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Mobility-Aware and Code-Oriented Partitioning Computation Offloading in multi-access edge computing

JOURNAL OF GRID computing 2022年第2期20卷 1-15页

作者： Liu, Yaqin Liu, Chubo Liu, Jing Hu, Yikun Li, Kenli Li, Keqin Hunan Univ Dept Informat Sci & Engn Changsha Peoples R China Natl Supercomp Ctr Changsha Changsha 410082 Hunan Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA Wuhan Univ Sci & Technol Dept Comp Sci & Technol Wuhan 430065 Peoples R China

multi-access edge computing (MEC) enables less resourceful smart mobile devices (SMDs) to use computation and memory intensive applications by offloading them to edge servers with tolerant latency, significantly improving the computing paradigm of SMDs. But, when involving mobility in MEC, offloading strategy and overhead can be significantly influenced by the movements of SMDs. What(')s more, the movements of SMDs make it harder to deal with precedence among subtasks. However, to the best of our knowledge, few articles have studied mobility management in code-oriented partitioning offloading. To give an efficient solution, we propose the cost-saving offloading policy with mobility prediction using convex optimization and Lagrangian approach. Our scheme can help moving SMDs in MEC like driverless vehicles efficiently complete their tasks and reveal the impact of task dependency on completion time. The experimental results show that our algorithm can achieve at least 12% performance improvement on average than other three common methods.

关键词： Computation offloading multi-access edge computing Mobility management Resource allocation Task dependency

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Efficient Task Offloading in multi-access edge computing Servers using Asynchronous Meta Reinforcement Learning in 5G 29

Efficient Task Offloading in Multi-access Edge Computing Ser...

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29th IEEE Symposium on Computers and Communications (IEEE ISCC)

作者： Ashengo, Yeabsira Asefa Yahiya, Tara Ali Zema, Nicola Roberto Univ Paris Saclay CNRS Lab Interdisciplinaire Sci Numer Gif Sur Yvette France Addis Ababa Inst Technol Sch Elect & Comp Engn Addis Ababa Ethiopia

ISBN: (纸本)9798350354249;9798350354232

The deployment of 5G networks has ushered in a new era for technologies that demand Ultra Reliable Low Latency (URLL) networks. However, the User Equipment (UE) responsible for handling such applications may not always have the computational capacity to meet the demanding requirements of URLL implementations. In such scenarios, certain tasks can be offloaded to a high-performance computing network known as multi-access edge computing (MEC), located near the UE at a base station. Nonetheless, MEC servers themselves have computational limitations, and they can't handle all the tasks from all the UEs efficiently. To address this challenge, we propose an efficient task offloading approach based on asynchronous Meta Reinforcement Learning (MRL). Our method involves developing an algorithm that prioritizes and organizes UE tasks based on their importance and interdependencies, and a Deep asynchronous MRL algorithm models the task offloading policy. What sets our model apart is its ability to adapt quickly to a variety of environments, whether they are heterogeneous or homogeneous. Our simulation results show that our solution has a 60% overall performance improvement in terms of training latency, energy consumption, and memory and CPU time usage compared to a benchmark.

关键词： multi-access edge computing Machine Learning Task Offloading Reinforcement learning

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Fuzzy Logic-Based Orchestration of multi-access edge computing in LEO Satellite Constellations 59

Fuzzy Logic-Based Orchestration of Multi-Access Edge Computi...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Rojas, Camilo Fraire, Juan A. Patrone, Fabio Marchese, Mario Univ Genoa Genoa Italy INRIA INSA Lyon CITI UR3720 F-69621 Villeurbanne France Univ Nacl Cordoba CONICET Cordoba Argentina

ISBN: (纸本)9798350304060;9798350304053

In the fast-evolving domain of satellite communications, massive constellations of LEO satellites are increasingly gaining notice in both academic and industrial sectors. Operating these growing constellations has become a complex challenge, and new obstacles have emerged with integrating cellular networks in the space sector. Some techniques like multi-access edge computing (MEC) were initially designed for static terrestrial networks and should be adapted to the dynamic nature of Non-Terrestrial Networks (NTN). This study introduces an innovative Fuzzy Logic-based controller tailored explicitly for determining the operational status of satellite MEC servers based on historical task loads and task processing failures. Our approach is grounded in a distributed control framework, where each satellite node operates autonomously relying on its own data and limited information from neighboring nodes. We evaluated our fuzzy controller against a baseline strategy, demonstrating significant improvements in reducing the overall active time of MEC nodes. The orchestration effectiveness of these strategies was tested by using our custom-built continuous time emulation framework for satellite constellations. This framework integrates 1) the Simplified General Perturbations Model 4 (SGP4) for orbit propagation, 2) Mininet for network virtualization, 3) Docker for operating system virtualization, and 4) MEC task offloading and orchestration.

关键词： Satellite Constellation multi-access edge computing

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multi-access edge computing (MEC) Based on MIMO: A Survey

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SENSORS 2023年第8期23卷 3883页

作者： Zhu, Mengyu Gao, Shaoshuai Tu, Guofang Chen, Deyuan Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 101408 Peoples R China

With the rapid development of wireless communication technology and the emergence of intelligent applications, higher requirements have been put forward for data communication and computing capacity. multi-access edge computing (MEC) can handle highly demanding applications by users by sinking the services and computing capabilities of the cloud to the edge of the cell. Meanwhile, the multiple input multiple output (MIMO) technology based on large-scale antenna arrays can achieve an order-of-magnitude improvement in system capacity. The introduction of MIMO into MEC takes full advantage of the energy and spectral efficiency of MIMO technology, providing a new computing paradigm for time-sensitive applications. In parallel, it can accommodate more users and cope with the inevitable trend of continuous data traffic explosion. In this paper, the state-of-the-art research status in this field is investigated, summarized and analyzed. Specifically, we first summarize a multi-base station cooperative mMIMO-MEC model that can easily be expanded to adapt to different MIMO-MEC application scenarios. Subsequently, we comprehensively analyze the current works, compare them to each other and summarize them, mainly from four aspects: research scenarios, application scenarios, evaluation indicators and research issues, and research algorithms. Finally, some open research challenges are identified and discussed, and these indicate the direction for future research on MIMO-MEC.

关键词： multiple input multiple output multi-access edge computing

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NOMA Enabled multi-access edge computing: A Joint MU-MIMO Precoding and Computation Offloading Design 33

NOMA Enabled Multi-Access Edge Computing: A Joint MU-MIMO Pr...

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33rd International Symposium on Industrial Electronics (ISIE)

作者： Zhang, Deyou Lyu, Honghao Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China

ISBN: (纸本)9798350394085;9798350394092

This paper investigates computation offloading and transmit precoding co-design for multi-access edge computing (MEC), where multiple MEC users (MUs) equipped with multiple antennas access the MEC server in a non-orthogonal multiple access manner. We aim to minimize the total energy consumption of all MUs while satisfying the latency constraints by jointly optimizing the computational frequency, offloading ratio, and precoding matrix of each MU. For tractability, we first decompose the original problem into three subproblems and then solve these subproblems iteratively until convergence. Simulation results validate the convergence of the proposed method and demonstrate its superiority over baseline algorithms.

关键词： multi-access edge computing non-orthogonal multiple access computation offloading precoding design

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Optimal Slicing of Virtualized Passive Optical Networks to Support Dense Deployment of Cloud-RAN and multi-access edge computing

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IEEE NETWORK 2022年第2期36卷 131-138页

作者： Das, Sandip Slyne, Frank Ruffini, Marco Trinity Coll Dublin CONNECT Ctr Dublin Ireland Trinity Coll Dublin CONNECT Dublin Ireland Trinity Coll Dublin Dublin Ireland

The commercialization of Cloud-RAN, and Open-RAN in particular, is a key factor to enable 5G cell densification by providing lower cost and more agile deployment of small cells. In addition, the adoption of multi-access edge computing (MEC) is important to support the ultra-low latency and high reliability required by mission-critical applications, which constitute a milestone of the 5G and beyond vision of a fully connected society. However, connecting antenna site, C-RAN processing, and MEC at low cost is challenging, as it requires high-capacity, low-latency connectivity delivered through a highly inter-connected topology. While passive optical networks (PONs) are being considered as a solution for providing low-cost connectivity to C-RAN, they only allow data transmission from the endpoints (e.g., hosting the radio unit, RU, at the antenna site) toward a central node (e.g., the central office, hosting computing equipment), and thus cannot support traffic from RUs toward MEC end nodes that could host the distributed unit (DU) and possibly central unit (CU) and network core. This has led to research into the evolution of PON architectures with the ability to provide direct communications between endpoints, thus supporting mesh traffic patterns required by MEC installations. In this context, virtualization plays a key role in enabling efficient resource allocation (i.e., optical transmission capacity) to endpoints, according to their communication patterns. In this article, we address the challenge of dynamic allocation of virtual PON slices over mesh-PON architectures to support C-RAN and MEC nodes. We make use of a mixed analytical-iterative model to compute optimal virtual PON slice allocation, with the objective of minimizing the use of MEC node resources, while meeting a target latency threshold (100 mu s in our scenario). Our method is particularly effective in reducing computation time, enabling virtual PON slice allocation in timescales compatible with real

关键词： multi-access edge computing Costs Microprocessors Computational modeling Computer architecture Real-time systems Passive optical networks Radio access networks Cloud computing

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Resource management and switch migration in SDN-based multi-access edge computing environments

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JOURNAL OF SUPERcomputing 2022年第13期78卷 15532-15566页

作者： Guo, Jingjing Li, Chunlin Luo, Youlong State Key Lab Safety & Hlth Met Mines Maanshan 243000 Anhui Peoples R China Naval Univ Engn Coll Power Engn Wuhan 430033 Peoples R China Wuhan Univ Technol Dept Comp Sci Wuhan 430063 Peoples R China

As the growing of data volumes due to the successive development of new mobile devices and the creation of new applications, the emergence of multi-access edge computing can successfully improve quality of service based on reduced latency and lower system energy consumption. The introduction of software-defined networking technologies in multi-access edge computing environments supports access to more network devices and enhances the scalability and service management flexibility of mobile edge computing environments. The limited nature of computing resources in mobile edge computing environments makes resource management a critical issue. Therefore, to minimize the energy consumption and latency of task execution in mobile edge computing environment, and to ensure reasonable resource allocation during task execution, a resource management strategy based on multi-objective optimization in edge computing environment is proposed. In this strategy, the overall energy consumption weighting and minimization problem is solved by optimizing the management of communication and computing resources, and an improved NSGA-II algorithm is proposed to rationally allocate communication and computational resources for each task. To deal with load imbalance caused by large traffic fluctuations in multi-access edge computing environments based on software-defined networks, in this paper, a load-balancing-oriented switch migration strategy is proposed in which a switch migration algorithm based on an improved ant colony algorithm is proposed to optimally select the switch migration process so that the static deployment of the controller adapts to the changing needs of dynamic flows in the network. Experimental results demonstrate that the proposed resource management strategy minimizes the latency and energy consumption during task execution and increases resource utilization and average throughput of servers. The proposed switch migration strategy can effectively achieve load balanci

关键词： multi-access edge computing Software-defined network Resource management Switch migration

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Intelligence Networking for Autonomous Driving in Beyond 5G Networks With multi-access edge computing

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第6期71卷 5853-5866页

作者： Wu, Mengyao Yu, F. Richard Liu, Peter Xiaoping Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

Artificial intelligence (AI)-powered autonomous vehicles (AVs) can integrate different machine learning (ML) techniques to build up a complex autonomous driving system. However, single AV intelligence is not enough to cope with ever-changing driving environments. The underlying reason is that, with current neural network design and training algorithms, it is challenging for the driving model to generalize to diverse driving environments all at once due to sample inefficiency and the curse of dimensionality. Powerful computing resources and massive amount of data can be used to train a good driving model offline. However, the driving model obtained offline might fail in corner case scenarios. In this paper, we propose an intelligence networking framework among AVs assisted by multi-access edge computing (MEC) with end-to-end learning for demonstration. In this framework, driving road is divided into segments and data is collected for each road segment separately. Assisted by MEC networks, a continuously updated driving model is produced in near real-time for each road segment when the environment changes. By dividing the road into segments, we aim to reduce the burden of generalization since a single model only needs to adapt to a specific road segment. Simulation results show that our solutions can produce updated driving model for each road segment to adapt to environmental changes better than the existing scheme. Upcoming AVs can then adapt to changing environments by downloading the updated driving model.

关键词： Roads Autonomous vehicles Computational modeling Adaptation models Training Data models Task analysis Intelligence networking Autonomous driving multi-access edge computing

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The impact of 5G multi-access edge computing cooperation announcement on the telecom operators' firm value

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ETRI JOURNAL 2022年第4期44卷 588-598页

作者： Nam, Sangjun Elect & Telecommun Res Inst Ind Inst Res Sect Daejeon South Korea

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

关键词： 5G event study innovation uncertainty multi-access edge computing strategic alliances

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Determinantal point process-based new radio unlicensed link scheduling for multi-access edge computing

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2022年第5期25卷 2215-2239页

作者： Xing, Chigang Li, Yangfan Chen, Cen Li, Fangmin Zeng, Zeng Zou, Xiaofeng Wuhan Univ Technol Sch Informat Engn Luoshi Rd 122 Wuhan 430070 Hubei Peoples R China Hunan Univ Coll Informat Sci & Engn Lushan Rd Changsha 410082 Hunan Peoples R China Hunan Univ Shenzhen Inst Keyuan South Rd Shenzhen 518055 Guangdong Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Fusionopolis Way 20-10 Singapore Singapore Shanghai Univ Sch Microelect Shangda Rd Shanghai 518055 Peoples R China Changsha Univ Dept Math & Comp Sci Hongshan Rd 98 Changsha 410022 Hunan Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Lushan Rd Changsha 410082 Hunan Peoples R China

The combination of multi-access edge computing (MEC) and New Radio Unlicensed (NR-U) can provide more powerful computing power and wireless access for the Internet of Things (IoT). An essential function in NR-U is to allocate time-frequency resources for IoT devices while ensuring coexistence with wireless local area networks (Wi-Fi) in the same spectrum. However, in the licensed assisted access (LAA) based NR-U, the listen-before-talk (LBT) mechanism makes the nodes located at the edges of the NR-U suffer from the hidden node problem. This can lead to a waste of time-frequency resources in NR-U and degrade the system performance. In this paper, we propose a determinantal point process (DPP) based link scheduling to improve the terminals downlink performance in the hidden nodes zone. Instead of scheduling relay links based on assignment methods, we introduce a recommendation scheme that recommends the D2D transmission nodes from a set of candidate nodes for hidden nodes and establishes D2D relay links. The recommendation scheme is designed based on a DPP model. The simulation results demonstrate that the proposed DPP method is capable of effectively selecting the optimal transmitting nodes and optimizing more hidden nodes while maximizing the sum rate.

关键词： link scheduling determinantal point process hidden node problem new radio unlicensed device-to-device multi-access edge computing

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