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Reverse Auction-Based computation offloading and Resource Allocation in Mobile Cloud-Edge Computing

IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第10期22卷 6144-6159页

作者： Zhou, Huan Wu, Tong Chen, Xin He, Shibo Guo, Deke Wu, Jie China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Hubei Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha 410073 Hunan Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

This article proposes a novel Reverse Auction-based computation offloading and Resource Allocation Mechanism, named RACORAM for the mobile Cloud-Edge computing. The basic idea is that the Cloud Service Center (CSC) recruits edge server owners to replace it to accommodate offloaded computation from nearby resource-constraint Mobile Devices (MDs). In RACORAM, the reverse auction is used to stimulate edge server owners to participate in the offloading process, and the reverse auction-based computation offloading and resource allocation problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) problem, aiming to minimize the cost of the CSC. The original problem is decomposed into an equivalent master problem and subproblem, and low-complexity algorithms are proposed to solve the related optimization problems. Specifically, a Constrained Gradient Descent Allocation Method (CGDAM) is first proposed to determine the computation resource allocation strategy, and then a Greedy Randomized Adaptive Search Procedure based Winning Bid Scheduling Method (GWBSM) is proposed to determine the computation offloading strategy. Meanwhile, the CSC's payment determination for the winning edge server owners is also presented. Simulations are conducted to evaluate the performance of RACORAM, and the results show that RACORAM is very close to the optimal method with significantly reduced computational complexity, and greatly outperforms the other baseline methods in terms of the CSC's cost under different scenarios.

关键词： computation offloading resource allocation reverse auction mobile cloud-edge computing

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AoI-aware energy control and computation offloading for industrial IoT

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年 139卷 29-37页

作者： Huang, Jiwei Gao, Han Wan, Shaohua Chen, Ying China Univ Petr Beijing Key Lab Petr Data Min Beijing 102249 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China Beijing Informat Sci & Technol Univ Sch Comp Sci Beijing 100101 Peoples R China

In Industrial Internet of Things (IIoT), a large volume of data is collected periodically by IoT devices, and timely data routing and processing are important requirements. Age of Information (AoI), which is a metric to evaluate the freshness of status information in data processing, has become one of the most important objectives in IIoT. In this paper, considering limited communication, computation and energy resources on IoT devices, we jointly study the optimal AoI-aware energy control and computation offloading problem within a dynamic IIoT scenario with multiple IoT devices and multiple edge servers. Based on extensive analysis of real-life IoT dataset, Markovian queueing models are constructed to capture the dynamics of IoT devices and edge servers, and their corresponding analyses are provided. With the quantitative analytical results, we formulate a dynamic Markov decision problem with the objective of minimizing the long-term energy consumption while satisfying AoI constraints for real-time data processing. To solve the problem, we apply Deep Reinforcement Learning (DRL) techniques for adapting to large-scale dynamic IIoT environments, and design an intelligent Energy Control and computation offloading (ECCO) algorithm. Extensive simulation experiments are conducted based on real-world dataset, and the comparison results illustrate the superiority of our ECCO algorithm over both existing DRL and non-DRL algorithms.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Age of Information (AoI) computation offloading Deep Reinforcement Learning (DRL) Industrial Internet of Things (IIoT)

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computation offloading Based on Game Theory in Multi-access Edge Computing for 6G Network 14

Computation Offloading Based on Game Theory in Multi-access ...

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14th International Conference on Communication Software and Networks (ICCSN)

作者： Gao, Lixue Chen, Xin Yin, Bo Wu, Bilian Beijing Informat Sci & Technol Univ Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781665453288

Multi-access Edge Computing (MEC) can provide efficient computation offloading service for mobile devices (MDs) in the sixth-generation (6G) network. Owing to ubiquitous connectivity of 6G, dense base stations (BSs) need to be deployed globally. However, it will emerge massive overlapping coverage of BSs, which brings great offloading challenges for MDs in overlapping areas. In this paper, we focus on the computation offloading problem in a 6G-supported MEC system with multiple BSs and multiple MDs, where MDs in the overlapping area of BSs' service scopes. Moreover, a Stackelberg game is formulated to investigate the economic effect and offloading strategic interactions between BSs and MDs. The existence and uniqueness of Stackelberg Equilibrium (SE) are proved by using backward induction approach. In addition, various factors are considered such as the economic incentive of BSs, the social effect of MDs and the delay constraint of tasks. Then, we propose the optimal price and best response algorithm (OPBR) to maximize the utilities of BSs and MDs. Finally, numerical simulation results demonstrate that OPBR algorithm can reach SE and has better performance through comparing with other algorithms.

关键词： 6G Multi-access Edge Computing Stackelberg game computation offloading

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Energy-efficient multiuser and multitask computation offloading optimization method

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Intelligent and Converged Networks 2023年第1期4卷 76-92页

作者： Meini Pan Zhihua Li Junhao Qian School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China School of IoT Engineering Jiangnan UniversityWuxi 214122China

For dynamic application scenarios of Mobile Edge Computing(MEC),an Energy-efficient Multiuser and Multitask computation offloading(EMMCO)optimization method is *** the consideration of multiuser and multitask computation offloading,first,the EMMCO method takes into account the existence of dependencies among different tasks within an implementation,abstracts these dependencies as a Directed Acyclic Graph(DAG),and models the computation offloading problem as a Markov decision ***,the task embedding sequence in the DAG is fed to the RNN encoder-decoder neural network with combination of the attention mechanism,the long-term dependencies among different tasks are successfully captured by this ***,the Improved Policy Loss Clip-based PPO2(IPLC-PPO2)algorithm is developed,and the RNN encoder-decoder neural network is trained by the developed *** loss function in the IPLC-PPO2 algorithm is utilized as a preference for the training process,and the neural network parameters are continuously updated to select the optimal offloading scheduling *** results demonstrate that the proposed EMMCO method can achieve lower latency,reduce energy consumption,and obtain a significant improvement in the Quality of Service(QoS)than the compared algorithms under different situations of mobile edge network.

关键词： Mobile Edge Computing(MEC) computation offloading Reinforcement Learning(RL) optimization model

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Efficient computation offloading and Resource Allocation Scheme for Opportunistic Access Fog-Cloud Computing Networks

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第2期9卷 521-533页

作者： Sun, Wen-Bin Xie, Jian Yang, Xin Wang, Ling Meng, Wei-Xiao Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Harbin Inst Technol Commun Res Ctr Harbin 150001 Peoples R China

Fog-cloud computing is one of cores techniques in wireless networks, where fog and cloud nodes provide high-speed and large-scale computing services to mobile users through a collaborative method. For the conventional fog-cloud computing schemes, the computing abilities of nodes and the computation task offloading methods are the main factors affecting the latency and energy consumption. However, when computing abilities of nodes reach saturation, the latency and energy consumption caused by task data transmission are approximate to those caused by computation. In order to reduce latency and energy consumption of data transmission and computation, this paper proposes an opportunistic access fog-cloud computing network (OFCN), where each mobile user selects fog node through opportunistic access method. Then, we formulate an optimization problem, considering both resource allocation and computation offloading under the constraints of users' quality of service (QoS) requirements. Due to the difficulty of solving, we divide the original problem into four suboptimal problems and develop an iterative algorithm to approach the global optimal solution. Numerical results show that the proposed OFCN can achieve lower latency and energy consumption than the conventional fog-cloud computing networks.

关键词： computation offloading resource allocation opportunistic access fog-cloud computing network

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GaMiCO: Game-slicing based Multi-interface computation offloading in 5G Vehicular Networks

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JOURNAL OF COMMUNICATIONS AND NETWORKS 2023年第4期25卷 491-506页

作者： Jung, Suhwan Kim, Hyoil Zhang, Xinyu Dey, Sujit Ulsan Natl Inst Sci & Technol Dept Elect Engn Ulsan 44919 South Korea Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

Smart vehicles require constantly running heavy vehicular computations with their limited computation/energy resources. 5G vehicular networks have potential to resolve the issue, by letting the vehicular tasks offloaded to 5G mobile edge computing (MEC) servers. To better support vehicular computation offloading, this paper proposes a road-side 5G infrastructure consisting of multiple millimeter-wave (mmWave) small-cell base stations (BSs) and a cellular mid-band based macro-cell BS where each BS is equipped with an MEC server. Then, the vehicles with mmWave/mid-band dual interfaces can decide which BS to choose for offloading. We propose a decentralized offloading decision mechanism where each vehicle tries to minimize the time-energy joint cost with three choices: local computing, offloading to a small-cell MEC, offloading to a macro-cell MEC. In particular, we model the problem as an ordinal potential game, derive its potential function to ensure the existence of and finite-time convergence to a Nash equilibrium (NE), analyze its Price-of-Anarchy, and develop an iterative offloading decision update algorithm. In doing so, we also consider slicing the global game into multiple non-overlapping smaller games and running them in parallel, to investigate the best slicing strategy. Our extensive simulations show the game's real-time convergence to an NE, reveal the NE's near-optimal performance, and present the efficacy of the proposed game slicing.

关键词： 5G computation offloading game slicing millimeter wave mobile edge computing potential game vehicular network

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RMDDQN-Learning: computation offloading Algorithm Based on Dynamic Adaptive Multi-Objective Reinforcement Learning in Internet of Vehicles

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第9期72卷 11374-11388页

作者： Zhang, Xiangjun Wu, Weiguo Zhao, Zhihe Wang, Jinyu Liu, Song Xi An Jiao Tong Univ Xian 710049 Peoples R China Chinese Univ Hong Kong Hong Kong 999077 Peoples R China

As a promising computing paradigm driven by 5G, mobile edge computing (MEC) empowers smart vehicles to offload computation-intensive tasks to edge devices in the Internet of Vehicles (IoV), thereby providing a plethora of exciting applications (e.g. on-board AR/VR, autonomous driving, etc.) with the unique quality of service (QoS) guarantees. However, a key challenge of MEC is howto keep the delay and energy consumption to aminimum in the computation offloading process, while ensuring the privacy security of offloaded data and the load balancing of edge resources. Namely, howto simultaneously optimize multiple indicators that affect computation offloading, which creates a challenging multi-objective optimization (MOO) problem. Aiming at the MOO problem, we propose a novel multi-objective reinforcement learning (MORL) algorithm based on double deep Q-network (DDQN). Each DDQN agent obtains rewards on different objectives according to different reward functions, and dynamically approximates the optimal offloading decision on multiple objectives. To conquer the trade-off problem among multiple conflicting objectives, we propose a weight-learning network based on radial basis function (RBF) networks, which dynamically adjusts the weights by learning the value changes among the objectives. Interestingly, we discovered encouraging potential ofMORLfor solving computation offloading problems in IoV, and numerical results show that the proposed algorithm outperforms traditional reinforcement learning methods by 30.24% in terms of overall energy consumption.

关键词： computation offloading Internet of Vehicles mobile edge computing multi-objective reinforcement learning radial basis neural networks

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computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

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CONCURRENCY AND computation-PRACTICE & EXPERIENCE 2024年第13期36卷 e8050-e8050页

作者： Kanupriya Chana, Inderveer Goyal, Raman Kumar Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala Punjab India

In today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state-of-the-art CO techniques in edge computing (EC) in the context of energy, Quality of Service (QoS), authentication and traceability. In this SLR, the evolution of offloading techniques is analyzed in detail. A total of 138 articles, spanning from 2016 to 2023 (till date), have been classified into QoS, energy, and authentication and traceability-based CO techniques. The optimization-based techniques are the most preferred choices to improve the QoS and reduce energy in the research field of CO in EC. In addition, this paper explores the significant issues and challenges that require further investigation. For future research, energy, QoS, and data provenance for dependent or dynamic task offloading in mobility scenarios can be explored further.

关键词： authentication computation offloading edge computing energy consumption provenance QoS

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JOINT COMPUTING AND COMMUNICATION RESOURCE ALLOCATION FOR TDMA-BASED BINARY computation offloading 49

JOINT COMPUTING AND COMMUNICATION RESOURCE ALLOCATION FOR TD...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Manouchehrpour, M. Amin Davidson, Timothy N. McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada

ISBN: (纸本)9798350344868;9798350344851

Mobile edge computing systems provide computational resources at the network access point that can be shared among the attached devices. Effective utilization of these resources requires careful selection of the offloading devices and joint allocation of the computing and communication resources. We develop an efficient resource allocation technique that exploits the temporal structure of computation offloading. Our initial formulation is combinatoric, but insight into the energy required to communicate a given message reveals effective heuristics for the allocation of computing resources, and a reduced-dimension convex formulation for the communication resources. Our numerical results demonstrate that the proposed approach provides performance that is close to that of the jointly optimal solution.

关键词： computation offloading mobile edge computing

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Joint Optimization of Security Strength and Resource Allocation for computation offloading in Vehicular Edge Computing

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第12期22卷 8751-8765页

作者： Xiao, Huizi Zhao, Jun Feng, Jie Liu, Lei Pei, Qingqi Shi, Weisong Xidian Univ Sch Telecommun Engn State Key Lab ISN Xian 710071 Shaanxi Peoples R China Univ Shaanxi Prov Engn Res Ctr Trusted Digital Econ Xian 710049 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Xian Univ Posts & Telecommun Shaanxi Key Lab Informat Commun Network & Secur Xian 710121 Shaanxi Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA

Vehicular Edge Computing (VEC) is a promising new paradigm that has attracted much attention in recent years, which can enhance the storage and computing capabilities of vehicular networks to provide users with low latency and high-quality services. Due to the open access and unreliable wireless channels, some appropriate security measures should be implemented in the VEC to ensure information security. However, the operation of the security mechanism dominates supererogatory computing resources, thus affecting the performance of VEC systems. The scarcity of computation and energy resources of the vehicles conflicts with the requirement of tasks for time delay and information security. In this paper, taking the driving velocity and position of the vehicles, the number of lanes, the model and density of the attackers, and security strength into consideration, we formulate a max-min optimization problem to jointly optimize offloading decision, transmit power, task computation frequency, encryption computation frequency, edge computation frequency, and block length to obtain optimal secure information capacity and local computation delay. The formulated optimization problem is a mixed integer nonlinear programming (MINLP), which is intractable. We apply the generalized benders decomposition (GBD)-based method to solve it. The simulation results show that our proposed algorithms have convergence and effectiveness and achieve fairness among vehicles on the road.

关键词： computation offloading resource allocation secure information capacity vehicular edge computing

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