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Joint Optimization of computation offloading and Resource Allocation in C-RAN With Mobile Edge Computing Using Evolutionary Algorithms

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IEEE ACCESS 2023年 11卷 112693-112705页

作者： Singh, Sumit Kim, Dong Ho Seoul Natl Univ Sci & Technol Dept Integrated IT Engn Seoul 01811 South Korea Seoul Natl Univ Sci & Technol Dept IT Media Engn Seoul 01811 South Korea

Mobile Edge Computing has been widely recognized as a key enabler for new latency-sensitive applications on resource constrained mobile devices. The objective to offload a computationally intensive task to a cloud server is in general intended to reduce the system's energy consumption and/or latency. In this paper, we attempt to examine how profitable computation offloading is from a service provider's perspective. The joint optimization of radio and computing resources along with offloading decisions results in a mixed integer nonlinear optimization problem which belongs to the class of NP-hard problems. To counter this challenge, we decouple the offloading decision from the resource allocation problem. Initially, approximately optimal offloading decisions are determined using evolutionary algorithms such as genetic algorithms and binary particle swarm optimization algorithms. After several iterations of the evolutionary process to make offloading decisions, the optimal solution is ultimately obtained that performs resource allocation based on exact calculation of the profit value. For faster execution of the evolutionary algorithm, instead of using an optimization solver to find the exact solution, we use a novel approach to seeding the initial population and a regression-based machine learning method to predict the optimal resource allocation values to minimize the objective function evaluation time. According to the simulations performed as part of this study, the proposed evolutionary algorithms outperform existing spectral efficiency-based offloading algorithm in terms of profitability, with shorter execution times as well. The effects of resource availability and the parameters of the algorithm on the profitability of offloading are also examined.

关键词： computation offloading genetic algorithm binary PSO profit in MEC

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Handover-Enabled Dynamic computation offloading for Vehicular Edge Computing Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第7期72卷 9394-9405页

作者： Maleki, Homa Basaran, Mehmet Durak-Ata, Lutfiye Istanbul Tech Univ Informat Inst Informat & Commun Res Grp ICRG TR-34469 Istanbul Turkiye 6GEN Lab TR-34854 Istanbul Turkiye Istanbul Tech Univ Informat & Commun Res Grp TR-34469 Istanbul Turkiye

The computation offloading technique is a promising solution that empowers computationally limited resource devices to run delay-constrained applications efficiently. Vehicular edge computing incorporates the processing capabilities into the vehicles, and thus, provides computing services for other vehicles through computation offloading. Mobility affects the communication environment and leads to critical challenges for computation offloading. In this paper, we consider an intelligent task offloading scenario for vehicular environments including smart vehicles and roadside units, which can cooperate to perform resource sharing. Intending to minimize the average offloading cost which takes into account energy consumption together with delay in transmission and processing phases, we formulate the task offloading problem as an optimization problem and implement an algorithm based on deep reinforcement learning with Double Q-learning which allows user equipments to learn the offloading cost performance by observing the environment and make steady sequences of offloading decisions despite the uncertainties of the environment. Besides, concerning the high mobility of the environment, we propose a handover-enabled computation offloading strategy that leads to a better quality of service and experience for users in beyond 5G and 6G heterogeneous networks. Simulation results demonstrate that the proposed scheme achieves low-cost performance compared to the existing offloading decision strategies in the literature.

关键词： computation offloading handover intelligent transportation reinforcement learning vehicular edge computing

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A3C-based load-balancing solution for computation offloading in SDN-enabled vehicular edge computing networks

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2023年第2期16卷 1242-1256页

作者： Lu, Lingyu Yu, Jing Du, Haifeng Li, Xiang Beijing Jiaotong Univ Sch Software Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Sankuai Online Technol Co Ltd Beijing 100102 Peoples R China

In the Internet of vehicles (IoV), various applications emerging to provide users with safe, reliable, and comfortable driving services have caused explosive demand for computing capability. It is very challenging for vehicles with limited resources to meet the real-time demand. Mobile Edge Computing (MEC) is a convenient solution that utilizes computing resources at the network edge to enhance the capability of vehicles. With the help of MEC, vehicles can offload parts of tasks to nearby MEC servers to reduce the response cost. However, with the increase of vehicles, the unbalanced load among servers may lead to the unbalanced resource allocation and the degradation of task completion ratio. To address this problem, we first propose an SDN-enabled vehicular edge computing network architecture to facilitate the management of IoV with a global view. Then, the joint optimization problem of computation offloading and load balancing is formulated as a sequential decision problem to minimize the system cost in terms of time and energy. An A3C-based solution is proposed to solve the offloading decisions. Further, we develop the simple computation resource allocation scheme and calculation method of offloading ratio, respectively. Finally, the simulation results demonstrate the superior performance of our proposed algorithm compared to the benchmark solutions.

关键词： MEC IoV SDN computation offloading Load balancing Deep Reinforcement Learning

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A Deep-Reinforcement-Learning-Based computation offloading With Mobile Vehicles in Vehicular Edge Computing

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IEEE INTERNET OF THINGS JOURNAL 2023年第17期10卷 15501-15514页

作者： Lin, Jie Huang, Siqi Zhang, Hanlin Yang, Xinyu Zhao, Peng Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China

Vehicular edge networks involve edge servers that are close to mobile devices to provide extra computation resource to complete the computation tasks of mobile devices with low latency and high reliability. Considerable efforts on computation offloading in vehicular edge networks have been developed to reduce the energy consumption and computation latency, in which roadside units (RSUs) are usually considered as the fixed edge servers (FESs). Nonetheless, the computation offloading with considering mobile vehicles as mobile edge servers (MESs) in vehicular edge networks still needs to be further investigated. To this end, in this article, we propose a Deep-Reinforcement-Learning-based computation offloading with mobile vehicles in vehicular edge computing, namely, Deep-Reinforcement-Learning-based computation offloading scheme (DRL-COMV), in which some vehicles (such as autonomous vehicle) are deployed and considered as the MESs that move in vehicular edge networks and cooperate with FESs to provide extra computation resource for mobile devices, in order to assist in completing the computation tasks of these mobile devices with great Quality of Experience (QoE) (i.e., low latency) for mobile devices. Particularly, the computation offloading model with considering both mobile and FESs is conducted to achieve the computation tasks offloading through vehicle-to-vehicle (V2V) communications, and a collaborative route planning is considered for these MESs to move in vehicular edge networks with objective of improving efficiency of computation offloading. Then, a Deep-Reinforcement-Learning approach with designing rational reward function is proposed to determine the effective computation offloading strategies for multiple mobile devices and multiple edge servers with objective of maximizing both QoE (i.e., low latency) for mobile devices. Through performance evaluations, our results show that our proposed DRL-COMV scheme can achieve a great convergence and stability. Add

关键词： Task analysis Servers Mobile handsets Iron Quality of experience computational modeling Reinforcement learning computation offloading Deep-Reinforcement-Learning mobile edge servers (MESs) vehicle-to-vehicle (V2V) communications vehicular edge networks

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Multi-Agent Deep Reinforcement Learning for Efficient computation offloading in Mobile Edge Computing

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Computers, Materials & Continua 2023年第9期76卷 3585-3603页

作者： Tianzhe Jiao Xiaoyue Feng Chaopeng Guo Dongqi Wang Jie Song Department of Software Engineering Software CollegeNortheastern UniversityShenyang110819China

Mobile-edge computing(MEC)is a promising technology for the fifth-generation(5G)and sixth-generation(6G)architectures,which provides resourceful computing capabilities for Internet of Things(IoT)devices,such as virtual reality,mobile devices,and smart *** general,these IoT applications always bring higher energy consumption than traditional applications,which are usually *** provide persistent energy,many references have studied the offloading problem to save energy ***,the dynamic environment dramatically increases the optimization difficulty of the offloading *** this paper,we aim to minimize the energy consumption of the entireMECsystemunder the latency constraint by fully considering the dynamic *** games,we propose amulti-agent deep reinforcement learning approach based on the bi-level actorcritic learning structure to jointly optimize the offloading decision and resource allocation,which can solve the combinatorial optimization problem using an asymmetric method and compute the Stackelberg equilibrium as a better convergence point than Nash equilibrium in terms of Pareto *** method can better adapt to a dynamic environment during the data transmission than the single-agent strategy and can effectively tackle the coordination problem in the multi-agent *** simulation results show that the proposed method could decrease the total computational overhead by 17.8%compared to the actor-critic-based method and reduce the total computational overhead by 31.3%,36.5%,and 44.7%compared with randomoffloading,all local execution,and all offloading execution,respectively.

关键词： computation offloading multi-agent deep reinforcement learning mobile-edge computing latency energy efficiency

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Cooperative Multi-Agent Deep Reinforcement Learning for computation offloading in Digital Twin Satellite Edge Networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2023年第10期41卷 3414-3429页

作者： Ji, Zhe Wu, Sheng Jiang, Chunxiao Beijing Univ Posts & Telecommun Sch Informat & Commun Beijing 100876 Peoples R China Tsinghua Univ Tsinghua Space Ctr Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing 100084 Peoples R China

With the development of commercial off-the-shelf hardware, low Earth orbit (LEO) satellites are promising to provide flexible edge computing services. In this paper, we investigate a digital twin (DT)-empowered satellite-terrestrial cooperative edge computing network, where computation tasks from terrestrial users can be partially offloaded to the associated base station (BS) edge server, the associated LEO satellite edge server, and an adjacent LEO satellite edge server. We formulate a multi-tier computation offloading optimization problem to minimize the weighted sum of total system delay and satellite energy consumption, where a LEO-layer problem and a DT-layer problem are involved. The LEO-layer problem optimizes the three-tier computation resource allocation and task splitting ratio. From the multi-satellite network perspective, the DT-layer problem optimizes how many resources will be shared between adjacent satellites. We then propose a multi-agent double actors twin delayed deterministic policy gradient (MA-DATD3) algorithm to optimize the LEO-layer problem, and adopt a centralized training and decentralized execution (CTDE) paradigm. The proposed MA-DATD3 algorithm is extended to solve the DT-layer problem in a centralized way, and the resource sharing between adjacent satellites is optimized to maximize the time-averaged reward. Simulation results show that our algorithm achieves a better performance than the MADDPG algorithm, and effectively improves the computation offloading performance while balancing the energy consumption and the total delay.

关键词： Satellite edge computing digital twin computation offloading multi-agent reinforcement learning twin delayed deterministic policy gradient

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Efficient Task Allocation for computation offloading in Vehicular Edge Computing

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IEEE INTERNET OF THINGS JOURNAL 2023年第6期10卷 5595-5606页

作者： Zhang, Zheng Zeng, Feng Cent South Univ Sch Comp Sci & Engn Changsha 410000 Peoples R China

In vehicular edge computing, the edge servers may be overloaded once too many vehicles request the task offloading service, which will cause task offloading failure or the high service delay. To provide high-quality VEC service, the processing capabilities of the vehicles, the edge servers, and the cloud should be utilized simultaneously in task offloading. In this article, we focus on task offloading allocation for the requesting vehicle and the pricing schemes for the edge server and the cloud. From a market perspective, we model the competition and cooperation among the requesting vehicle, the edge server, and the cloud as a Stackelberg game. Then, based on the backward induction method, we transform the game problem into a convex optimization problem and theoretically prove that the game has a unique Nash equilibrium, thereby the optimal task allocation for the requesting vehicle can be obtained. Meanwhile, a genetic algorithm-based searching algorithm is proposed to find the optimal pricing schemes for the edge server and the cloud, and the proposed algorithm has a rapid convergence due to the convex feature of the objective problem. Simulation results demonstrate that the proposed task allocation strategy has better performance than other solutions in terms of task offloading delay and cost, thus, can make the existing resources fully used to undertake more offloading tasks.

关键词： Task analysis Servers Cloud computing Resource management Games Edge computing Delays computation offloading game theory genetic algorithm (GA) task allocation vehicular edge computing (VEC)

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Multitask Multiobjective Deep Reinforcement Learning-Based computation offloading Method for Industrial Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2023年第2期10卷 1848-1859页

作者： Cai, Jun Fu, Hongtian Liu, Yan Guangdong Polytech Normal Univ Sch Cyber Secur Guangzhou 510665 Guangdong Peoples R China

Edge computing has emerged as a promising paradigm to deploy computing resources to the network edge. However, most existing computation offloading strategies consider only one objective, including latency, energy consumption, and weighted sum of latency and energy consumption. It is challenging to meet different requirements of the heterogeneous Industrial Internet of Things (IIoT) systems, simultaneously. To address this challenge, a multiagent deep reinforcement learning (MADRL)-based computation offloading method is proposed for cloud-edge-device computing, which aims to meet various requirements of different tasks. In the proposed model, two typical types of tasks are considered: 1) latency-sensitive tasks and 2) energy-sensitive tasks. Each type of task can be executed in one of the three layers, i.e., cloud, edge, or device layer. In addition, in the MADRL model, two agents are defined to make global offloading decisions for the two types of tasks according to the task characteristics and network resource status. The experimental results show that the proposed model can guarantee the quality of service in a heterogeneous IIoT system and achieve better system performance in terms of latency and energy consumption than weighted-sum optimization methods.

关键词： Cloud-edge-device system computation offloading Industrial Internet of Things (IIoT) multiagent reinforcement learning multiobjective optimization

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Satellite Edge Computing With Collaborative computation offloading: An Intelligent Deep Deterministic Policy Gradient Approach

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IEEE INTERNET OF THINGS JOURNAL 2023年第10期10卷 9092-9107页

作者： Zhang, Hangyu Liu, Rongke Kaushik, Aryan Gao, Xiangqiang Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Shenzhen Inst Beihang Univ Shenzhen 518038 Peoples R China Univ Sussex Sch Engn & Informat Brighton BN1 9RH England China Acad Space Technol Xian 710100 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

Enabling a satellite network with edge computing capabilities can complement the advantages further of a single terrestrial network and provide users with a full range of computing service. Satellite edge computing is a potentially indispensable technology for future satellite-terrestrial integrated networks. In this article, a three-tier edge computing architecture consisting of the terminal-satellite-cloud is proposed, where tasks can be processed at three planes and intersatellites can cooperate to achieve on-board load balancing. Facing varying and random task queues with different service requirements, we formulate the objective problem of minimizing the system energy consumption under the delay and resource constraints, and jointly optimize the offloading decision, communication, and computing resource allocation variables. Moreover, the distribution of resources is based on the reservation mechanism to ensure the stability of the satellite-terrestrial link and the reliability of computation process. To adapt to the dynamic environment, we propose an intelligent computation offloading scheme based on the deep deterministic policy gradient (DDPG) algorithm, which consists of several different deep neural networks (DNNs) to output both discrete and continuous variables. Additionally, by setting the selection process of legal actions, the simultaneous decisions on offloading locations and allocating resources under multitask concurrency is realized. The simulation results show that the proposed scheme can effectively reduce the total energy consumption of the system by ensuring that the task is completed on demand, and outperform the benchmark algorithms.

关键词： Satellites Task analysis Edge computing Resource management Optimization Internet of Things Heuristic algorithms computation offloading deep deterministic policy gradient (DDPG) intersatellite cooperative computing resource allocation satellite edge computing

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Joint Service Caching, Resource Allocation and computation offloading in Three-Tier Cooperative Mobile Edge Computing System

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第6期10卷 3343-3353页

作者： Wang, Luyao Zhang, Guanglin Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Minist Educ Engn Res Ctr Digitized Text & Apparel Technol Shanghai 201620 Peoples R China

Mobile Edge Computing (MEC) provides cloud-like computing functionalities for mobile devices (MDs) by deploying servers at the edge of future 6G network. The cooperation among multi-servers and remote cloud offers low-latency services for MDs, meeting the demand of numerous delay-sensitive computation tasks. However, the limited storage capacity of edge servers makes it difficult to cache all kinds of services from the cloud simultaneously. Furthermore, the available bandwidth of communication links and the computation capacity of edge servers are insufficient for all MDs. In this paper, we consider a multi-server MEC scenario, where exists the competitions of multiple MDs for communication and computation resource, and various services for storage capacity. We investigate task offloading and resource allocation strategy in the three-tier MEC system, and explore the service caching strategy of the edge server. We aim at minimizing the cost of delay and energy consumption, and formulate the joint optimization problem as Markov Decision Process (MDP). Based on Double Deep Q-Network (DDQN) algorithm, the joint service caching, resource allocation and computation offloading scheme (SCRACO) is proposed to solve the problem. The simulation results demonstrate that the proposed scheme can effectively reduce total cost of the system compared with other three benchmarks.

关键词： Servers Resource management Task analysis Costs Cloud computing Delays Minimization Deep learning Q-factor Edge computing computation offloading double deep Q-Network mobile edge computing resource allocation service caching

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