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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Kang, Hong Li, Minghao Fan, Sizheng Cai, Wei Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9781665491228

Mobile Edge Computing (MEC) enables computation offloading from resource-constrained mobile devices to edge servers in close vicinity, effectively promoting the user experience on emerging interactive multimedia applications such as virtual/augmented reality, mobile gaming, and mobile video editing. However, most contemporary MEC offloading research disregards the interdependencies between partitioned subtasks of application. Also, few studies focused on application topologies have neglected to design effective incentives to encourage edge servers to provide offloading services. In this paper, we propose a dependency-aware offloading algorithm based on a multi-round truthful combinatorial reverse auction (MTCRA) to address the social welfare maximization problem in the paradigm of MEC. Building on the topology of directed acyclic graphs (DAGs) modeled from applications, we discuss the complementarity and substitutability of subtasks in the context of combinatorial auction. Theoretical analysis shows that the presented auction mechanism achieves computing efficiency while maintaining desirable economic features like truthfulness, individual rationality, and budget balance. Simulation results demonstrate that the proposed algorithm achieves high social welfare regarding reduced execution time and good economic benefits for MEC servers.

关键词： Mobile edge computing auction mechanism computational offloading dependent application

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Differential Pricing-Based Task offloading for Delay-Sensitive IoT Applications in Mobile Edge Computing System

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IEEE INTERNET OF THINGS JOURNAL 2022年第19期9卷 19116-19131页

作者： Seo, Hyeonseok Oh, Hyeontaek Choi, Jun Kyun Park, Sangdon Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34051 South Korea Korea Adv Inst Sci & Technol KAIST Inst IT Convergence Daejeon 34051 South Korea

With the evolutionary development of the Internet of Things (IoT), the demand for delay-sensitive applications has increased. In this manner, mobile-edge computing (MEC) has emerged as a promising technology to run these applications on mobile devices. Several studies have been conducted on pricing schemes in the MEC environment, and they have focused on the amount of offloaded data in pricing. However, under the previously proposed pricing policy, the server resource usage of users is not properly reflected in the payment, so users only try to occupy the server resources as much as possible without any consideration. To solve this problem, this article newly proposes a differential pricing scheme in which the unit price per second is determined based on the user's usage of server computational resources. Also, it suggests the user's optimal offloading strategy and server's equilibrium pricing strategy by formulating Stackelberg game based on the proposed pricing scheme and execution delay. Several numerical results show that the proposed pricing scheme and strategies can solve the problems and can increase the efficiency of edge server's computational resources.

关键词： Servers Pricing Delays Cloud computing Internet of Things Mobile handsets Games computational offloading differential pricing scheme execution delay minimization Internet of Things (IoT) mobile-edge computing (MEC) offloading decision

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Distributed Machine Learning for Multiuser Mobile Edge Computing Systems

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2022年第3期16卷 460-473页

作者： Guo, Yinghao Zhao, Rui Lai, Shiwei Fan, Lisheng Lei, Xianfu Karagiannidis, George K. Guangzhou Univ Sch Comp Sci Guangzhou 510006 Peoples R China Southwest Jiaotong Univ Prov Key Lab Informat Coding & Transmiss Chengdu 610031 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Aristotle Univ Thessaloniki Thessaloniki Greece

In this paper, we investigate a distributed machine learning approach for a multiuser mobile edge computing (MEC) network in a cognitive eavesdropping environment, where multiple secondary devices (SDs) have some tasks with different priorities to be computed. The SDs can be allowed to use the wireless spectrum as long as the interference to the primary user is tolerated, and an eavesdropper in the network can overhear the confidential message from the SDs, which threatens the data offloading. For the considered system, we firstly present three optimization criteria, whereas criterion I aims to minimize the linear combination of latency and energy consumption, criterion II tries to minimize the latency under a constraint on the energy consumption, and criterion III is to minimize the energy consumption under a constraint on the latency. We then exploit a federated learning framework to solve these optimization problems, by optimizing the offloading ratio, bandwidth and computational capability allocation ratio. Simulation results are finally demonstrated to show that the proposed method can effectively reduce the system cost in terms of latency and energy consumption, and meanwhile ensure more bandwidth and computational capability allocated to the user with a higher taskpriority.

关键词： Task analysis Optimization Energy consumption Collaborative work Mobile handsets Bandwidth Distance learning computational offloading federated learning resource allocation task priority

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Design of Healthcare Blockchain Systems to Support Accessible Edge Computing 11

Design of Healthcare Blockchain Systems to Support Accessibl...

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11th International Conference on Information Technology - IoT and Smart City (ITIoTSC)

作者： Cain, Yongqi Ma, Ziqiang Lan, Yajie Jiao, Jiyu Chen, Jiali Ningxia Univ Sch Elect & Elect Engn Collaborat Innovat Ctr Ningxia Big Data & Artific Yinchuan 750021 Ningxia Peoples R China Ningxia Univ Sch Informat Engn Collaborat Innovat Ctr Ningxia Big Data & Artific Yinchuan 750021 Ningxia Peoples R China Southeast Univ Sch Informat Sci & Engn Natl Mobile Commun Res Lab Nanjing 211100 Peoples R China

ISBN: (纸本)9798350328356;9798350328349

Edge computing is an extension of IoT evolution and cloud computing technology development, which is closer to data sources and can provide low-latency and high-bandwidth service support to end users. In this paper, we propose a blockchain-enabled offloading method for edge computing to address the data silos and high latency of service delivery in the healthcare industry, using a cloud-edge-chain three-layer underlying architecture model to achieve a joint design of task offloading and blockchain updates to improve the quality of healthcare services, and blockchain technology to provide authentication for edge service nodes to improve system security, data traceability, and user identity management. The results show that compared with the traditional reinforcement learning Q-learning algorithm, the computational offloading method of the DRLO algorithm designed in this paper converges faster and can effectively reduce the total system processing latency under the same conditions, with a 26% reduction in system processing latency and a 9.2% increase in reward value. Meanwhile, with the change of local terminal computing resources, the algorithm proposed in this paper can still keep the total system latency at a low level.

关键词： medical blockchain edge compute computational offloading

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Task offloading Based on LSTM Prediction and Deep Reinforcement Learning for Efficient Edge Computing in IoT

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FUTURE INTERNET 2022年第2期14卷 30-30页

作者： Tu, Youpeng Chen, Haiming Yan, Linjie Zhou, Xinyan Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Ningbo Univ Zhejiang Prov Key Lab Mobile Network Applicat Tec Ningbo 315211 Peoples R China

In IoT (Internet of Things) edge computing, task offloading can lead to additional transmission delays and transmission energy consumption. To reduce the cost of resources required for task offloading and improve the utilization of server resources, in this paper, we model the task offloading problem as a joint decision making problem for cost minimization, which integrates the processing latency, processing energy consumption, and the task throw rate of latency-sensitive tasks. The Online Predictive offloading (OPO) algorithm based on Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) networks is proposed to solve the above task offloading decision problem. In the training phase of the model, this algorithm predicts the load of the edge server in real-time with the LSTM algorithm, which effectively improves the convergence accuracy and convergence speed of the DRL algorithm in the offloading process. In the testing phase, the LSTM network is used to predict the characteristics of the next task, and then the computational resources are allocated for the task in advance by the DRL decision model, thus further reducing the response delay of the task and enhancing the offloading performance of the system. The experimental evaluation shows that this algorithm can effectively reduce the average latency by 6.25%, the offloading cost by 25.6%, and the task throw rate by 31.7%.

关键词： computational offloading resource allocation prediction DRL LSTM

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Deep reinforcement learning-based low-latency task offloading for mobile-edge computing networks

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APPLIED SOFT COMPUTING 2024年 166卷

作者： Yang, Wentao Liu, Zhibin Liu, Xiaowu Ma, Yuefeng Qufu Normal Univ Sch Comp Sci Rizhao 276826 Peoples R China

Wireless Mobile Edge Computing (MEC) is an emerging solution to facilitate users' computing. However, there are still some drawbacks, such as slow learning speed and high task latency. To overcome these difficulties, a deep reinforcement learning (DRL)-based task offloading (DRTO) framework is proposed, taking into account the time-varying nature of wireless channels and the unpredictability of task arrivals in multi-user MEC systems. Many deep neural networks (DNNs) are also used as scalable solutions to quickly determine the best offloading strategy by combining the perceptual capabilities of deep learning with the decision making capabilities of reinforcement learning (RL). Additionally, the concept of a minimum upper bound on the target Q-value is utilized to address the value overestimation problem that occurs during offload policy updates. This approach aims to minimize the latency of offload decisions by considering different channel environments, variable latency constraints among many users, and limited computational resources. Simulation results show that the algorithm reduces computation time and latency while achieving near-optimal performance compared to traditional Deep Deterministic Policy Gradient (DDPG) and Q-learning optimization methods. In addition, DRTO achieves more than 99.6% computational speedup compared to current almost ideal benchmarking techniques. Thus, the DRTO computational offload approach offers great potential for reducing overall system latency and improving the user experience.

关键词： Mobile-edge computing Resource allocation Reinforcement learning computational offloading Deep reinforcement learning

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Facilitating Deep Learning for Edge Computing: A Case Study on Data Classification 5

Facilitating Deep Learning for Edge Computing: A Case Study ...

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5th IEEE Conference on Dependable and Secure Computing (IEEE DSC)

作者： Alsalemi, Abdullah Amira, Abbes Malekmohamadi, Hossein Diao, Kegong De Montfort Univ Inst Artificial Intelligence Leicester Leics England Univ Sharjah Coll Comp & Informat Sharjah U Arab Emirates De Montfort Univ Inst Energy & Sustainable Dev Leicester Leics England

ISBN: (数字)9781665421416

ISBN: (纸本)9781665421416

Deep Learning (DL) is increasingly empowering technology and engineering in a plethora of ways, especially when big data processing is a core requirement. Many challenges, however, arise when solely depending on cloud computing for Artificial Intelligence (AI), such as data privacy, communication latency, and power consumption. Despite the elevating popularity of edge computing, its overarching issue is not the lack of technical specifications in many edge computing platforms but the sparsity of comprehensive documentation on how to correct utilize hardware to run ML and DL algorithms. Due to its specialized nature, installing the full version of TensorFlow, a common ML library, on an edge device is a complicated procedure that is seldom successful, due to the many dependent software libraries needed to be compatible with varying architectures of edge computing devices. Henceforth, in this paper, we present a novel technical guide on setting up the TensorFlow Lite, a lightweight version of TensorFlow, and demonstrate a complete workflow of model training, validation, and testing on the ODROID-XU4. Results are presented for a case study on energy data classification using the outlined model show almost 7 times higher computational performance compared to cloud-based AI.

关键词： Edge computing artificial intelligence deep learning data classification computational offloading

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Distributed Robotic Systems in the Edge-Cloud Continuum with ROS 2: a Review on Novel Architectures and Technology Readiness 7

Distributed Robotic Systems in the Edge-Cloud Continuum with...

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7th International Conference on Fog and Mobile Edge Computing (FMEC)

作者： Zhang, Jiaqiang Keramat, Farhad Yu, Xianjia Hernandez, Daniel Montero Queralta, Jorge Pena Westerlund, Tomi Univ Turku Turku Intelligent Embedded & Robot Syst TIERS Lab Turku Finland

ISBN: (纸本)9798350334524

Robotic systems are more connected, networked, and distributed than ever. New architectures that comply with the de facto robotics middleware standard, ROS 2, have recently emerged to fill the gap in terms of hybrid systems deployed from edge to cloud. This paper reviews new architectures and technologies that enable containerized robotic applications to seamlessly run at the edge or in the cloud. We also overview systems that include solutions from extension to ROS 2 tooling to the integration of Kubernetes and ROS 2. Another important trend is robot learning, and how new simulators and cloud simulations are enabling, e.g., large-scale reinforcement learning or distributed federated learning solutions. This has also enabled deeper integration of continuous interaction and continuous deployment (CI/CD) pipelines for robotic systems development, going beyond standard software unit tests with simulated tests to build and validate code automatically. We discuss the current technology readiness and list the potential new application scenarios that are becoming available. Finally, we discuss the current challenges in distributed robotic systems and list open research questions in the field.

关键词： Robotics Distributed Robotic Systems EdgeCloud Continuum Cloud robotics Edge computing ROS 2 Containerization computational offloading

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Energy Efficient Priority-Based Task Scheduling for Computation offloading in Fog Computing 1

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21st International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Yin, Jiaying Fu, Jing Wu, Jingjin Zheng, Shiming BNU HKBU United Int Coll Div Sci & Technol Zhuhai Guangdong Peoples R China RMIT Univ Sch Engn Melbourne Vic Australia

ISBN: (数字)9783030953843

ISBN: (纸本)9783030953843;9783030953836

Fog computing offers a flexible solution for computational offloading for Internet of Things (IoT) services at the edge of wireless networks. It serves as a complement to traditional cloud computing, which is not cost-efficient for most offloaded tasks in IoT applications involving small-to-medium levels of computing tasks. Given the heterogeneity of tasks and resources in fog computing, it is vital to offload each task to an appropriate destination to fully utilize the potential benefit of this promising technology. In this paper, we propose a scalable priority-based index policy, referred to as the Prioritized Incremental Energy Rate (PIER), to optimize the energy efficiency of the network. We demonstrate that PIER is asymptotically optimal in a special case applicable for local areas with high volumes of homogeneous offloaded tasks and exponentially distributed task durations. In more general cases with statistically different offloaded tasks, we further demonstrate the improvement of PIER over benchmark policies in terms of energy efficiency and the robustness of PIER to different task duration distributions by extensive simulations. Our results show that PIER can perform better than benchmark policies in more than 78.6% of all simulation runs.

关键词： Priority-based policies Energy efficiency Fog computing computational offloading Internet of Things

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MADDPG-Based offloading Strategy for Timing-Dependent Tasks in Edge Computing

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FUTURE INTERNET 2024年第6期16卷 181页

作者： Wang, Yuchen Huang, Zishan Wei, Zhongcheng Zhao, Jijun Hebei Univ Engn Sch Informat & Elect Engn Handan 056038 Peoples R China Hebei Key Lab Secur & Protect Informat Sensing & P Handan 056038 Peoples R China

With the increasing popularity of the Internet of Things (IoT), the proliferation of computation-intensive and timing-dependent applications has brought serious load pressure on terrestrial networks. In order to solve the problem of computing resource conflict and long response delay caused by concurrent application service applications from multiple users, this paper proposes an improved edge computing timing-dependent, task-offloading scheme based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that aims to shorten the offloading delay and improve the resource utilization rate by means of resource prediction and collaboration among multiple agents to shorten the offloading delay and improve the resource utilization. First, to coordinate the global computing resource, the gated recurrent unit is utilized, which predicts the next computing resource requirements of the timing-dependent tasks according to historical information. Second, the predicted information, the historical offloading decisions and the current state are used as inputs, and the training process of the reinforcement learning algorithm is improved to propose a task-offloading algorithm based on MADDPG. The simulation results show that the algorithm reduces the response latency by 6.7% and improves the resource utilization by 30.6% compared with the suboptimal benchmark algorithm, and it reduces nearly 500 training rounds during the learning process, which effectively improves the timeliness of the offloading strategy.

关键词： edge computing task dependency computational offloading resource prediction deep reinforcement learning multi-agent collaboration

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