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2023 International Conference on Ubiquitous Communication, Ucom 2023

作者： Zhang, You Mao, Zhengchong School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (纸本)9798350340433

This paper investigates a system where an unmanned aerial vehicle (UAV) provides computing services to multiple mobile terminals. To optimize the computation offloading services of the system, we jointly optimize mobile terminal scheduling, UAV trajectory, and task offloading ratio to minimize service delay and terminals' energy consumption under energy constraints. Considering the optimization problem with non-convexity, the high-dimensional states, and continuous action space, we propose a SAC-based computation offloading algorithm (SAC-CO) for UAV-Assisted mobile edge computing (MEC). The algorithm improves its convergence by combining off-policy and maximum entropy reinforcement learning. Simulation results demonstrate that SAC-UTO outperforms the benchmark algorithm in most cases. © 2023 IEEE.

关键词： computation offloading

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Reinforcement learning for intelligent online computation offloading in wireless powered edge networks

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第2期26卷 1053-1062页

作者： Mustafa, Ehzaz Shuja, Junaid Bilal, Kashif Mustafa, Saad Maqsood, Tahir Rehman, Faisal Khan, Atta ur Rehman COMSATS Univ Islamabad Dept Comp Sci Abbottabad Campus Abbottabad Pakistan Ajman Univ Coll Engn & Informat Technol Ajman U Arab Emirates

The method of charging mobile devices with wireless power transfer (WPT) from the base station (BS) integrated with mobile edge computing (MEC) increases the potential of MEC. The increasing demand for intelligent computation offloading requires effective decisions among local or remote computation specifically in wireless fading channels of the dynamic environment. Our main aim is to generate an effective offloading decision between local and remote computation in a real-time environment for each wireless channel while preserving optimal computation rate. In this article, we consider a wireless powered MEC system that governs a binary offloading decision to execute the task locally at the edge devices or the remote server. We propose a reinforcement learning based intelligent online offloading (RLIO) framework that adopts an optimal offloading action based on reinforcement methods. This framework acquires a worthy decision among local or remote computation for the time varying wireless channel conditions in dense networks. Numerical results show that the proposed framework can achieve optimal performance while preserving the computation time compared with existing optimization methods. Second, the average execution cost of RLIO is less than 0.4 ms per channel, which enables real-time and optimal offloading in dynamic and large-scale networks.

关键词： Mobile edge computing Wireless power transfer computation offloading Reinforcement learning

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DRL-Based computation offloading With Queue Stability for Vehicular-Cloud-Assisted Mobile Edge Computing Systems

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第4期8卷 2797-2809页

作者： Ma, Guifu Wang, Xiaowei Hu, Manjiang Ouyang, Wenjie Chen, Xiaolong Li, Yang Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China Hunan Univ Wuxi Intelligent Control Res Inst WICRI Wuxi 214072 Jiangsu Peoples R China

computation offloading is considered as a promising method to improve computation performance of Intelligent Vehicles (IVs), where IVs can offload resource-hungry applications to Mobile Edge Computing (MEC) servers. However, due to the dynamic environment of Intelligent Transportation Systems, it's challenging to develop an effective offloading scheme that ensures stable operation of the system. Furthermore, IVs only offload tasks to MEC servers or parked vehicles, how to take full advantage of resources in a random traffic flow is a main challenge. In this paper, we propose a vehicular-cloud-assisted MEC network in urban cities to fully explore the idle resources and ensure the stability of task queue. Specifically, we develop a novel virtual platform based on deep neural network that predicts vehicles trajectory and creates vehicular cloud to integrate computation resources. We then design a vehicular-cloud-assisted offloading scheme that aims to maximize task throughput in continuous time slots subject to multi-constraint. To address the coupling among multiple variables and different time slots, we put forward a lightweight framework to apply the Lyapunov optimization in combination with deep reinforcement learning. In particular, we apply Lyapunov drift-plus-penalty approach to decompose the initial problem into per-slot subproblems and control the long-term task queue stability. To further reduce the computation complexity, we decouple the subproblem into three processes, and the deep Q-network is put forward to solve it. Extensive simulations demonstrate that under different task arrivals, our proposed scheme can achieve the task queue stability within 10 time slots with a low transmission power.

关键词： Task analysis Servers Stability analysis Optimization TV Cloud computing Trajectory computation offloading deep reinforcement learning intelligent vehicles mobile edge computing

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Application-aware computation offloading in edge computing networks

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年第1期146卷 86-97页

作者： Lin, Rongping Guo, Xuhui Luo, Shan Xiao, Yong Moran, Bill Zukerman, Moshe Univ Elect Sci & Technol China UESTC Sch Informat & Commun Engn Chengdu Peoples R China UESTC Sch Aeronaut & Astronaut Chengdu Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Edge computing involves distributive computation resources deployed at the network edge, unlike cloud computing, which has central computation resources in data centers. Edge computing is a complement of cloud computing because edge computing effectively reduces the computing response delay by processing computation tasks and data near terminals. Considering the dramatic increase of terminals connected to networks and data generated by terminals, computation tasks from different applications may require significantly different services with different computation requirements, storage requirements, and response delay requirements. Application-aware computation offloading and resource allocation in edge computation can provide efficient and guaranteed computation services to terminals. In this paper, an application-aware computation offloading and resource allocation problem is investigated in edge computing networks, where computation tasks from different applications have different requirements. A non-convex optimization problem of energy consumption minimization is formulated, where terminals, edge nodes, and a cloud are considered. We convert the original non-convex optimization problem into a lower-bound convex problem and an upper-bound convex problem. Then, an algorithm based on the branch-and-bound method is proposed to force the lower-and upper-bound solutions to approach the optimal solution. Finally, the performance of the algorithm is analyzed where the gap to the optimal solution is provided. Numerical results show that the proposed algorithm can provide guaranteed services for tasks of different application types, with improvements over application-unaware algorithms.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Edge computing computation offloading Application-aware Non-convex Branch-and-bound Stochastic gain

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Intelligent computation offloading and Resource Allocation in IIoT With End-Edge-Cloud Computing Using NSGA-III

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第5期10卷 3032-3046页

作者： Peng, Kai Huang, Hualong Zhao, Bohai Jolfaei, Alireza Xu, Xiaolong Bilal, Muhammad Huaqiao Univ Sch Engn Quanzhou 362021 Peoples R China Flinders Univ S Australia Coll Sci & Engn Adelaide SA 5042 Australia Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Hankuk Univ Foreign Studies Dept Comp & Elect Syst Engn Yongin 17035 Gyeonggi Do South Korea

Industrial Internet of Things (IIoT), which consists of massive IoT devices and industrial infrastructures such as wireless access points to acquire intelligent services, has been devoted as a critical physical information platform to realizing the fourth industrial revolution, otherwise known as Industry 4.0. Currently, a new paradigm called mobile edge computing (MEC) has brought an opportunity to accelerate the development of IIoT. It has powerful computing capabilities and can be well-used to provide low-latency services to execute some computation applications generated by IIoT devices. However, the existing methods may not be directly used for IIoT scenarios due to the large size of IIoT devices and the characteristics of the applications, as well as the limited and heterogeneous resources of edge servers. In view of this, the computation offloading and resource allocation are formulated as a multi-objective optimization problem, and an end-edge-cloud collaborative intelligent optimization method is devised in this paper. Comprehensive experiments and evaluations are carried out to prove the effectiveness and efficiency of our proposed method with regard to the energy consumption and time consumption of IIoT devices, as well as resource utilization and load balancing of edge servers.

关键词： IIoT MEC computation offloading resource allocation multi-objective optimization

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Cost-Minimized computation offloading of Online Multifunction Services in Collaborative Edge-Cloud Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第1期20卷 292-304页

作者： Feng, Chuan Han, Pengchao Zhang, Xu Zhang, Qihan Zong, Yue Liu, Yejun Guo, Lei Northeastern Univ Sch Comp Sci & Engn Shenyang 110169 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

Cloud Computing (CC) is powerful for the computation offloading of services, promoting the implementation of various modern applications. Mobile Edge Computing (MEC) can provide low-latency services utilizing edge servers locating in proximity to users. The combination of MEC and CC can give play to the dual advantages of both. However, it is a challenging problem to offload service requests to the collaborative edge-cloud networks aiming at minimizing costs due to the resource limitation of edge servers and the online feature of services. To address this issue, we mathematically model the service requests with multiple inter-connected functions. Then, the problem of computation offloading of multi-function service requests in collaborative edge-cloud networks is formulated to be an Integer Linear Programming (ILP) and is proved to be NP-hard. Furthermore, a Cost-minimized computation offloading with Reconfiguration (CCOR) algorithm is proposed to minimize the total cost of online services. Finally, simulation results show that the proposed CCOR algorithm can effectively reduce the cost of computation offloading with higher resource utilization of edge cloud compared with baseline algorithms.

关键词： Servers Cloud computing Costs Games Collaboration Bandwidth Wireless communication computation offloading cost optimization mobile edge computing cloud computing

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A Markov Decision Process Solution for Energy-Saving Network Selection and computation offloading in Vehicular Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第9期72卷 12031-12046页

作者： Shinde, Swapnil Sadashiv Tarchi, Daniele Univ Bologna Dept Elect Elect & Informat Engn Guglielmo Marcon I-40126 Bologna Italy

Vehicular Edge Computing (VEC) enables the integration of edge computing facilities in vehicular networks (VNs), allowing data-intensive and latency-critical applications and services to end-users. Though VEC brings several benefits in terms of reduced task computation time, energy consumption, backhaul link congestion, and data security risks, VEC servers are often resource-constrained. Therefore, the selection of proper edge nodes and the amount of data to be offloaded becomes important for having VEC process benefits. However, with the involvement of high mobility vehicles and dynamically changing vehicular environments, proper VEC node selection and data offloading can be challenging. In this work, we consider a joint network selection and computation offloading problem over a VEC environment for minimizing the overall latency and energy consumption during vehicular task processing, considering both user and infrastructure side energy-saving mechanisms. We have modeled the problem as a sequential decision-making problem and incorporated it in a Markov Decision Process (MDP). Numerous vehicular scenarios are considered based upon the users' positions, the states of the surrounding environment, and the available resources for creating a better environment model for the MDP analysis. We use a value iteration algorithm for finding an optimal policy of the MDPs over an uncertain vehicular environment. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy.

关键词： Vehicular edge computing network selection computation offloading energy saving mechanisms markov decision process

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computation offloading Design for Deep Neural Network Inference on IoT Devices

Computation Offloading Design for Deep Neural Network Infere...

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作者： Boosarapu, Asmika Rowan University

学位级别：M.S., Master of Science/Master of Surgery

In recent times, advances in the technologies of Internet-of-Things (IoT) and Deep Neural Networks (DNN) have significantly increased the accuracy and speed of a variety of smart applications. However, one of the barriers to deploying DNN to IoT is the computational limitations of IoT devices as compared with the computationally expensive task of DNN inference. computation offloading is an approach that addresses this problem by offloading DNN computation tasks to cloud servers. In this thesis we propose a collaborative computation offloading solution, in which some of the work is done on the IoT device, and the remainder of the work is done by the cloud server. There are two components to this collaborative approach. First, the input image to the DNN is partitioned into multiple pieces, allowing the pieces of the image to be processed in parallel, speeding up the inference time. Second, the DNN is split between two of its layers, so that layers before the split point are processed on the IoT device, and layers after the split point are processed by the cloud server. We investigated several strategies for partitioning the image and splitting the DNN, and we evaluated the results using several commonly-used DNNs: Lenet-5, AlexNet, and VGG-16. The results show that collaborative computation offloading sped up the inference time of IoT devices by 35-40% as compared with non-collaborative methods.

关键词： Collaborative computing computation offloading Deep neural network Input partition IoT devices Split layer

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Enhancing QoS for IoT Devices through Heuristics-based computation offloading in Multi-access Edge Computing

INFOCOMMUNICATIONS JOURNAL

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INFOCOMMUNICATIONS JOURNAL 2024年第4期16卷 10-17页

作者： Myyara, Marouane Lagnfdi, Oussama Darif, Anouar Farchane, Abderrazak Sultan Moulay Slimane Univ Polydisciplinary Fac Lab Innovat Mathematids Applicat & Informat Techno Beni Mellal 23000 Morocco

Multi-access Edge Computing (MEC) networks, particularly with the advent of 5G, aim to reduce latency and increase speed to meet the demands of resource-intensive ap-plications in the Internet of Things (IoT), such as private wire-less networks, online gaming, industry, and remote healthcare. These applications require guaranteed performance. However, while Quality of Service (QoS) management is well established in the Cloud, improving it remains a challenge in MEC en-vi-ronments. This study addresses this challenge by proposing heu-ristic computation offloading algorithms for IoT-intensive devic-es in MEC networks. These algorithms aim to minimize service time while maximizing the QoS, taking into account tasks and resource characteristics to determine the optimal execution lo-cation for IoT device applications. We evaluated our approach using the EdgeCloudSim simulator, and the results demonstrate its superiority over existing solutions. Our approach significant-ly improves QoS by reducing the service time of IoT application tasks. This research fills a gap in efficient QoS improvement and contributes to advances in computation offloading strategies in MEC environments. It paves the way for enhanced performance of IoT applications in these networks.

关键词： MEC IoT computation offloading Quality of Service Heuristic Algorithms EdgeCloudSim

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Energy-Efficient computation offloading for Static and Dynamic Applications in Hybrid Mobile Edge Cloud System

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2023年第2期8卷 232-244页

作者： Bi, Jing Zhang, Kaiyi Yuan, Haitao Zhang, Jia Beijing Univ Technol Sch Software Engn Fac Informat Technol Beijing 100124 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100190 Peoples R China Southern Methodist Univ Dept Comp Sci Lyle Sch Engn Dallas TX 75205 USA

As a promising paradigm, mobile edge computing (MEC) provides cloud resources in a network edge to offer low-latency services to mobile devices (MDs). MEC addresses the limited resource and energy issues of MDs by deploying edge servers, which are often located in small base stations. It is a big challenge, however, as how to dynamically connect resource-limited MDs to nearby edge servers, and reduce total energy consumption by MDs, small base stations and a cloud data center (CDC) all in a hybrid system. To tackle the challenge, this work provides an intelligent computation offloading method for both static and dynamic applications among entities in such a hybrid system. The minimization problem of total energy consumption is first formulated as a typical mixed integer non-linear program. An improved meta-heuristic optimization algorithm, named Particle swarm optimization based on Genetic Learning (PGL), is tailored to solve the problem. PGL synergistically take advantage of both the fast convergence of particle swarm optimization, and the global search ability of genetic algorithm. It jointly optimizes task offloading of heterogeneous applications, bandwidth allocation of wireless channels, MDs' association with small base stations and/or a cloud datacenter, and computing resource allocation of MDs. Numerical results with real-life system configurations prove that PGL outperforms several state-of-the-art peers in terms of total energy consumption of the hybrid system.

关键词： Task analysis Base stations Energy consumption Cloud computing Wireless communication Particle swarm optimization Vehicle dynamics computation offloading MEC cloud computing particle swarm optimization genetic algorithm

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