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Throughput Maximization of Offloading Tasks in multi-access edge computing Networks for High-Speed Railways

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第9期70卷 9525-9539页

作者： Xu, Junyi Wei, Zhenchun Lyu, Zengwei Shi, Lei Han, Jianghong Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Anhui Peoples R China Engn Res Ctr Safety Crit Ind Measurement & Contro Minist Educ Hefei 230009 Anhui Peoples R China

With the development of high-speed railways (HSRs), people's lives have become more convenient. At the same time, people are demanding more data services on trains in terms of wireless communications and mobile computation. Recently, multi-access edge computing (MEC), a new computing paradigm, has the great potential to improve the user experience of computing services by placing the cloud infrastructures closer to end-users so that users can offload their computation-intensive and delay-sensitive tasks to the edge servers. In this paper, we concentrate on finding proper data routing paths for each offloaded task and each process result, with the objective of maximizing the network throughput by serving as many tasks as possible while meeting the MEC network resource constraints and the user demand constraints within a finite time horizon of equal time slots. It is challenging because of the unspecific data volume of an offloaded task and handovers of task offloading procedures that may frequently occur. Thus, we first investigate the impact of handovers in uplinks and downlinks. Then we propose a graph-based model to portray the resource availability in the network. Furthermore, a heuristic algorithm is devised, which finds a set of single-source-single-destination delay-constrained shortest paths in a series of auxiliary graphs. Finally, we perform an empirical evaluation of the proposed algorithm by simulations. The simulation results show that the proposed algorithm could achieve a higher network throughput than the other two benchmark heuristics.

关键词： Task analysis Handover Delays Rail transportation Heuristic algorithms edge computing Computational modeling multi-access edge computing High-speed railways Delay-constrained shortest path problem Network resource allocation Admission control

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A Cooperative Community-Based Framework for Service Caching and Task Offloading in multi-access edge computing

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第3期21卷 3224-3235页

作者： Liao, Zhuofan Yin, Guiying Tang, Xiaoyong Liu, Penglu Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Peoples R China

In multi-access edge computing, services are cached from cloud servers to the edge base station providing low-latency services. Horizontal Collaboration (HC) between Base Stations (BSs) can alleviate the problem of resource limitation in edge base stations. However, the initiative of cooperation between base stations and the interests of base stations themselves are not guaranteed. In this paper, we study the Joint Service Caching and Task Offloading decision Problem (JSCTOP). To solve this problem, we propose a Two-Stage Optimization Framework (TSOF) based on cooperative community idea to maximize the benefit of each base station. In the first stage, a Collaborative Community Mechanism (CCM) is designed based on the Dissimilarity of Service Types (DST) to enhance the collaborative capability of BSs. In the second stage, an algorithm for service caching and task decision to update and replace the Service Types (STs) of BSs. We also design an algorithm for service pricing to incentivize BSs. In this algorithm, BSs are able to profit from providing services to users and also profit from assisting other base stations. Extensive simulation results show that TSOF outperforms its counterparts in average delay, total energy, total benefit, and community.

关键词： multi-access edge computing community partition caching offloading multi-access edge computing community partition caching offloading

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Energy-efficient computation offloading strategy with task priority in cloud assisted multi-access edge computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年第1期148卷 298-313页

作者： He, Zhenli Xu, Yanan Liu, Di Zhou, Wei Li, Keqin Yunnan Univ Engn Res Ctr Cyberspace Kunming 650500 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Yunnan Univ Sch Software Kunming 650500 Peoples R China Norwegian Univ Sci & Technol Dept Comp Sci N-7491 Trondheim Norway SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

multi-access edge computing (MEC) provides cloud-like services at the edge of the radio access network close to mobile devices (MDs). This infrastructure can provide low-latency services to MDs and significantly reduce the pressure on the backbone network. However, the computing resources configured on an edge server (ES) are limited compared to a cloud data center (DC). It is difficult for ESs to satisfy the demands of MDs anytime and anywhere. Thus, a new paradigm that combines DC with ESs has been proposed to provide better capability and flexibility, namely, cloud-assisted MEC (CA-MEC). In CA-MEC, MDs can offload tasks to ESs and the DC, which means more elasticity and more complicated offloading decisions. This paper studies MDs' energy-efficient computation offloading strategy in CA-MEC, which considers two different priority tasks. First, we establish mathematical models to characterize the CA-MEC environment. Second, we mathematically analyze the MD's average task response time and average power consumption. Third, we propose efficient numerical algorithms to obtain a computation offloading strategy to optimize the energy efficiency of the target MD. Finally, we demonstrate several numerical examples and construct a comparative experiment to show the effectiveness of our algorithms. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Computation offloading Energy efficiency multi-access edge computing Queueing model Task priority

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Joint Communication, Computation, Caching, and Control in Big Data multi-access edge computing

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IEEE TRANSACTIONS ON MOBILE computing 2020年第6期19卷 1359-1374页

作者： Ndikumana, Anselme Tran, Nguyen H. Tai Manh Ho Han, Zhu Saad, Walid Niyato, Dusit Hong, Choong Seon Kyung Hee Univ Dept Comp Sci & Engn Yongin 17104 Gyeonggi Do South Korea Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia Univ Qubec INRS Montreal PQ H3C 3P8 Canada Univ Houston Elect & Comp Engn Dept Houston TX 77004 USA Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

The concept of multi-access edge computing (MEC) has been recently introduced to supplement cloud computing by deploying MEC servers to the network edge so as to reduce the network delay and alleviate the load on cloud data centers. However, compared to the resourceful cloud, MEC server has limited resources. When each MEC server operates independently, it cannot handle all computational and big data demands stemming from users devices. Consequently, the MEC server cannot provide significant gains in overhead reduction of data exchange between users devices and remote cloud. Therefore, joint computing, Caching, Communication, and Control (4C) at the edge with MEC server collaboration is needed. To address these challenges, in this paper, the problem of joint 4C in big data MEC is formulated as an optimization problem whose goal is to jointly optimize a linear combination of the bandwidth consumption and network latency. However, the formulated problem is shown to be non-convex. As a result, a proximal upper bound problem of the original formulated problem is proposed. To solve the proximal upper bound problem, the block successive upper bound minimization method is applied. Simulation results show that the proposed approach satisfies computation deadlines and minimizes bandwidth consumption and network latency.

关键词： Servers Big Data Collaboration Cloud computing Delays Optimization Resource management Communication computation caching distributed control multi-access edge computing 5G network

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SDN Enhanced multi-access edge computing (MEC) for E2E Mobility and QoS Management

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IEEE access 2020年 8卷 77459-77469页

作者： Shah, Syed Danial Ali Gregory, Mark A. Li, Shuo Fontes, Ramon Dos Reis RMIT Univ Sch Engn Melbourne Vic 3000 Australia Fed Inst Educ Sci & Technol Bahia BR-40301015 Salvador BA Brazil

multi-access edge computing (MEC) is a key enabler of the fifth-generation (5G) mobile cellular networks. MEC enables Ultra-reliable and Low-latency Communications (URLLC) by bringing the data and computational resources closer to the mobile users. As 5G deployments commence in earnest, researchers have turned their attention to various aspects of edge computing in an effort to leverage the new capabilities offered by 5G. In this paper, we propose the integration of Software Defined Networking (SDN) and cloud-native virtualization techniques, such as containers, with the MEC architecture, to facilitate the orchestration and management of Mobile edge Hosts (MEH). The proposed architecture focuses on the end-to-end mobility support required to maintain service continuity when mobile users relocate from one MEH to another. SDN is proposed as a reliable, programmatic paradigm to provide mobile edge orchestration and dynamic configuration of the underlying network for improved service continuity and quality of experience. The proposed architecture is validated through vehicle-to-everything simulations that highlight the advantage of the centralized network intelligence and the modularity and portability offered by SDN and containers.

关键词： Software defined networking mobility management multi-access edge computing 5G cloud-native containerization URLLC

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Dynamic D2D multihop Offloading in multi-access edge computing From the Perspective of Learning Theory in Games

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第1期20卷 305-318页

作者： Xie, Jindou Jia, Yunjian Wen, Wanli Chen, Zhengchuan Liang, Liang Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 211189 Peoples R China

In a D2D-enabled MEC system, devices cooperate in task computation by relaying tasks to servers or providing computation capabilities for users. We investigate how nodes choose the roles to join in the offloading process in a dynamic environment, where mobile devices forming a tree-like multihop network can play relays and intermediate executors earning corresponding economic utility. By mathematically modeling the multihop computation offloading, we formulate the task-flow constrained network-wide utility maximization problem as a potential game. Based on the properties of the potential game, we prove the existence of Nash equilibrium and propose two learning-based algorithms, i.e., myopic best response (MBR-CO) and stochastic learning-based computation offloading (SL-CO), to find the equilibrium point in a distributed manner. Theoretical and simulation results show that MBR-CO is dominant in static scenarios, and SL-CO achieves a high utility and stable performance in dynamic scenarios.

关键词： Task analysis Servers Device-to-device communication Games Relays Mobile handsets Spread spectrum communication multi-access edge computing D2D multihop offloading potential game Nash equilibrium stochastic learning

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Computation Offloading in Hierarchical multi-access edge computing Based on Contract Theory and Bayesian Matching Game

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第11期69卷 13686-13701页

作者： Su, Chunxia Ye, Fang Liu, Tingting Tian, Yuan Han, Zhu Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Nanjing Inst Technol Sch Commun Engn Nanjing 211167 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

multi-access edge computing (MEC) has emerged as a promising paradigm because of its good performance for computation-intensive and latency-critical applications. However, the enormous computing requests from computation service subscribers (CSSs) still cannot be satisfied by pre-existing edge computation nodes (ECNs). To fully utilize the advantage of the MEC network, a hierarchical computation offloading framework is developed under network virtualization (NV) scenario. Accordingly, a two-step sequential process is designed to stimulate the proposed framework. In the first step, an incentive mechanism is proposed in which more temporary ECNs can be motivated by MEC operator and then join the MEC network. Without perfect ECN information, the optimal contract items (the ECN's CPU contribution and reward) between the MEC operator and ECNs can be achieved by taking account of individual rationality (IR) and incentive compatible (IC) constraints. After acquiring the ECNs' CPU contributions, the computing resource allocation problem between the ECNs and CSSs is then considered in the second step. Since the CSSs have private information, a Bayesian matching game with externality is leveraged to model the problem. Whereas, the conventional resident-oriented Gale-Shapley (RGS) algorithm cannot ensure the stability. Hence, an iterative matching algorithm that can always converge to stable results is developed. Finally, simulation results demonstrate that our proposed two-step sequential decision process can significantly improve social welfare considering the practical scenarios, with reasonable computational complexity.

关键词： Contracts Cascading style sheets Resource management edge computing Bayes methods Computational modeling Delays Bayesian matching game contract theory hierarchical framework multi-access edge computing

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DRL-Based Offloading for Computation Delay Minimization in Wireless-Powered multi-access edge computing

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第3期71卷 1755-1770页

作者： Zheng, Kechen Jiang, Guodong Liu, Xiaoying Chi, Kaikai Yao, Xinwei Liu, Jiajia Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou 310023 Peoples R China Northwestern Polytech Univ Sch Cybersecur Xian 710072 Peoples R China

Wireless power transfer (WPT) and edge computing have been validated as effective ways to solve the energy-limited problem and computation-capacity-limited problem of wireless devices (WDs), respectively. This paper studies the wireless-powered multi-access edge computing (WP-MEC) network, where WDs conduct either local computing or task offloading for their individable computation tasks. We aim to minimize total computation delay (TCD) when each WD has a computation task to execute, referred to as the total computation delay minimization (TCDM) problem, by jointly optimizing the offloading-decision, WPT duration, and transmission durations of offloading WDs. The TCDM problem is a mixed integer programming (MIP) problem that is challenging to efficiently obtain the optimal or near-optimal solution. To tackle this challenge, we decompose the TCDM problem into the sub-problem of optimizing the WPT duration and transmission durations, and the top-problem of optimizing the offloading decision. For the nonconvex sub-problem, we design a worst-WD-adjusting (WDA) algorithm to efficiently obtain its optimal solution. For the top-problem, under the time-varying channel conditions, traditional optimization methods are hard to determine the optimal or near-optimal offloading decision within the channel coherence duration. To fast obtain the near-optimal offloading decision, we propose a deep neural networks (DNN)-based deep reinforcement learning (DRL) model, which takes the sub-problem solving as one component for utility evaluation. Finally, numerical results demonstrate that the proposed online DRL-based offloading algorithm achieves the near-minimal TCD with low computational complexity, and is suitable for the fast-fading WP-MEC network.

关键词： multi-access edge computing wireless power transfer deep reinforcement learning computation delay

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Spectrum Management for multi-access edge computing in Autonomous Vehicular Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2020年第7期21卷 3001-3012页

作者： Peng, Haixia Ye, Qiang Shen, Xuemin Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

In this paper, a dynamic spectrum management framework is proposed to improve spectrum resource utilization in a multi-access edge computing (MEC) in autonomous vehicular network (AVNET). To support the increasing communication data traffic and guarantee quality-of-service (QoS), spectrum slicing, spectrum allocating, and transmit power controlling are jointly considered. Accordingly, three non-convex network utility maximization problems are formulated to slice spectrum among base stations (BSs), allocate spectrum among autonomous vehicles (AVs) associated with a BS, and control transmit powers of BSs, respectively. Through linear programming relaxation and first-order Taylor series approximation, these problems are transformed into tractable forms and then are jointly solved through an alternate concave search (ACS) algorithm. As a result, the optimal spectrum slicing ratios among BSs, optimal BS-vehicle association patterns, optimal fractions of spectrum resources allocated to AVs, and optimal transmit powers of BSs are obtained. Based on our simulation, a high aggregate network utility is achieved by the proposed spectrum management scheme compared with two existing schemes.

关键词： Servers Wireless fidelity Radio spectrum management Resource management Dynamic scheduling Quality of service edge computing multi-access edge computing NFV spectrum resource allocation QoS-guaranteed service autonomous vehicles

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Game Theory for multi-access edge computing: Survey, Use Cases, and Future Trends

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2019年第1期21卷 260-288页

作者： Moura, Jose Hutchison, David ISCTE Inst Univ Lisboa Sch Technol & Architecture P-1649026 Lisbon Portugal Univ Lancaster Sch Comp & Commun InfoLab21 Lancaster LA1 4WA England

Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios.

关键词： Wireless networks resource management model games fog environment multi-access edge computing

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