Ulfi Kartika Oktaviana. "THE EFFECT OF PERCEIVED VALUE ON SERVICE SATISFACTION IN BRAND IMAGE MEDIATION (Case Study toward Accounting Student, Faculty of Economics, State University in East Java)." Management and Economics Journal (MEC-J) 1, no. 1 (December 6, 2017): 21. http://dx.doi.org/10.18860/mec-j.v1i1.4526.
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<p>This study aims to examine, test, and examine the effect of Perceived Value on Service Satisfaction. Testing the role of Brand Image as a variable mediation influence Perceived Value on Service Satisfaction. The research was conducted in Accounting Department of Faculty of Economics, Surabaya State University, Airlangga University Surabaya, State Islamic University Maulana Malik Ibrahim Malang, Brawijaya University, and State University of Malang. The population of this study are all students of Accounting Department of Economics Faculty of State University in East Java as many as 5,366 students. The sample of this research is 98 students. Data collected directly from resonden by using research instrument in the form of questionnaire and technical data using Path Analysis. The results show that Perceived Value has a significant effect on brand image. Brand image mediates the influence of Perceived Value on Service Satisfaction, where the image of the brand image as a full mediation. Based on these results, it can be interpreted that every member of the organization should be able to create and maintain excellent service provided to students, so that students feel safe and comfortable to perform activities that are closely related to organizational goals, and not reluctant to perform activities that can increase the value that customers perceive to the organization. With a good understanding of the basic concepts of service satisfaction that are firmly rooted in the mind of a student, the student will show the satisfaction of the various forms of service as well. Students will tend to think that the satisfaction of the service is received in accordance with the perceived value of the sacrifice of the cost given to it in obtaining the service. The better the services provided by the department to the students the better the assessment given by the student. So that good service brings out the brand image itself against a student in the State College.</p><strong>Keyword:</strong> Perceived Value, Service Satisfaction, Brand Image
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Anastasopoulos, Markos P., Anna Tzanakaki, and Dimitra Simeonidou. "Scalable Service Chaining in MEC-Assisted 5G Networks." Journal of Lightwave Technology 37, no. 16 (August 15, 2019): 4115–24. http://dx.doi.org/10.1109/jlt.2019.2923079.
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Atanasov, Ivaylo I., Evelina N. Pencheva, Denitsa L. Velkova, and Ivaylo P. Asenov. "Multiparty Call Control at the Network Edge." Elektronika ir Elektrotechnika 26, no. 5 (October 27, 2020): 39–49. http://dx.doi.org/10.5755/j01.eie.26.5.26007.
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Network programmability is a key feature of fifth generation (5G) system which, in combination with cloud-based services, can support many use cases, including mission critical and healthcare communications. Programmability enables flexibility in customization of service connectivity. Multi-access Edge Computing (MEC) services and applications are enablers for network programmability. In this paper, MEC capabilities for programmability of multiparty multimedia call control at the network edge are studied. Multiparty video calls are one of the key applications of 5G, and are efficient way to exchange ideas, knowledge, expertise, information, and so on. The paper presents an approach to design MEC Application Programming Interfaces (APIs) which enable third party applications to create multiparty multimedia sessions and dynamically manage session participations. The API functionality is described by required information and message flows. The paper specifies the proposed MEC API with data model. Feasibility study includes modelling and formal validation of multiparty session state models supported by the network and mobile edge application. The latency injected by the API is evaluated by emulation.
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Hossain, Md Delowar, Tangina Sultana, VanDung Nguyen, Waqas ur Rahman, Tri D. T. Nguyen, Luan N. T. Huynh, and Eui-Nam Huh. "Fuzzy Based Collaborative Task Offloading Scheme in the Densely Deployed Small-Cell Networks with Multi-Access Edge Computing." Applied Sciences 10, no. 9 (April 29, 2020): 3115. http://dx.doi.org/10.3390/app10093115.
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Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.
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Fu, Shuang, Chenyang Ding, and Peng Jiang. "Computational Offloading of Service Workflow in Mobile Edge Computing." Information 13, no. 7 (July 19, 2022): 348. http://dx.doi.org/10.3390/info13070348.
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Mobile edge computing (MEC) sinks the functions and services of cloud computing to the edge of the network to provide users with storage and computing resources. For workflow tasks, the interdependency and the sequence constraint being among the tasks make the offloading strategy more complicated. To obtain the optimal offloading and scheduling scheme for workflow tasks to minimize the total energy consumption of the system, a workflow task offloading and scheduling scheme based on an improved genetic algorithm is proposed in an MEC network with multiple users and multiple virtual machines (VMs). Firstly, the system model of the offloading and scheduling of workflow tasks in a multi-user and multi-VMs MEC network is built. Then, the problem of how to determine the optimal offloading and scheduling scheme of workflow to minimize the total energy consumption of the system while meeting the deadline constraint is formulated. To solve this problem, the improved genetic algorithm is adopted to obtain the optimal offloading strategy and scheduling. Finally, the simulation results show that the proposed scheme can achieve a lower energy consumption than other benchmark schemes.
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Park, Jaesung, and Yujin Lim. "Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System." Applied Sciences 13, no. 4 (February 17, 2023): 2620. http://dx.doi.org/10.3390/app13042620.
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The energy consumption of a multi-access edge computing (MEC) system must be reduced to save operational costs. Determining a set of active MEC servers (MECSs) that can minimize the energy consumption of the MEC system while satisfying the service delay requirements of the tasks is an NP-complete problem. To solve this problem, we take a bio-inspired approach. We note that the sleep control problem of the MECS differentiates the operational mode among neighboring MECSs. Therefore, by mimicking the cell differentiation process in a biological system, we designed a distributed sleep control method. Each MECS periodically gathers the utilization and delta levels of the neighboring MECSs. Subsequently, by using the gathered information and the Delta–Notch inter-cell signaling model, a MECS autonomously decides whether to sleep. We evaluated the performance of our method through extensive simulations. Compared with a conventional method, the proposed method reduces energy consumption in a MEC system by more than 13% while providing a comparable service delay. In addition, our method reduces the variations in the service delay by more than 35%.
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Nakazato, Jin, Zongdian Li, Kazuki Maruta, Keiichi Kubota, Tao Yu, Gia Khanh Tran, Kei Sakaguchi, and Soh Masuko. "MEC/Cloud Orchestrator to Facilitate Private/Local Beyond 5G with MEC and Proof-of-Concept Implementation." Sensors 22, no. 14 (July 8, 2022): 5145. http://dx.doi.org/10.3390/s22145145.
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The emergence of 5G-IoT opens up unprecedented connectivity possibilities for new service use cases and players. Multi-access edge computing (MEC) is a crucial technology and enabler for Beyond 5G, supporting next-generation communications with service guarantees (e.g., ultra-low latency, high security) from an end-to-end (E2E) perspective. On the other hand, one notable advance is the platform that supports virtualization from RAN to applications. Deploying Radio Access Networks (RAN) and MEC, including third-party applications on virtualization platforms, and renting other equipment from legacy telecom operators will make it easier for new telecom operators, called Private/Local Telecom Operators, to join the ecosystem. Our preliminary studies have discussed the ecosystem for private and local telecom operators regarding business potential and revenue and provided numerical results. What remains is how Private/Local Telecom Operators can manage and deploy their MEC applications. In this paper, we designed the architecture for fully virtualized MEC 5G cellular networks with local use cases (e.g., stadiums, campuses). We propose an MEC/Cloud Orchestrator implementation for intelligent deployment selection. In addition, we provide implementation schemes in several cases held by either existing cloud owners or private and local operators. In order to verify the proposal’s feasibility, we designed the system level in E2E and constructed a Beyond 5G testbed at the Ōokayama Campus of the Tokyo Institute of Technology. Through proof-of-concept in the outdoor field, the proposed system’s feasibility is verified by E2E performance evaluation. The verification results prove that the proposed approach can reduce latency and provide a more stable throughput than conventional cloud services.
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Li, Guanwen, Huachun Zhou, Bohao Feng, Guanglei Li, Taixin Li, Qi Xu, and Wei Quan. "Fuzzy Theory Based Security Service Chaining for Sustainable Mobile-Edge Computing." Mobile Information Systems 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/8098394.
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Mobile-Edge Computing (MEC) is a novel and sustainable network architecture that enables energy conservation with cloud computing and network services offloading at the edge of mobile cellular networks. However, how to efficiently manage various real-time changing security functions is an essential issue which hinders the future MEC development. To address this problem, we propose a fuzzy security service chaining approach for MEC. In particular, a new architecture is designed to decouple the required security functions with the physical resources. Based on this, we present a security proxy to support compatibility to traditional security functions. Furthermore, to find the optimal order of the required security functions, we establish a fuzzy inference system (FIS) based mechanism to achieve multiple optimal objectives. Much work has been done to implement a prototype, which is used to analyze the performance by comparing with a widely used method. The results prove that the proposed FIS mechanism achieves an improved performance in terms of Inverted Generational Distance (IGD) values and execution time with respect to the compared solution.
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Sedhom, Germien G., Alshimaa H. Ismail, and Basma M. Yousef. "Literature Review and Novel Trends of Mobile Edge Computing for 5G and Beyond." Journal of Artificial Intelligence and Metaheuristics 2, no. 2 (2022): 18–28. http://dx.doi.org/10.54216/jaim.020202.
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Because of the rapid evolution of communications technologies, such as the Internet of Things (IoT) and fifth generation (5G) systems and beyond, the latest developments have seen a fundamental change in mobile computing. Mobile computing is moved from central mobile cloud computing to mobile edge computing (MEC). Therefore, MEC is considered an essential technology for 5G technology and beyond. The MEC technology permits user equipment (UEs) to execute numerous high-computational operations by creating computing capabilities at the edge networks and inside access networks. Consequently, in this paper, we extensively address the role of MEC in 5G networks and beyond. Accordingly, we first investigate the MEC architecture, the characteristics of edge computing, and the MEC challenges. Then, the paper discusses the MEC use cases and service scenarios. Further, computations offloading is explored. Lastly, we propose upcoming research difficulties in incorporating MEC with the 5G system and beyond.
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Li, Guang-Shun, Ying Zhang, Mao-Li Wang, Jun-Hua Wu, Qing-Yan Lin, and Xiao-Fei Sheng. "Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing." Complexity 2020 (January 20, 2020): 1–11. http://dx.doi.org/10.1155/2020/8936064.
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With the emergence and development of the Internet of Vehicles (IoV), quick response time and ultralow delay are required. Cloud computing services are unfavorable for reducing delay and response time. Mobile edge computing (MEC) is a promising solution to address this problem. In this paper, we combined MEC and IoV to propose a specific vehicle edge resource management framework, which consists of fog nodes (FNs), data service agents (DSAs), and cars. A dynamic service area partitioning algorithm is designed to balance the load of DSA and improve the quality of service. A resource allocation framework based on the Stackelberg game model is proposed to analyze the pricing problem of FNs and the data resource strategy of DSA with a distributed iteration algorithm. The simulation results show that the proposed framework can ensure the allocation efficiency of FN resources among the cars. The framework achieves the optimal strategy of the participants and subgame perfect Nash equilibrium.
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He, Tao, Kunxin Zhu, Zhipeng Chen, Ruomei Wang, and Fan Zhou. "Popularity-Guided Cost Optimization for Live Streaming in Mobile Edge Computing." Wireless Communications and Mobile Computing 2022 (January 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/5562995.
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Live streaming service usually delivers the content in mobile edge computing (MEC) to reduce the network latency and save the backhaul capacity. Considering the limited resources, it is necessary that MEC servers collaborate with each other and form an overlay to realize more efficient delivery. The critical challenge is how to optimize the topology among the servers and allocate the link capacity so that the cost will be lower with delay constraints. Previous approaches rarely consider server collaborations for live streaming service, and the scheduling delay is usually ignored in MEC, leading to suboptimal performances. In this paper, we propose a popularity-guided overlay model which takes the scheduling delay into consideration and utilizes MEC collaboration to achieve efficient live streaming service. The links and servers are shared among all channel streams and each stream is pushed from cloud servers to MEC servers via the trees. Considering the optimization problem is NP-hard, we propose an effective optimization framework called cost optimization for live streaming (COLS) to predict the channel popularity by a LSTM model with multiscale input data. Finally, we compute topology graph by greedy scheme and allocate the capacity with convex programming. Experimental results show that the proposed approach achieves higher prediction accuracy, reducing the capacity cost by more than 40% with an acceptable delay compared with state-of-the-art schemes.
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Roedder, Alexandra. "The Localization of Kiki’s Delivery Service." Mechademia 9, no. 1 (2014): 254–67. http://dx.doi.org/10.1353/mec.2014.0008.
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Czekierda, Łukasz, Krzysztof Zieliński, and Sławomir Zieliński. "Automated Orchestration of Online Educational Collaboration in Cloud-based Environments." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1 (April 16, 2021): 1–26. http://dx.doi.org/10.1145/3412381.
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Integrated collaboration environments (ICEs) are widely used by corporations to increase productivity by fostering groupwide and interpersonal collaboration. In this article, we discuss the enhancements of such environment needed to build an educational ICE (E-ICE) that addresses the specific needs of educational users. The motivation for the research was the Małopolska Educational Cloud (MEC) project conducted by AGH University and its partners. The E-ICE developed by MEC project fosters collaboration between universities and high schools by creating an immersive virtual collaboration space. MEC is a unique project due to its scale and usage domain. Multiple online collaboration events are organized weekly between over 150 geographically scattered institutions. Such events, aside from videoconferencing, require various services. The MEC E-ICE is a complex composition of a significant number of services and various terminals that require very specific configuration and management. In this article, we focus on a model-driven approach to automating the organization of online meetings in their preparation, execution, and conclusion phases. We present a conceptual model of E-ICE-supported educational courses, introduce a taxonomy of online educational services, identify planes and modes of their operation, as well as discuss the most common collaboration patterns. The MEC E-ICE, which we present as a case study, is built in accordance with the presented, model-driven approach. MEC educational services are described in a way that allows for converting the declarative specification of E-ICE application models into platform-independent models, platform-specific models, and, finally, working sets of orchestrated service instances. Such approach both reduces the level of technical knowledge required from the end-users and considerably speeds up the construction of online educational collaboration environments.
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Sun, Shimin, Xinchao Zhang, Wentian Huang, Aixin Xu, Xiaofan Wang, and Li Han. "QoS-Based Multicast Routing in Network Function Virtualization-Enabled Software-Defined Mobile Edge Computing Networks." Mobile Information Systems 2021 (April 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/5590963.
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Mobile Edge Computing (MEC) technology brings the unprecedented computing capacity to the edge of mobile network. It provides the cloud and end user swift high-quality services with seamless integration of mobile network and Internet. With powerful capability, virtualized network functions can be allocated to MEC. In this paper, we study QoS guaranteed multicasting routing with Network Function Virtualization (NFV) in MEC. Specifically, data should pass through a service function chain before reaching destinations along a multicast tree with minimal computational cost and meeting QoS requirements. Furthermore, to overcome the problems of traditional IP multicast and software-defined multicasting approaches, we propose an implementable multicast mechanism that delivers data along multicast tree but uses unicast sessions. We finally evaluate the performance of the proposed mechanism based on experimental simulations. The results show that our mechanism outperforms others reported in the literature.
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Madduru, Pavan. "ARTIFICIAL INTELLIGENCE AS A SERVICE IN DISTRIBUTED MULTI ACCESS EDGE COMPUTING ON 5G EXTRACTING DATA USING IOT AND INCLUDING AR/VR FOR REAL-TIME REPORTING." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 15, 2021): 912–31. http://dx.doi.org/10.17762/itii.v9i1.220.
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To meet the growing demand for mobile data traffic and the stringent requirements for Internet of Things (IoT) applications in emerging cities such as smart cities, healthcare, augmented / virtual reality (AR / VR), fifth-generation assistive technologies generation (5G) Suggest and use on the web. As a major emerging 5G technology and a major driver of the Internet of Things, Multiple Access Edge Computing (MEC), which integrates telecommunications and IT services, provides cloud computing capabilities at the edge of an access network. wireless (RAN). By providing maximum compute and storage resources, MEC can reduce end-user latency. Therefore, in this article we will take a closer look at 5G MEC and the Internet of Things. Analyze the main functions of MEC in 5G and IoT environments. It offers several core technologies that enable the use of MEC in 5G and IoT, such as cloud computing, SDN / NFV, information-oriented networks, virtual machines (VMs) and containers, smart devices, shared networks and computing offload. This article also provides an overview of MEC's role in 5G and IoT, a detailed introduction to MEC-enabled 5G and IoT applications, and future perspectives for MEC integration with 5G and IoT. Additionally, this article will take a closer look at the MEC research challenges and unresolved issues around 5G and the Internet of Things. Finally, we propose a use case that MEC uses to obtain advanced intelligence in IoT scenarios.
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Liu, Xiang, Xu Zhao, Guojin Liu, Fei Huang, Tiancong Huang, and Yucheng Wu. "Collaborative Task Offloading and Service Caching Strategy for Mobile Edge Computing." Sensors 22, no. 18 (September 7, 2022): 6760. http://dx.doi.org/10.3390/s22186760.
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Mobile edge computing (MEC), which sinks the functions of cloud servers, has become an emerging paradigm to solve the contradiction between delay-sensitive tasks and resource-constrained terminals. Task offloading assisted by service caching in a collaborative manner can reduce delay and balance the edge load in MEC. Due to the limited storage resources of edge servers, it is a significant issue to develop a dynamical service caching strategy according to the actual variable user demands in task offloading. Therefore, this paper investigates the collaborative task offloading problem assisted by a dynamical caching strategy in MEC. Furthermore, a two-level computing strategy called joint task offloading and service caching (JTOSC) is proposed to solve the optimized problem. The outer layer in JTOSC iteratively updates the service caching decisions based on the Gibbs sampling. The inner layer in JTOSC adopts the fairness-aware allocation algorithm and the offloading revenue preference-based bilateral matching algorithm to get a great computing resource allocation and task offloading scheme. The simulation results indicate that the proposed strategy outperforms the other four comparison strategies in terms of maximum offloading delay, service cache hit rate, and edge load balance.
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Hu, Jintian, Gaocai Wang, Xiaotong Xu, and Yuting Lu. "Study on Dynamic Service Migration Strategy with Energy Optimization in Mobile Edge Computing." Mobile Information Systems 2019 (October 13, 2019): 1–12. http://dx.doi.org/10.1155/2019/5794870.
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In the mobile edge computing (MEC) platform, tasks that are being performed often change due to mobile device migration. In order to improve the energy utilization of the MEC platform and the migration process of the mobile terminal and to ensure effective and continuous operation of services, dynamic service migration strategy with energy optimization is required. Aiming at the problem of energy consumption optimization of dynamic service migration with the far-near effect in mobile networks, this article proposes a dynamic service migration strategy with energy optimization, which ensures the performance requirements of the service by considering the minimum energy cost of the relevant equipment during the dynamic migration process. First, by analyzing the relationship between migration distance and equipment transmit power, the energy consumption model associated with the migration distance is established. Then, according to the task dynamic service migration scenario, the dynamic service migration energy consumption model is constructed, so as to obtain the reward function for migrating energy consumption. Finally, the dynamic service migration strategy with energy optimization is realized through the optimal migration energy consumption expectation, which is obtained by the optimal stopping theory. The experimental results show that the optimization strategy proposed in this article can effectively reduce the energy consumption of dynamic service migration in different simulation environments and can improve the dynamic migration performance.
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Pang, Beibei, Fei Hao, Doo-Soon Park, and Carmen De Maio. "A Multi-Criteria Multi-Cloud Service Composition in Mobile Edge Computing." Sustainability 12, no. 18 (September 16, 2020): 7661. http://dx.doi.org/10.3390/su12187661.
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The development of mobile edge computing (MEC) is accelerating the popularity of 5G applications. In the 5G era, aiming to reduce energy consumption and latency, most applications or services are conducted on both edge cloud servers and cloud servers. However, the existing multi-cloud composition recommendation approaches are studied in the context of resources provided by a single cloud or multiple clouds. Hence, these approaches cannot cope with services requested by the composition of multiple clouds and edge clouds jointly in MEC. To this end, this paper firstly expands the structure of the multi-cloud service system and further constructs a multi-cloud multi-edge cloud (MCMEC) environment. Technically, we model this problem with formal concept analysis (FCA) by building the service–provider lattice and provider–cloud lattice, and select the candidate cloud composition that satisfies the user’s requirements. In order to obtain an optimized cloud combination that can efficiently reduce the energy consumption, money cost, and network latency, the skyline query mechanism is utilized for extracting the optimized cloud composition. We evaluate our approach by comparing the proposed algorithm to the random-based service composition approach. A case study is also conducted for demonstrating the effectiveness and superiority of our proposed approach.
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Poluektov, Dmitry S., and Abdukodir A. Khakimov. "Development and analysis of models for service migration to the MEC server based on hysteresis approach." Discrete and Continuous Models and Applied Computational Science 30, no. 3 (October 5, 2022): 244–57. http://dx.doi.org/10.22363/2658-4670-2022-30-3-244-257.
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Online video services are among the most popular ways of content consumption. Video hosting servers have a very high load every day, which we propose to reduce by migrating the application with the video content in demand to the local Multi-access Edge Computing (MEC) server of the target. This makes it possible to improve the quality of services (QoS) provided to users by reducing the transmission delay. Therefore, an architecture has been proposed that allows, at times of increased demand for the same video content, to migrate the video service application to the edge servers of the network operator. To evaluate the performance of this approach, a mathematical model was developed in the form of a queuing system. The results of the numerical experiment make it possible to optimize the time of using local MEC servers to provide video content.
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Yuan, Youwei, Lu Qian, Gangyong Jia, Longxuan Yu, Zixuan Yu, and Qi Zhao. "Efficient Computation Offloading for Service Workflow of Mobile Applications in Mobile Edge Computing." Mobile Information Systems 2021 (April 2, 2021): 1–11. http://dx.doi.org/10.1155/2021/5578465.
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Edge computing has become a promising solution to overcome the user equipment (UE) constraints such as low computing capacity and limited energy. A key edge computing challenge is providing computing services with low service congestion and low latency, but the computing resources of edge servers were limited. User task randomness and network inhomogeneity brought considerable challenges to limited-resource MEC systems. To solve these problems, the presented paper proposed a blocking- and delay-aware schedule strategy for MEC environment service workflow offloading. First, the workflow was modeled in mobile applications and the buffer queue in servers. Then, the server collaboration area was divided through a collaboration area division method based on clustering. Finally, an improved particle swarm optimization scheduling method was utilized to solve this NP-hard problem. Many simulation results verified the effectiveness of the proposed scheme. This method was superior to existing methods, which effectively reduces the blocking probability and execution delay and ensures the quality of the experience of the user.
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Corici, Marius, Pousali Chakraborty, and Thomas Magedanz. "A Study of 5G Edge-Central Core Network Split Options." Network 1, no. 3 (December 20, 2021): 354–68. http://dx.doi.org/10.3390/network1030020.
Full textAbstract:
With the wide adoption of edge compute infrastructures, an opportunity has arisen to deploy part of the functionality at the edge of the network to enable a localized connectivity service. This development is also supported by the adoption of “on-premises” local 5G networks addressing the needs of different vertical industries and by new standardized infrastructure services such as Mobile Edge Computing (MEC). This article introduces a comprehensive set of deployment options for the 5G network and its network management, complementing MEC with the connectivity service and addressing different classes of use cases and applications. We have also practically implemented and tested the newly introduced options in the form of slices within a standard-based testbed. Our performed validation proved their feasibility and gave a realistic perspective on their impact. The qualitative assessment of the connectivity service gives a comprehensive overview on which solution would be viable to be deployed for each vertical market and for each large-scale operator situation, making a step forward towards automated distributed 5G deployments.
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Ale, Laha, Ning Zhang, Scott A. King, and Jose Guardiola. "Spatio-temporal Bayesian Learning for Mobile Edge Computing Resource Planning in Smart Cities." ACM Transactions on Internet Technology 21, no. 3 (June 9, 2021): 1–21. http://dx.doi.org/10.1145/3448613.
Full textAbstract:
A smart city improves operational efficiency and comfort of living by harnessing techniques such as the Internet of Things (IoT) to collect and process data for decision-making. To better support smart cities, data collected by IoT should be stored and processed appropriately. However, IoT devices are often task-specialized and resource-constrained, and thus, they heavily rely on online resources in terms of computing and storage to accomplish various tasks. Moreover, these cloud-based solutions often centralize the resources and are far away from the end IoTs and cannot respond to users in time due to network congestion when massive numbers of tasks offload through the core network. Therefore, by decentralizing resources spatially close to IoT devices, mobile edge computing (MEC) can reduce latency and improve service quality for a smart city, where service requests can be fulfilled in proximity. As the service demands exhibit spatial-temporal features, deploying MEC servers at optimal locations and allocating MEC resources play an essential role in efficiently meeting service requirements in a smart city. In this regard, it is essential to learn the distribution of resource demands in time and space. In this work, we first propose a spatio-temporal Bayesian hierarchical learning approach to learn and predict the distribution of MEC resource demand over space and time to facilitate MEC deployment and resource management. Second, the proposed model is trained and tested on real-world data, and the results demonstrate that the proposed method can achieve very high accuracy. Third, we demonstrate an application of the proposed method by simulating task offloading. Finally, the simulated results show that resources allocated based upon our models’ predictions are exploited more efficiently than the resources are equally divided into all servers in unobserved areas.