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Multi-user reinforcement learning based task migration in mobile edge computing

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Frontiers of Computer Science 2024年第4期18卷 161-173页

作者： Yuya CUI Degan ZHANG Jie ZHANG Ting ZHANG Lixiang CAO Lu CHEN Tianjin Key Lab of Intelligent computing and Novel Software Technology Tianjin University of TechnologyTianjin 300384China School of Internet of Things Engineering Jiangsu Vocational College of Information TechnologyWuxi 214153China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Sports Economics and Management Tianjin University of SportTianjin 301617China

mobile edge computing(MEC)is a promising *** service migration is a key technology in *** order to maintain the continuity of services in a dynamic environment,mobile users need to migrate tasks between multiple servers in real *** to the uncertainty of movement,frequent migration will increase delays and costs and non-migration will lead to service ***,it is very challenging to design an optimal migration *** this paper,we investigate the multi-user task migration problem in a dynamic environment and minimizes the average service delay while meeting the migration *** order to optimize the service delay and migration cost,we propose an adaptive weight deep deterministic policy gradient(AWDDPG)*** distributed execution and centralized training are adopted to solve the high-dimensional *** show that the proposed algorithm can greatly reduce the migration cost and service delay compared with the other related algorithms.

关键词： mobile edge computing mobility service migration deep reinforcement learning deep deterministic policy gradient

Energy-efficient task offloading strategy in mobile edge computing for resource-intensive mobile applications

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Digital Communications and Networks 2022年第6期8卷 1048-1058页

作者： Michael Pendo John Mahenge Chunlin Li Camilius A.Sanga Department of Informatics and Information Technology Sokoine University of AgricultureMorogoro3038Tanzania School of Computer Science and Technology Wuhan University of TechnologyWuhanHubei430063China

mobile edge computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as mobile Cloud computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.

关键词： mobile edge computing Quality of experience Task ofloading Communication networks Particle swarm optimizati on

An adaptive offloading framework for Android applications in mobile edge computing

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Science China(Information Sciences) 2019年第8期62卷 114-130页

作者： Xing CHEN Shihong CHEN Yun MA Bichun LIU Ying ZHANG Gang HUANG College of Mathematics and Computer Science Fuzhou University Fujian Key Laboratory of Network computing and Intelligent Information Processing Fuzhou University School of Software Tsinghua University Key Laboratory of High Confidence Software Technologies Ministry of Education

mobile edge computing(MEC) provides a fresh opportunity to significantly reduce the latency and battery energy consumption of mobile applications. It does so by enabling the offloading of parts of the applications on mobile edges, which are located in close proximity to the mobile devices. Owing to the geographical distribution of mobile edges and the mobility of mobile devices, the runtime environment of MEC is highly complex and dynamic. As a result, it is challenging for application developers to support computation offloading in MEC compared with the traditional approach in mobile cloud computing, where applications use only the cloud for offloading. On the one hand, developers have to make the offloading adaptive to the changing environment, where the offloading should dynamically occur among available computation *** the other hand, developers have to effectively determine the offloading scheme each time the environment changes. To address these challenges, this paper proposes an adaptive framework that supports mobile applications with offloading capabilities in MEC. First, based on our previous study(DPartner), a new design pattern is proposed to enable an application to be dynamically offloaded among mobile devices, mobile edges,and the cloud. Second, an estimation model is designed to automatically determine the offloading *** this model, different parts of the application may be executed on different computation nodes. Finally, an adaptive offloading framework is implemented to support the design pattern and the estimation model. We evaluate our framework on two real-world applications. The results demonstrate that our approach can aid in reducing the response time by 8%–50% and energy consumption by 9%–51% for computation-intensive applications.

关键词： computation offloading software adaptation mobile edge computing application refactoring Android

Data and Model Driven Task Offloading Strategy in the Dynamic mobile edge computing System

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Journal of Systems Science & Complexity 2024年第1期37卷 351-368页

作者： DONG Hairong WU Wei SONG Haifeng LIU Zhen ZHANG Zixuan School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Electronic and Information Engineering BeiHang UniversityBeijing 100191China CRSC Research&Design Institute Group Co. Ltd.Beijing 100070China

mobile edge computing(MEC)provides communication and computational capabilities for the industrial Internet,meeting the demands of latency-sensitive ***,traditional model-driven task offloading strategies face challenges in adapting to situations with unknown network communication status and computational *** limitation becomes notably significant in complex industrial networks of high-speed *** by these considerations,a data and model-driven task offloading problem is proposed in this paper.A redundant communication network is designed to adapt to anomalous channel states when tasks are offloaded to edge *** link switching mechanism is executed by the train according to the attributes of the completed *** task offloading optimization problem is formulated by introducing data-driven prediction of communication states into the traditional ***,the optimal strategy is achieved by employing the informer-based prediction algorithm and the quantum particle swarm optimization method,which effectively tackle real-time optimization problems due to their low time *** simulations illustrate that the data and model-driven task offloading strategy can predict the communication state in advance,thus reducing the cost of the system and improving its robustness.

关键词： Data driven model informer mobile edge computing quantum particle swarm optimization task offloading.

Multiuser Computation Offloading for Long-Term Sequential Tasks in mobile edge computing Environments

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Tsinghua Science and Technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

Users'experience matter:Delay sensitivity-aware computation offloading in mobile edge computing

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Digital Communications and Networks 2022年第6期8卷 955-963页

作者： Mingzhi Wang Tao Wu Tao Ma Xiaochen Fan Mingxing Ke National University of Defense Technology Hefei230031China Consulting Center for Strategic Assessment Academy of Military ScienceBeijing100091China

As a promising computing paradigm,mobile edge computing(MEC)provides communication and computing capability at the edge of the network to address the concerns of massive computation requirements,constrained battery capacity and limited bandwidth of the Internet of Things(IoT)*** existing works on mobile edge task ignores the delay sensitivities,which may lead to the degraded utility of computation offloading and dissatisfied *** this paper,we study the delay sensitivity-aware computation offloading by jointly considering both user's tolerance towards delay of task execution and the network status under computation and communication ***,we use a specific multi-user and multi-server MEC system to define the latency sensitivity of task offloading based on the analysis of delay distribution of task ***,we propose a scoring mechanism to evaluate the sensitivity-dependent utility of task execution and devise a Centralized Iterative Redirection Offloading(CIRO)algorithm to collect all information in the MEC *** starting with an initial offloading strategy,the CIRO algorithm enables IoT devices to cooperate and iteratively redirect task offloading decisions to optimize the offloading strategy until it *** simulation results show that our method can significantly improve the utility of computation offloading in MEC systems and has lower time complexity than existing algorithms.

关键词： mobile edge computing Computation floading Delay sensitivity Centralized ofloading

Dynamic Task Offloading for mobile edge computing with Hybrid Energy Supply

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Tsinghua Science and Technology 2023年第3期28卷 421-432页

作者： Ying Chen Fengjun Zhao Yangguang Lu Xin Chen Computer School Beijing Information Science and Technology UniversityBeijing 100101China

mobile edge computing(MEC),as a new distributed computing model,satisfies the low energy consumption and low latency requirements of computation-intensive *** task offloading of MEC has become an important research hotspot,as it solves the problems of insufficient computing capability and battery capacity of Internet of things(IoT)*** study investigates task offloading scheduling in a dynamic MEC *** integrating energy harvesting technology into IoT devices,we propose a hybrid energy supply *** jointly optimize local computing,offloading duration,and edge computing decisions to minimize system *** the basis of stochastic optimization theory,we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called *** can make task offloading decisions by weighing system cost and queue *** quote dynamic programming theory to obtain the optimal task offloading *** results verify the effectiveness of DTOME,and show that DTOME entails lower system cost than two baseline task offloading strategies.

关键词： mobile edge computing Internet of Things dynamic offloading hybrid energy supply stochastic optimization

Efficient Multi-User for Task Offloading and Server Allocation in mobile edge computing Systems

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China Communications 2022年第7期19卷 226-238页

作者： Qiuming Liu Jing Li Jianming Wei Ruoxuan Zhou Zheng Chai Shumin Liu School of Software Engineering Jiangxi University of Science and TechnologyNanchang 330013China Nanchang Key laboratory of Virtual Digital Factory and Cultural Communications Nanchang 330013China

mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud *** conserve energy as well as maintain quality of service,low time complexity algorithm is proposed to complete task offloading and server *** this paper,a multi-user with multiple tasks and single server scenario is considered for small network,taking full account of factors including data size,bandwidth,channel state ***,we consider a multi-server scenario for bigger network,where the influence of task priority is taken into *** jointly minimize delay and energy cost,we propose a distributed unsupervised learning-based offloading framework for task offloading and server *** exploit a memory pool to store input data and corresponding decisions as key-value pairs for model to learn to solve optimization *** further reduce time cost and achieve near-optimal performance,we use convolutional neural networks to process mass data based on fully connected *** results show that the proposed algorithm performs better than other offloading schemes,which can generate near-optimal offloading decision timely.

关键词： distributed unsupervised learning energy efficiency mobile edge computing task offloading

Green resource allocation for mobile edge computing

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Digital Communications and Networks 2023年第5期9卷 1190-1199页

作者： Anqi Meng Guandong Wei Yao Zhao Xiaozheng Gao Zhanxin Yang State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing100024China Engineering Center of Digital Audio and Video Communication University of ChinaBeijing100024China School of Information and Electronics Beijing Institute of TechnologyBeijing100081China

We investigate the green resource allocation to minimize the energy consumption of the users in mobile edge computing systems,where task offloading decisions,transmit power,and computation resource allocation are jointly *** considered energy consumption minimization problem is a non-convex mixed-integer nonlinear programming problem,which is challenging to ***,we develop a joint search and Successive Convex Approximation(SCA)scheme to optimize the non-integer variables and integer variables in the inner loop and outer loop,***,in the inner loop,we solve the optimization problem with fixed task offloading *** to the non-convex objective function and constraints,this optimization problem is still non-convex,and thus we employ the SCA method to obtain a solution satisfying the Karush-Kuhn-Tucker *** the outer loop,we optimize the offloading decisions through exhaustive ***,the computational complexity of the exhaustive search method is greatly *** reduce the complexity,a heuristic scheme is proposed to obtain a sub-optimal *** results demonstrate the effectiveness of the developed schemes.

关键词： mobile edge computing Green communications Mixed-integer programming Resource allocation