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Method towards collaborative cloud and edge computing via RBC for joint communication and computation resource allocation

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2025年 44卷

作者： Gao, Ruiling Zhang, Wenzhong Mao, Wenyi Tan, Jinjing Zhang, Jin Huang, Haiyun Tan, Wen'an Huang, Feiyue Shanghai Polytech Univ Sch Comp & Informat Engn Shanghai 201219 Peoples R China Shanghai Polytech Univ Sch Resources & Environm Engn Shanghai 201219 Peoples R China Tufts Univ Medford MA 02155 USA Shanghai Jiao Tong Univ Ruijin Hosp Digital Med Innovat Ctr Sch Med Shanghai 200025 Peoples R China

With the extensive adoption of cloud and edge computing in intelligent manufacturing systems driven by the Industrial Internet of Things (IIoT) and Artificial Intelligence, enhancing the efficiency of cloud-edge collaboration under constrained communication and computational resources has emerged as a prominent research focus. We develop the GRALB model, which is based on Role-Based Collaboration (RBC) in cooperative services, to comprehensively manage the offloading strategy of terminal user tasks between edge nodes and the cloud to solve the joint communication and computing resource allocation problem in intelligent manufacturing systems. First, we jointly model the end-to-end latency and energy consumption based on the physical scenario of cloud- edge collaboration. Then, we extend the GRA model based on E-CARGO and propose the GRALB model with load balancing, which formally models the original joint communication and computing resource allocation problem as an equivalent cooperative service model and provides a proof of algorithm convergence. Finally, we design an x-ILP solution to support the verification and integrated application of the proposed model. Simulation results further confirm our theoretical analysis and show that the proposed collaborative cloud and edge computing solution significantly improves the overall system performance.

关键词： cloud and edge computing Intelligent manufacturing systems Offloading Strategy RBC Load Balancing

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Computation Offloading in LEO Satellite Networks With Hybrid cloud and edge computing

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IEEE INTERNET OF THINGS JOURNAL 2021年第11期8卷 9164-9176页

作者： Tang, Qingqing Fei, Zesong Li, Bin Han, Zhu Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Guangxi Key Lab Multimedia Commun & Network Techn Nanning 530004 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

Low earth orbit (LEO) satellite networks can break through geographical restrictions and achieve global wireless coverage, which is an indispensable choice for future mobile communication systems. In this article, we present a hybrid cloud and edge computing LEO satellite (CECLS) network with a three-tier computation architecture, which can provide ground users with heterogeneous computation resources and enable ground users to obtain computation services around the world. With the CECLS architecture, we investigate the computation offloading decisions to minimize the sum energy consumption of ground users, while satisfying the constraints in terms of the coverage time and the computation capability of each LEO satellite. The considered problem leads to a discrete and nonconvex since the objective function and constraints contain binary variables, which makes it difficult to solve. To address this challenging problem, we convert the original nonconvex problem into a linear programming problem by using the binary variables relaxation method. Then, we propose a distributed algorithm by leveraging the alternating direction method of multipliers (ADMMs) to approximate the optimal solution with low computational complexity. Simulation results show that the proposed algorithm can effectively reduce the total energy consumption of ground users.

关键词： Satellites Low earth orbit satellites Task analysis cloud computing Servers Computational modeling Delays Alternating direction method of multipliers (ADMMs) cloud and edge computing computation offloading low earth orbit (LEO) satellite networks

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Joint Computation Offloading and Resource Allocation for Hybrid cloud and edge computing in Satellite-assisted Unmanned System 39

Joint Computation Offloading and Resource Allocation for Hyb...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Wu, Yeyu Fan, Huili Yu, Jian Yang, Li Huang, Qilong Qi, Yaowen Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China China Ship Dev & Design Ctr Wuhan Peoples R China

ISBN: (纸本)9798350390780;9798350379228

Unmanned systems such as unmanned surface vehicle (USV) and unmanned aerial vehicle (UAV) are difficult to undertake complex computational tasks on their own due to their limited computational capabilities. The existing method of sending the computation tasks to the ground terminal for processing will bring large transmission delay and reduce the real-time performance of task execution. Inspired by terrestrial mobile edge computing (MEC), deploying edge computing servers on low-orbit satellites (LEO) allows unmanned system to offload tasks to the satellite to enhance the task processing efficiency. However, owing to restricted computing and communication resources of satellite, designing appropriate computational offloading and resource allocation strategies is of great relevance. In this article, we propose a partial computation offloading strategy for a three-tier edge computing architecture consisting of unmanned system, satellites and cloud. We consider the energy optimization of the system under resource constraints while meeting the requirements of tasks. To address this issue, we propose a computational offloading method based on Deep Deterministic Policy Gradient (DDPG). This method can simultaneously decide the optimal offloading ratio, offloading location and resource allocation ratio under the dynamic environment with multi-task concurrency. The proposed method meets task's requirements while effectively decreasing the total energy consumption of system. Finally, experimental results prove the effectiveness of the formulated method.

关键词： Unmanned system Computation offloading cloud and edge computing Deep deterministic policy gradient Resource allocation

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Intelligent weed management using aerial image processing and precision herbicide spraying: An overview

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CROP PROTECTION 2025年 194卷

作者： Ehrampoosh, Armin Hettiarachchi, Pushpika Koirala, Anand Hassan, Jahan Islam, Nahina Ray, Biplob Nabi, Md Nurun Tolba, Mohamed Mazid, Abdul Md Xu, Cheng-Yuan Ashwath, Nanjappa Dzitac, Pavel Moore, Steven Cent Queensland Univ Sch Engn & Technol Norman Gardens Australia Cent Queensland Univ Sch Hlth Med & Appl Sci Norman Gardens Australia Cent Queensland Univ Ctr Machine Learning Networking & Educ Technol CML Norman Gardens Australia Cent Queensland Univ Ctr Reg Econ & Supply Chains Norman Gardens Australia Charles Darwin Univ Res Inst Environm & Livelihoods Casuarina Australia Res Inst Northern Agr Casuarina Australia Leica Biosyst Mt Waverley Australia

Modern agriculture is increasingly adopting intelligent technologies to enhance productivity while minimizing production costs and reducing adverse environmental impacts. A prime example of this synergy is the use of image processing to identify weeds, enabling targeted herbicide spraying with autonomous devices such as robots and drones. This approach not only reduces production costs but also ensures sustainable farming while minimizing negative environmental impacts. Designing an intelligent weed management system requires a multidisciplinary approach, combining agriculture, big data processing, machine learning, computer science, robotics, and plant science. Currently, independent studies have focused on some of these aspects, but few have taken a holistic approach to address the issue. This paper highlights the approach taken in developing innovative and ecologically sustainable weed management systems for agriculture. It also presents a comprehensive overview of a weed management system that integrates coordinated weed detection and spraying, detailing its unique components. The paper reviews and contrasts various image analysis techniques used in weed detection, particularly those employing artificial intelligence and imagery captured by unmanned aerial vehicles (UAVs). Furthermore, the paper highlights recent advancements in image processing platforms, such as the shift towards local and edge computing, and the growing need for near-real-time processing in agricultural applications. It also explores the development of commercial weed-spraying drones and discusses various aspects of an autonomous weed control system, including design, navigation, and spraying mechanisms for targeted application. Finally, the paper identifies key research needs for developing an AI-based, targeted herbicide spraying system that could significantly contribute to sustainable, economically viable, and efficient agricultural practices.

关键词： Aerial image processing Artificial intelligence Blanket spraying cloud and edge computing Deep learning Machine learning Precision herbicide spraying Smart farming Sprayer-drones UAVs-based spot spraying Weed detection Weed management

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Bilateral collaborative computing offloading via LEO satellites for remote network applications

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COMPUTER NETWORKS 2025年 261卷

作者： Xu, Shuang Li, Xutong Zhang, Jiayu Li, Feng Liang, Yuanyuan Hao, Shufeng Taiyuan Univ Technol Coll Artificial Intelligence Taiyuan 030002 Shanxi Peoples R China

In remote areas, due to weak network infrastructure, limited device resources, and diverse application requirements, smart terminals face the challenge of reducing energy consumption while maintaining low latency and high reliability data processing. To address these issues, we integrate the advantages of Low Earth Orbit (LEO) satellites communications and edge computing, and propose a bilateral collaborative offloading mechanism assisted by LEO satellites. In ecological monitoring missions, data are collected by GATs. The results are demanded and used by ground environmental monitoring centers located indifferent locations. We make use of the features that satellite networks have full coverage and multiple ground base stations (GBS) are close to the environmental center to provide stable network support and achieve bilateral collaborative computing offloading. These ground base stations, working in collaboration with Low Earth Orbit (LEO) satellites, can solve the problem of limited computing resources when satellites handle large-scale tasks, while avoiding some problems caused by cloud computing. Results show that this mechanism obviously optimizes the task execution delay and terminal energy.

关键词： Bilateral collaborative computing offloading Low earth orbit satellites cloud and edge computing

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End-to-End Performance-Based Autonomous VNF Placement With Adopted Reinforcement Learning

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2020年第2期6卷 534-547页

作者： Bunyakitanon, Monchai Vasilakos, Xenofon Nejabati, Reza Simeonidou, Dimitra Univ Bristol Smart Internet Lab Bristol BS8 1UB Avon England

The autonomous placement of Virtual Network Functions (VNFs) is a key aspect of Zero-touch network and Service Management (ZSM) in Fifth Generation (5G) networking. Therefore, current orchestration frameworks need to be enhanced, accordingly. To address this need, this work presents an Adapted REinforcement Learning VNF Performance Prediction module for Autonomous VNF Placement, namely AREL3P. Our solution design bears a dual novelty. First, it leverages end-to-end service-level performance predictions for placing VNFs. Second, whereas the majority of other Machine Learning efforts in the literature use Supervised Learning (SL) techniques, AREL3P is based on a particular form of Reinforcement Learning adapted to predictions. This makes placement decisions more resilient to dynamic conditions, as well as portable to other network nodes, and able to generalize in heterogeneous network environments. Backed by a meticulous performance evaluation over a real 5G end-to-end testbed, we verify the above properties after integrating AREL3P to Open Source Management and Orchestration (OSM MANO) decisions. Among other highlights, we show increased VNF performance predictions accuracy by 40-45%, and an overall improved VNF placement efficiency against other SL benchmarks reflected by near-optimal decision scores in 23 out of a total of 27 investigated scenarios.

关键词： Machine learning network function virtualization end-to-end communication zero-touch management cloud and edge computing

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Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4348-4361页

作者： Wang, Kui Li, Zongdian Nonomura, Kazuma Yu, Tao Sakaguchi, Kei Hashash, Omar Saad, Walid Tokyo Inst Technol Sch Engn Dept Elect & Elect Engn Tokyo 1528550 Japan Virginia Tech Bradley Dept Elect & Comp Engn Arlington VA 24061 USA Lebanese Amer Univ Artificial Intelligence & Cyber Syst Res Ctr Beirut Lebanon

Digital twins (DTs) have driven major advancements across various industrial domains over the past two decades. With the rapid advancements in autonomous driving and vehicle-to-everything (V2X) technologies, integrating DTs into vehicular platforms is anticipated to further revolutionize smart mobility systems. In this paper, a new smart mobility DT (SMDT) platform is proposed for the control of connected and automated vehicles (CAVs) over next-generation wireless networks. In particular, the proposed platform enables cloud services to leverage the abilities of DTs to promote the autonomous driving experience. To enhance traffic efficiency and road safety measures, a novel navigation system that exploits available DT information is designed. The SMDT platform and navigation system are implemented with state-of-the-art products, e.g., CAVs and roadside units (RSUs), and emerging technologies, e.g., cloud and cellular V2X (C-V2X). In addition, proof-of-concept (PoC) experiments are conducted to validate system performance. The performance of SMDT is evaluated from two standpoints: (i) the rewards of the proposed navigation system on traffic efficiency and safety and, (ii) the latency and reliability of the SMDT platform. Our experimental results using SUMO-based large-scale traffic simulations show that the proposed SMDT can reduce the average travel time and the blocking probability due to unexpected traffic incidents. Furthermore, the results record a peak overall latency for DT modeling and route planning services to be 155.15 ms and 810.59 ms, respectively, which validates that our proposed design aligns with the 3GPP requirements for emerging V2X use cases and fulfills the targets of the proposed design.

关键词： Autonomous vehicles Sensors cloud computing Real-time systems Navigation edge computing Task analysis Smart mobility digital twin navigation system vehicle-to-everything cloud and edge computing implementation

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cloud computing and Local Chip-Based Dynamic Economic Dispatch for Microgrids

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IEEE TRANSACTIONS ON SMART GRID 2020年第5期11卷 3774-3784页

作者： Wang, Siyuan Wang, Xuanding Wu, Wenchuan Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China Guangdong Power Exchange Ctr Guangzhou 510080 Peoples R China

Dynamic economic dispatch in microgrids is usually realized in a centralized energy management system (EMS). However, centralized systems are subject to single-point failure problems. In rural areas and islands, a failed EMS cannot be recovered in a timely manner due to lack of technical support. In this paper, we propose a cloud and edge computing-based framework to realize dynamic economic dispatch, which is conducted on a local Digital Signal Processor (DSP) chip and a remote cloud computing platform (CCP). Based on the multi-parametric programming algorithm, the dispatch process is divided into two parts: offline calculation and real-time decision making. These calculation tasks can be conducted on the remote cloud computing platform and the DSP chip of the inverter in an arbitrary renewable generator, respectively. The tests are carried out on the Amazon Web Service (AWS) EC2 instance and an autonomous microgrid with DSP TMS320F28335 chip. The results show that the proposed method can obtain the same results as the conventional method, with significantly improved reliability.

关键词： Microgrids Economics cloud computing Inverters Energy management Power generation Real-time systems Microgrid multi-parametric programming economic dispatch cloud and edge computing

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cloud and edge based data analytics for privacy-preserving multi-modal engagement monitoring in the classroom

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INFORMATION SYSTEMS FRONTIERS 2021年第1期23卷 151-164页

作者： Preuveneers, Davy Garofalo, Giuseppe Joosen, Wouter Katholieke Univ Leuven Imec DistriNet Celestijnenlaan 200A B-3001 Heverlee Belgium

Learning management systems are service platforms that support the administration and delivery of training programs and educational courses. Prerecorded, real-time or interactive lectures can be offered in blended, flipped or fully online classrooms. A key challenge with such service platforms is the adequate monitoring of engagement, as it is an early indicator for a student's learning achievements. Indeed, observing the behavior of the audience and keeping the participants engaged is not only a challenge in a face-to-face setting where students and teachers share the same physical learning environment, but definitely when students participate remotely. In this work, we present a hybrid cloud and edge-based service orchestration framework for multi-modal engagement analysis. We implemented and evaluated an edge-based browser solution for the analysis of different behavior modalities with cross-user aggregation through secure multiparty computation. Compared to contemporary online learning systems, the advantages of our hybrid cloud-edge based solution are twofold. It scales up with a growing number of students, and also mitigates privacy concerns in an era where the rise of analytics in online learning raises questions about the responsible use of data.

关键词： data analytics multi-modal engagement monitoring privacy cloud and edge computing browser

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edge/cloud Infinite-Time Horizon Resource Allocation for Distributed Machine Learning and General Tasks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第1期21卷 697-713页

作者： Sartzetakis, Ippokratis Soumplis, Polyzois Pantazopoulos, Panagiotis Katsaros, Konstantinos V. Sourlas, Vasilis Varvarigos, Emmanouel Natl Tech Univ Athens Sch Elect & Comp Engn Athens 15773 Greece Natl Tech Univ Athens Inst Commun & Comp Syst Athens 15773 Greece

edge computing has emerged as a computing paradigm where the application and data processing takes place close to the end devices. It decreases the distances over which data transfers are made, offering reduced delay and fast speed of action for general data processing and store/retrieve jobs. The benefits of edge computing can also be reaped for distributed computation algorithms, where the cloud also plays an assistive role. In this context, an important challenge is to allocate the required resources at both edge and cloud to carry out the processing of data that are generated over a continuous ("infinite") time horizon. This is a complex problem due to the variety of requirements (resource needs, accuracy, delay, etc.) that may be posed by each computation algorithm, as well as the heterogeneous resources' features (e.g., processing, bandwidth). In this work, we develop a solution for serving weakly coupled general distributed algorithms, with emphasis on machine learning algorithms, at the edge and/or the cloud. We present a dual-objective Integer Linear Programming formulation that optimizes monetary cost and computation accuracy. We also introduce efficient heuristics to perform the resource allocation. We examine various distributed ML allocation scenarios using realistic parameters from actual vendors. We quantify trade-offs related to accuracy, performance and cost of edge/cloud bandwidth and processing resources. Our results indicate that among the many parameters of interest, the processing costs seem to play the most important role for the allocation decisions. Finally, we explore interesting interactions between target accuracy, monetary cost and delay.

关键词： cloud and edge computing distributed computing distributed machine learning inference training resource allocation

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