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An Improved and Efficient Computational Offloading Method Based on ADMM Strategy in cloud-edge collaborative computing Environment for Resilient Industry 5.0

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 1392-1402页

作者： Shu, Wanneng Nie, Shaoliang Jian, Wencheng Ge, Xin South Cent Minzu Univ Coll Comp Sci Wuhan 430074 Peoples R China

With the rapid growth of data demand and the rise of the Industry 5.0, cloud-edge collaborative computing achieves intelligent connectivity and data sharing between devices for intelligent management and optimization of the production process. Computational offloading, as a key technology in the cloud-edge collaborative computing environment, provides higher-quality user services and realizes higher data requirements for intelligent manufacturing and production. In this paper, we proposes an Improved Efficient Alternating Direction Method of Multipliers (IEADMM) for computational offloading in cloud-edge collaborative computing environment, aiming to reduce the total system cost with the optimization goal of minimizing the total time delay. The proposed IEADMM fully utilizes the Alternating Direction Method of Multipliers ADMM) to minimize the total delay by increasing the convergence speed. The experimental result shows that our proposed IEADMM has good performance and practicality for computational offloading in cloud-edge collaborative computing environment, and can be widely applied in practical application scenarios such as intelligent monitoring, Internet of Things (IoT), mobile games, etc.

关键词： Task analysis Servers Costs Computational modeling Symbols Optical fiber networks Improved efficient alternating direction method of multipliers cloud-edge collaborative computing MEC server computational offloading resilient industry 50

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Network Anomaly Traffic Detection Using WGAN-CNN-BiLSTM in Big Data cloud-edge collaborative computing Environment

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2024年第3期20卷 375-390页

作者： Wang, Yue Nanyang Inst Technol Sch Comp & Software Nanyang Henan Peoples R China

edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience;however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud- edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deeplearning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine -tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short -term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. T

关键词： Abnormal Traffic Mining Big Data BiLSTM cloud-edge collaborative computing CNN Wasserstein Generative Adversarial Networks

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Priority-Based Offloading Optimization in cloud-edge collaborative computing

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IEEE TRANSACTIONS ON SERVICES computing 2023年第6期16卷 3906-3919页

作者： He, Zhenli Xu, Yanan Zhao, Mingxiong Zhou, Wei Li, Keqin Yunnan Univ Engn Res Ctr Cyberspace Sch Software Kunming 650091 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

As an emerging computing paradigm, cloud-edge collaborative computing (CECC) combines computing resources at the back-end and the edge of the network to provide more flexible service delivery, thus striking a good balance between abundant computing resources and high responsiveness. However, mobile devices (MDs) must make strategic offloading decisions in such an environment. Although existing research has made remarkable progress in computation offloading strategies, most works ignore multi-priority settings in complex application scenarios. In this article, we focus on the impact of multi-priority settings and mixed queue disciplines on offloading decisions in CECC. First, we utilize queueing models to characterize all computing nodes in the environment and establish mathematical models to describe the considered scenario. Second, we formulate offloading decisions of the target MD into three multi-variable optimization problems to investigate the cost-performance tradeoff. Third, we propose numerical algorithms based on the Karush-Kuhn-Tucke conditions to address these problems. Finally, we construct numerical examples, a comparative experiment, and a simulation experiment to demonstrate the effectiveness of our methods. Our work provides important insights into the optimization of computation offloading for MDs in complex application scenarios, which can help achieve a better cost-performance tradeoff in CECC.

关键词： Cost-performance tradeoff cloud-edge collaborative computing offloading optimization queue disciplines task priorities

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A Heterogeneous cloud-edge collaborative computing Architecture with Affinity-Based Workflow Scheduling and Resource Allocation for Internet-of-Things Applications

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MOBILE NETWORKS & APPLICATIONS 2023年第4期28卷 1443-1459页

作者： Lyu, Shuyu Dai, Xinfa Ma, Zhong Zhou, Ying Liu, Xing Gao, Yi Hu, Zhekun Harbin Engn Univ Haerbin Peoples R China Wuhan Digital Engn Inst Wuhan Peoples R China Wuhan Univ Technol Wuhan Peoples R China

cloud-edge collaborative computing (CECC) is a critical way to solve the real-time problems in the Medical Internet of Things (MIoT) and Industrial IoT (IIoT) applications. However, how to efficiently schedule the real-time and non real-time tasks to the cloud or edge servers that are configured with heterogeneous computing resources such as GPUs, NPUs and FGPAs remains a critical challenge. To address this challenge, this paper first defines the capability elements of CECC architecture, and then formally describes the workflow containerized tasks and heterogeneous computing resources of CECC system. Next, it proposes a heuristic task scheduling algorithm based on the affinity between tasks and nodes (physical machines or virtual machines) by matching the task resource requests with the node resource configurations. Later, it generates the initial mapping matrix between tasks and nodes with the affinity sorting result. And finally, it optimizes the mapping results under the constraints of limited resources and task dependency, which consequently generates an efficient scheduling scheme for the real-time IoT tasks in the CECC system. Experimental results demonstrate that the proposed algorithm can effectively increases the utilization efficiency of heterogeneous resources and improves the scheduling performance of real-time IoT tasks.

关键词： Task scheduling cloud-edge collaborative computing IoT Heterogeneous resource allocation

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Energy-Efficient cloud-edge collaborative computing: Joint Task Offloading, Resource Allocation, and Service Caching 20th

Energy-Efficient Cloud-Edge Collaborative Computing: Joint T...

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20th International Conference on Intelligent computing (ICIC)

作者： Liang, Yong Sun, Haifeng Deng, Yunfeng Southwest Univ Sci & Technol Sch Comp Sci & Technol Mianyang 621010 Sichuan Peoples R China

ISBN: (纸本)9789819756742;9789819756759

Mobile edge computing (MEC) deploys computing and storage resources from cloud centers to edge servers, thereby meeting the latency requirements of emerging applications and lowering energy consumption. Due to the task diversity, computational services necessary for these tasks must be cached at the edge server to efficiently handle computational tasks. However, due to the constraints of resources in the edge server, strategically caching meaningful computing services becomes crucial. After investigating a three-tier network architecture composed of IoT Mobile Devices (IMDs), one edge server and the cloud server, we propose a cloud-edge collaborative computing strategy that jointly optimizes service caching, resource allocation for communication bandwidth and computing, and task offloading decisions. The goal is to achieve long-term minimization of energy consumption for IMDs while processing tasks under latency constraints. We propose an algorithm employing Twin Delayed Deep Deterministic Policy Gradient (TD3), an advanced reinforcement learning algorithm, to address this Mixed-Integer Non-Linear Programming (MINLP) problem. The simulation results demonstrate that the proposed algorithm surpasses baseline methods by significantly reducing task processing energy consumption across all IMDs, while ensuring compliance with latency constraints.

关键词： Mobile edge computing service caching cloud-edge collaborative computing

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Task partitioning and offloading in IoT cloud-edge collaborative computing framework: a survey

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JOURNAL OF cloud computing-ADVANCES SYSTEMS AND APPLICATIONS 2022年第1期11卷 1-19页

作者： Chen, Haiming Qin, Wei Wang, Lei Ningbo Univ Fac Elect Engn & Comp Sci Ningbo Peoples R China

Internet of Things (IoT) is made up with growing number of facilities, which are digitalized to have sensing, networking and computing capabilities. Traditionally, the large volume of data generated by the IoT devices are processed in a centralized cloud computing model. However, it is no longer able to meet the computational demands of large-scale and geographically distributed IoT devices for executing tasks of high performance, low latency, and low energy consumption. Therefore, edge computing has emerged as a complement of cloud computing. To improve system performance, it is necessary to partition and offload some tasks generated by local devices to the remote cloud or edge nodes. However, most of the current research work focuses on designing efficient offloading strategies and service orchestration. Little attention has been paid to the problem of jointly optimizing task partitioning and offloading for different application types. In this paper, we make a comprehensive overview on the existing task partitioning and offloading frameworks, focusing on the input and core of decision engine of the framework for task partitioning and offloading. We also propose comprehensive taxonomy metrics for comparing task partitioning and offloading approaches in the IoT cloud-edge collaborative computing framework. Finally, we discuss the problems and challenges that may be encountered in the future.

关键词： IoT cloud-edge collaborative computing Task partitioning Offloading

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A cloud-edge collaborative computing Framework for Model Partitioning Based on Genetic Algorithm and Reinforcement Learning 21

A Cloud-Edge Collaborative Computing Framework for Model Par...

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21st International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Xiabing, Wang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China

ISBN: (纸本)9798331519254

Deep Neural Networks are rapidly attracting widespread attention as a key technology for AI applications in 5G era. However, running DNN-based computation tasks on mobile devices is challenging due to limited computational resources and conventional cloud-based computation may lead to cloud overloading. In this paper, we introduce a novel cloud-edge collaborative computing framework to optimize task allocation between cloud and edge. The framework is based on model partitioning and explores different partitioning strategies to adapt to different edge cluster environments through genetic algorithms and reinforcement learning. It achieves flexible allocation of edge resources and effective balance of computing demands. The experiment show that the proposed method effectively reduces the cloud load and the combination of genetic algorithm and reinforcement learning greatly improves the efficiency of finding the optimal solution. © 2024 IEEE.

关键词： cloud-edge collaborative computing Genetic algorithm Reinforcement learning

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A cloud-edge collaborative computing framework using potential games for space-air-ground integrated IoT

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2024年第1期2024卷 54页

作者： Peng, Yuhuai Guang, Xiaoliang Zhang, Xinyu Liu, Lei Wu, Cemulige Huang, Lei Northeastern Univ Sch Comp Sci & Engn 11Lane 3Culture Rd Shenyang 110819 Peoples R China Xidian Univ Guangzhou Inst Technol Huangpu Dist Zhongxin Knowledge City Guangzhou 510555 Peoples R China Univ Electrocommun Meta Networking Res Ctr Tokyo 1828585 Japan

As a critical component of space-air-ground integrated IoT, the aerial network provides highly reliable, low-latency and ubiquitous information services to ground users by virtue of their high mobility, easy deployment and low cost. However, the current computation and resource management model of air-ground integrated networks are insufficient to meet the latency demanding of emerging intelligent services such as autonomous systems, extended reality and haptic feedback. To tackle these challenges, we propose a computation offloading and optimization method based on potential game. First, we construct an cloud-edge collaborative computing model. Secondly, we construct Offloading Decision Objective Functions (ODOF) with the objective of minimum task processing latency and energy consumption. ODOF is proved to be a Mixed Inferior Nonlinear Programming (MINLP) problem, which is hard to solve. ODOF is converted to be a full potential game, and the Nash equilibrium solution exists. Then, a computational resource allocation algorithm based on Karush-Kuhn-Tucker (KKT) conditions is proposed to solve resource allocation problem. On this basis, a distributed game-based computational offloading algorithm is proposed to minimize the offloading cost. Extensive simulation results demonstrate that the convergence performance of the proposed algorithm is reduced by 50%, the convergence time is reduced by 13.3% and the average task processing delay is reduced by 10%.

关键词： Space-air-ground integrated IoT cloud-edge collaborative computing Resource allocation Offloading decisions Potential game

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Attacks classification and data privacy protection in cloud-edge collaborative computing systems

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2024年

作者： Devarajan, Mohanarangan Veerappermal Yallamelli, Akhil Raj Gaius Yalla, Rama Krishna Mani Kanta Mamidala, Vijaykumar Ganesan, Thirusubramanian Sambas, Aceng Ernst & Young EY 731 K St Suite 300 Sacramento CA 95814 USA Amazon Web Serv Inc Seattle WA USA Conga Apttus Broomfield CO USA Cognizant Technol Solut Plano TX USA Univ Muhammadiyah Tasikmalaya Tasikmalaya Indonesia

collaborative computing systems used today are a significant problem, and the variety of attacks and data privacy protection play an important role. Accordingly, this research takes the latest technology, such as federated learning and cloud-edge collaborative computing systems. Among them, multi-national validation with attacks/without attacks architecture is mainly developed, and 'End-to-end privacy-preserving deep learning for attack classification method is used to classify each episode that occurs and done through End-to-end privacy-preserving deep understanding (E2EPPDL). This is the most essential core component of our research. We justified them with Time, Node Count, Routing count, and Data delivery ratio estimates.

关键词： Attacks classification cloud computing cloud-edge collaborative computing data privacy edge computing federated learning

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Multi-objective optimization task offloading decision for intelligent transportation system in cloud edge collaborative computing scenario

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WIRELESS NETWORKS 2025年第3期31卷 2797-2816页

作者： Zhu, Si-feng Liu, Cheng-tai Zhu, Hai Qiao, Rui Chen, Hao Zhang, Hui Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin 300384 Peoples R China Zhoukou Normal Univ Sch Network Engn Zhoukou 466001 Peoples R China

With the widespread popularity of family cars, traffic management problems are becoming increasingly complex, and a large number of real-time processing tasks have emerged in intelligent traffic management systems. To solve the task offloading decision problem in the multi-user and multi-server scenarios of intelligent transportation system, the communication model, cache model, delay model, energy consumption model, load model and multi-objective optimization task offloading model were designed, and an offloading decision scheme based on NSGA-III algorithm was proposed in this paper. The experimental results show that the proposed scheme can reduce the vehicle task offloading delay and system energy consumption of the intelligent transportation system, and has a good application prospect.

关键词： Intelligent transportation cloud-edge collaborative computing Offloading decision Multi-objective optimization algorithm NSGA-III algorithm

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