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Mobility-Aware computation offloading in Edge Computing Using Machine Learning

IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第1期22卷 328-340页

作者： Maleki, Erfan Farhangi Mashayekhy, Lena Nabavinejad, Seyed Morteza Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA Inst Res Fundamental Sci IPM Sch Comp Sci Tehran 19395 Iran

Cloudlets are resource-rich computing infrastructures of edge computing that are located at physical proximity of users to provide one-hop, high-bandwidth wireless access to additional computational resources. They enable computation offloading for user applications, which compensates for the resource limitation of user devices by providing ultra-low latency processing for their applications. Although the computation capability of user devices is dramatically augmented by offloading, spatio-temporal uncertainties due to user mobility and changes in application specifications bring the most challenging obstacles in deciding where to offload to provide minimum latency. In this paper, we focus on these challenges by designing efficient offloading approaches that take into account these uncertainties and dynamics in order to minimize the turnaround time of the applications, which is constituted by offloading latency, migration delay, and execution time. We first formulate this NP-hard problem as an integer programming model to obtain optimal offloading decisions. We tackle its intractability by designing two novel offloading approaches, called S-OAMC and G-OAMC, that fully assign applications to cloudlets by considering their expected future locations and specifications predicted by Matrix Completion, a machine learning method. S-OAMC is a sampling-based approximation dynamic programming approach that enhances scalability and obtains near-optimal solutions. G-OAMC is a fast greedy-based approach for finding low-turnaround time offloading decisions. We conduct extensive experiments to assess the performance of our proposed approaches. The results show that S-OAMC and G-OAMC lead to near-optimal turnaround time in a reasonable time, and they both obtain low migration rates.

关键词： Edge computing computation offloading mobility sampling dynamic programming

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A DRL-Based Decentralized computation offloading Method: An Example of an Intelligent Manufacturing Scenario

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第9期19卷 9631-9641页

作者： Lu, Shaofei Liu, Shen Zhu, Yajun Liang, Wei Li, Kuanching Lu, Yingping Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hunan Prov Key Lab Blockchain Infrastruct & Applic Changsha 410082 Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China Hunan Key Lab Serv Comp & Novel Software Technol Xiangtan 411201 Peoples R China Quantum Corp Mendota Hts MN 55120 USA

With the development of edge computing and 5G, the demand for resource-limited devices to execute computation-intensive tasks can be effectively alleviated. The research on computation offloading lays an essential foundation for realizing mobile edge computing, and deep reinforcement learning (DRL) has become an emerging technique to address the computation offloading problem. This article utilizes a DRL-based algorithm to design a decentralized computation offloading framework aimed at minimizing the computational cost. We employ a multiuser system model with a single-edge server suitable for industrial scenarios. Then, we propose a dual-critic deep deterministic policy gradient (DC-DDPG) algorithm based on the deep deterministic policy gradient (DDPG) algorithm to tackle computation offloading and resource allocation problems for all users. DC-DDPG adopts two critic nets in both the primary and target nets to fit the action value of two different optimization objectives, which expedites the convergence during the training process and reduces the computational cost of the edge computation system during operation. Compared with other DRL methods, such as deep Q-network and DDPG, numerical results demonstrate that the proposed DC-DDPG algorithm has a faster convergence speed and performs significantly better than other DRL-based algorithms in terms of system computational cost in computing-intensive tasks, which makes it more suitable for industrial intelligent manufacturing scenarios with large data volume.

关键词： computation offloading deep deterministic policy gradient (DDPG) deep reinforcement learning (DRL) dual critic edge computing

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Energy-Efficient Heuristic computation offloading With Delay Constraints in Mobile Edge Computing

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第6期16卷 4404-4417页

作者： Mei, Jing Tong, Zhao Li, Kenli Zhang, Lianming Li, Keqin Hunan Normal Univ Coll Informat Sci & Engn Changsha 410081 Hunan Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

By offloading computation-intensive tasks to the edge cloud, mobile edge computing (MEC) has been regarded as an effective technology for enhancing computational capacity and extending the battery lifetime of mobile devices (MDs). However, due to the limitation of bandwidth and computing resources in MEC, unreasonable task offloading might lead to intensive resource competition, which recedes the performance gains benefit from offloading. When the tasks are latency-sensitive, a proper task offloading strategy is more important. Considering the heterogeneous delay constraints and resource competition comprehensively, we aim at minimizing the energy consumption of MDs subject to the individual delay constraints of tasks by jointly optimizing the task offloading and resource allocation in terms of wireless channel and remote computation capacity in a multi-MD MEC system in this paper. Due to the complexity of the primal optimization problem, a heuristic algorithm is devised. In the algorithm, a subset of tasks to be offloaded is incrementally constructed, and the corresponding offloading sub-problem is then repeatedly solved for this task subset using a two-stage algorithm until the total energy consumption can no longer be further reduced. The first stage of solving the sub-problem is to find the optimal full offloading scheme for the to-offload tasks, which is proved to be a convex optimization problem. For the task subset without a full offloading solution, an effective iterative algorithm is employed in the second stage where the channel allocation and computing resource allocation are optimized alternately. A great number of experiments are given to verify the performance of the proposed algorithm. We observe that the heuristic algorithm shows different performance when adopting different task ordering schemes. The proposed heuristic algorithm is evaluated against three reference schemes, and the results show that it can save up to 14.20% of energy consumption whi

关键词： computation offloading delay constraint edge computing energy optimization resource competition

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Asynchronous FDRL-Based Low-Latency computation offloading for Integrated Terrestrial and Non-Terrestrial Power IoT

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IEEE NETWORK 2023年第5期37卷 33-41页

作者： Li, Sifeng Zhang, Sunxuan Wang, Zhao Zhou, Zhenyu Wang, Xiaoyan Mumtaz, Shahid Guizani, Mohsen Frascolla, Valerio North China Elect Power Univ Sch Elect & Elect Engn Hebei Key Lab Power Internet Things Technol Beijing 102206 Peoples R China Ibaraki Univ Grad Sch Sci & Engn Ibaraki 3108512 Japan Silesian Tech Univ Akademicka Dept Appl Informat PL-44100 Gliwice Poland Nottingham Trent Univ Dept Engn Nottingham NG1 4FQ England Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Abu Dhabi U Arab Emirates Intel Deutschland GmbH D-85579 Neubiberg Germany

Integrated terrestrial and non-terrestrial power internet of things (IPIoT) has emerged as a paradigm shift to three-dimensional vertical communication networks for power systems in the 6G era. computation offloading plays key roles in enabling real-time data processing and analysis for electric services. However, computation offloading in IPIoT still faces challenges of coupling between task offloading and computation resource allocation, resource heterogeneity and dynamics, and degraded model training caused by electromagnetic interference (EMI). In this article, we propose an asynchronous federated deep reinforcement learning (AFDRL)-based computation offloading framework for IPIoT, where models are uploaded asynchronously for federated averaging to relieve network congestion and improve global model training. Then, we propose an Asynchronous fedeRated deep reinforcemenT learnIng-baSed low-laTency computation offloading algorithm (ARTIST) to realize low-latency computation offloading through joint optimization of task offloading and computation resource allocation. Particularly, ARTIST adopts EMI-aware federated set determination to remove aberrant local models from federated averaging and improve training accuracy. Next, a case study is developed to validate the excellent performance of ARTIST in reducing task offloading and total queuing delays.

关键词： Task analysis Training computational modeling Optimization Resource management Real-time systems Delays Low latency communication Power transmission Internet of Things 6G mobile communication Power systems Communication networks Reinforcement learning Telecommunication congestion control Queueing analysis 6G integrated terrestrial and non-terrestrial power internet of things computation offloading asynchronous federated deep reinforcement learning electromagnetic interference awareness

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ROGI: Partial computation offloading and Resource Allocation in the Fog-Based IoT Network Towards Optimizing Latency and Power Consumption

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第3期26卷 1767-1784页

作者： Tabarsi, Benyamin T. Rezaee, Ali Movaghar, Ali Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Sharif Univ Technol Dept Comp Engn Tehran Iran

Fog computing has risen into a popular topic in recent years with the idea of deploying computation and communication closer to end-users. The capability of the fog model to face serious challenges like high latency and power consumption is of paramount research significance, notably in the context of the Internet of Things (IoT), given the volume and requirements of IoT applications. Accordingly, issues such as using the capacity of the fog layer maximally, minimizing latency while maintaining reliability, and efficiently distributing the workload across the network can be well explored. This motivated us to propose a scheme for partial computation offloading and resource allocation in the fog-based IoT network with the goal of optimizing latency and power consumption (ROGI). In this research, the nodes in all network layers are involved in processing the workload. Also, the power consumption in the end-users layer is reduced. Additionally, the fog nodes collaborate to improve system reliability and provide more resources for handling users' requests. Furthermore, the concept of partial offloading is adopted, which would potentially lead to higher flexibility in resource management and provide the opportunity to leverage more resources in each layer of IoT architecture. Moreover, the whole model is decomposed into three problems, each of which is solved via optimization techniques. Extensive simulations are carried out to show the performance of the proposed scheme.

关键词： Internet of Things (IoT) Fog computing computation offloading Resource allocation Latency Power consumption Reliability

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Intelligent computation offloading for educational virtual reality applications in smart campus using MoCell

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computationAL INTELLIGENCE 2023年第1期39卷 82-103页

作者： Liu, Peichen Peng, Kai Tao, Peng Huaqiao Univ Coll Engn Quanzhou Peoples R China Sincetech Fujian Technol Co Ltd Quanzhou Peoples R China

Nowadays, as the smart campus concept becomes a reality, virtual reality (VR) applications are being applied to connect students with the virtual teaching world via VR devices (VDs), enhancing learning efficiency. Nevertheless, VR applications are latency-sensitive while VDs are subjected to shortcomings to handle many VR applications simultaneously. Fortunately, mobile edge computing (MEC) has been recognized as a promising solution that can bring abundant resources to VDs to relieve hardware limits. However, the computing resources of edge nodes in MEC are limited, and thus how to allocate resources effectively is critical. Meanwhile, some sensitive information of students collected by VD needs to be protected. In view of this, we investigate computation offloading for educational VR applications in MEC-enabled smart campus. The aims of this issue are to optimize the motion-to-photon latency, energy consumption, and resource utilization while satisfying the privacy and security constraints. To this end, we propose a new multi-objective optimization method using MoCell. Eventually, the experimental evaluations are designed to illustrate the effectiveness and superiority our proposed method.

关键词： computation offloading mobile edge computing privacy security smart campus virtual reality

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Joint computation offloading and Resource Allocation for MIMO-NOMA Assisted Multi-User MEC Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第7期71卷 4360-4376页

作者： Wang, Meng Shi, Shuo Zhang, Deyou Wu, Chenyu Wang, Ye Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China KTH Royal Inst Technol Sch Elect Engn & Comp Sci S-10044 Stockholm Sweden

This paper investigates the resource allocation and computation offloading problem for multi-access edge computing (MEC) systems, where multiple mobile users (MUs) equipped with multiple antennas access the base station in a non-orthogonal multiple access manner. We jointly optimize the offloading ratio, computational frequency and transmit precoding matrix of each MU to minimize the total energy consumption of all MUs while satisfying the latency constraints. The problem is formulated as a non-convex optimization problem and a two-layer iterative method is proposed to solve the problem efficiently with low complexity. Specifically, we first decompose the original problem into several subproblems, and then sequentially solve these subproblems in an alternative fashion. Furthermore, we also discuss the optimal decoding order of MUs under two different scenarios. Firstly, when the MUs' channel conditions are similar, by deriving closed-form expressions for energy consumptions of all MUs, we prove that the optimal decoding order is only determined by the latency requirements. On the other hand, when the MUs' channel conditions are different, we show that the optimal decoding order is determined by both the channel conditions and the latency requirements. As such, we propose a metric aiming to balance the effects of channel conditions and latency requirements on the MUs' decoding order. Simulation results validate the convergence of the proposed method and demonstrate its superiority over benchmark algorithms.

关键词： Multi-access edge computing computation offloading resource allocation MIMO-NOMA decoding order

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Dynamic NOMA-Based computation offloading in Vehicular Platoons

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第10期72卷 13000-13010页

作者： Zheng, Dongsheng Chen, Yingyang Wei, Lai Jiao, Bingli Hanzo, Lajos Peking Univ Sch Elect Beijing 100871 Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China Jinan Univ Guangdong Prov Key Lab Data Secur & Privacy Prote Guangzhou 510632 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton SO171BJ Hants England

Both the Mobile edge computing (MEC)-based and fog computing (FC)-aided Internet of Vehicles (IoV) constitute promising paradigms of meeting the demands of low-latency pervasive computing. To this end, we construct a dynamic NOMA-based computation offloading scheme for vehicular platoons on highways, where the vehicles can offload their computing tasks to other platoon members. To cope with the rapidly fluctuating channel quality, we divide the timeline into successive time slots according to the channel's coherence time. Robust computing and offloading decisions are made for each time slot after taking the channel estimation errors into account. Considering a certain time slot, we first analytically characterize both the locally computed source data and the offloaded source data as well as the energy consumption of every vehicle in the platoons. We then formulate the problem of minimizing the long-term maximum task queue by optimizing the allocation of both the communication and computing resources. To solve the problem formulated, we design an online algorithm based on the classic Lyapunov optimization method and successive convex approximation (SCA) method. Finally, the numerical simulation results characterize the performance of our algorithm and demonstrate its advantages both over the local computing scheme and the orthogonal multiple access (OMA)-based offloading scheme.

关键词： computation offloading non-orthogonal multiple access (NOMA) vehicular platoons Lyapunov optimization successive convex approximation (SCA)

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Deep Reinforcement Learning Based computation offloading and Trajectory Planning for Multi-UAV Cooperative Target Search

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2023年第2期41卷 504-520页

作者： Luo, Quyuan Luan, Tom H. Shi, Weisong Fan, Pingzhi Southwest Jiaotong Univ Sch Informat Sci & Technol Prov Key Lab Informat Coding & Transmiss Chengdu 611756 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA

Unmanned aerial vehicles (UAVs) are widely used for surveillance and monitoring to complete target search tasks. However, the short battery life and moderate computational capability hinder UAVs to process computation-intensive tasks. The emerging edge computing technologies can alleviate this problem by offloading tasks to the ground edge servers. How to evaluate the search process so as to make optimal offloading decisions and make optimal flying trajectories represent fundamental research challenges. In this paper, we propose to utilize the concept of uncertainty to evaluate the search process, which reflects the reliability of the target search results. Thereafter, we propose a deep reinforcement learning (DRL) technique to jointly make optimal computation offloading decisions and flying orientation choices for multi-UAV cooperative target search. Specifically, we first formulate an uncertainty minimization problem based on the established system model. By introducing a reward function, we prove that the uncertainty minimization problem is equivalent to a reward maximization problem, which is further analyzed by a Markov decision process (MDP). To obtain the optimal task offloading decisions and flying orientation choices, a deep Q-network (DQN) based DRL architecture with a separated Q-network is then proposed. Finally, extensive simulations validate the effectiveness of the proposed techniques, and comprehensive discussions on how different parameters affect the search performance are given.

关键词： Task analysis Search problems Uncertainty Edge computing Autonomous aerial vehicles Trajectory Servers Unmanned aerial vehicle cooperative target search edge computing computation offloading deep reinforcement learning (DRL)

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Energy-Aware and Mobility-Driven computation offloading in MEC

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JOURNAL OF GRID COMPUTING 2023年第2期21卷 1-18页

作者： Chen, Liqiong Liu, Yingda Lu, Yijun Sun, Huaiying Shanghai Inst Technol Dept Comp Sci & Informat Engn Shanghai Peoples R China

Heuristic algorithms are widely used in Mobile Edge Computing(MEC) to improve the performance of mobile devices. However, the time complexity of the heuristic algorithm is high, and it is complex to optimize under constraints. Therefore, we proposed Multi-User Energy Constraint Time Optimization Algorithm(MU-ECTOA) for workflow makespan optimization under energy constraints. MU-ECTOA includes three stages: cluster analysis, evaluation of performance, and workflow offloading. In the first stage, the workflow tasks are classified according to their characteristics;In the second stage, the subtask groups are obtained, and the evaluation results of each subtask group are obtained. In the third stage, the optimal subtask group is selected for offloading and then updated the ready time of edge nodes. Extensive experiments have been conducted, the ACO&GA, Max-Min, Particle Swarm Optimization(PSO), GA-DPSO, and SFLA are taken as the compared methods. The results of MU-ECTOA performs better in aspects in completion time, load balancing, and successful offloading rate compared with other methods. By comparing the results of algorithms, the makespans of the algorithms are close, but the algorithm complexity and load balancing of MU-ECTOA are much better;The time complexity of the MU-ECTOA algorithm is close to the Max-Min's, but MU-ECTOA performs better in makespan and algorithm reliability.

关键词： Edge computing computation offloading Workflow K-Means Energy constraint

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