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Microservices-based IoT application placement within heterogeneous and resource constrained fog computing environments 19

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Microservices-based IoT application placement within heterog...

12th IEEE/ACM International Conference on Utility and cloud computing, UCC 2019

作者： Pallewatta, Samodha Kostakos, Vassilis Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Australia

ISBN: (纸本)9781450368940

Fog computing paradigm has created innovation opportunities within Internet of Things (IoT) domain by extending cloud services to the edge of the network. Due to the distributed, heterogeneous and resource constrained nature of the Fog computing nodes, Fog applications need to be developed as a collection of interdependent, lightweight modules. Since this concept aligns with the goals of microservices architecture, efficient placement of microservices-based IoT applications within Fog environments has the potential to fully leverage capabilities of Fog devices. In this paper, we propose a decentralized microservices-based IoT application placement policy for heterogeneous and resource constrained Fog environments. The proposed policy utilizes the independently deployable and scalable nature of microservices to place them as close as possible to the data source to minimize latency and network usage. Moreover, it aims to handle service discovery and load balancing related challenges of the microservices architecture. We implement and evaluate our policy using iFogSim simulated Fog environment. Results of the simulations show around 85% improvement in latency and network usage for the proposed microservice placement policy when compared with cloud-only placement approach and around 40% improvement over an alternative Fog application placement method known as Edge-ward placement policy. Moreover, the decentralized placement approach proposed in this paper demonstrates significant reduction in microservice placement delay over centralized placement. © 2019 Association for computing Machinery.

关键词： Internet of things

Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol

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arXiv 2023年

作者： Wang, Zhaorui Liu, Ya-Feng Wang, Ziyue Liu, Liang Pan, Haoyuan Cui, Shuguang The Future Network of Intelligence Institute The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices K. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of K is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for K and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of K are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of K and the design of the ADC quantizer;2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol. © 2023, CC BY.

关键词： Stochastic systems

A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained computing

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arXiv 2023年

作者： Moore, Ervin Imteaj, Ahmed Rezapour, Shabnam Hadi Amini, M. Florida International University MiamiFL33199 United States Knight Foundation School of Computing and Information Sciences Florida International University MiamiFL33199 United States Laboratory Southern Illinois University Carbondale United States Smart Decision-Making for Network-Centric Systems Florida International University Florida United States

Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users’ sensitive data, the performance of the FL process can be threatened and reached a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of FL process, the integration of blockchain for FL environments has drawn prolific attention from the people of academia and industry. Blockchain has the potential to prevent security and privacy threats with its decentralization, immutability, consensus, and transparency characteristic. However, if the blockchain mechanism requires costly computational resources, then the resource-constrained FL clients cannot be involved in the training. Considering that, this survey focuses on reviewing the challenges, solutions, and future directions for the successful deployment of blockchain in resource-constrained FL environments. We comprehensively review variant blockchain mechanisms that are suitable for FL process and discuss their trade-offs for a limited resource budget. Further, we extensively analyze the cyber threats that could be observed in a resource-constrained FL environment, and how blockchain can play a key role to block those cyber attacks. To this end, we highlight some potential solutions towards the coupling of blockchain and federated learning that can offer high levels of reliability, data privacy, and distributed computing performance. © 2023, CC BY.

关键词： Digital storage

Edge affinity-based management of applications in fog computing environments 19

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Edge affinity-based management of applications in fog comput...

12th IEEE/ACM International Conference on Utility and cloud computing, UCC 2019

作者： Mahmud, Redowan Ramamohanarao, Kotagiri Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Australia

ISBN: (纸本)9781450368940

Fog computing overcomes the limitations of executing Internet of Things (IoT) applications in remote cloud datacentres by extending the computation facilities closer to data sources. Since most of the Fog nodes are resource constrained, accommodation of every IoT application within Fog environments is very challenging. Hence, we need to efficiently identify which set of applications should be deployed in Fog. It becomes even more complicated when the application characteristics in terms of urgency, size and flow of inputs are considered simultaneously. The necessity of time-optimized execution further intensifies the application management problem. In this work, we propose a policy for Fog environments that distributes application management tasks across the gateway and the infrastructure level. It classifies and places applications according to their Edge affinity. Edge affinity of an application denotes the relative intensity of different attributes coherent with its characteristics such as user-defined deadline, amount of data per input and sensing frequency of IoT devices, which are required to be addressed within Fog environments to meet its Quality of Service (QoS). The proposed policy also minimizes the service delivery time of applications in Fog infrastructure. Its performance is compared with existing application management policies in both iFogSimsimulated and FogBus-based real environments. The experiment results show that our policy outperforms others in combined QoS enhancement, network relaxation and resource utilization. © 2019 Copyright held by the owner/author(s).

关键词： Internet of things

Workflow-as-a-service cloud platform and deployment of bioinformatics workflow applications

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arXiv 2020年

作者： Hilman, Muhammad H. Rodriguez, Maria A. Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Australia

Workflow management systems (WMS) support the composition and deployment of workflow-oriented applications in distributed computing environments. They hide the complexity of managing large-scale applications, which includes the controlling data pipelining between tasks, ensuring the application's execution, and orchestrating the distributed computational resources to get a reasonable processing time. With the increasing trends of scientific workflow adoption, the demand to deploy them using a third-party service begins to increase. Workflow-as-a-service (WaaS) is a term representing the platform that serves the users who require to deploy their workflow applications on third-party cloud-managed services. This concept drives the existing WMS technology to evolve towards the development of the WaaS cloud platform. Based on this requirement, we extend cloudBus WMS functionality to handle the workload of multiple workflows and develop the WaaS cloud platform prototype. We implemented the Elastic Budget-constrained resource Provisioning and Scheduling algorithm for Multiple workflows (EBPSM) algorithm that is capable of scheduling multiple workflows and evaluated the platform using two bioinformatics workflows. Our experimental results show that the platform is capable of efficiently handling multiple workflows execution and gaining its purpose to minimize the makespan while meeting the budget. Copyright © 2020, The Authors. All rights reserved.

关键词： Scheduling algorithms

A Survey of Fault Tolerance in cloud computing

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A Survey of Fault Tolerance in Cloud Computing

systems, Advanced Technologies and Knowledge (ISSATK), International Symposium of

作者： Om Kolthoum Elhajjej Nouha Alyaoui Samia Dardouri Karim Chabir Research Laboratory Modeling Aanalysis Control of System (MACS) LR16ES22 National engineering school of Gabes (ENIG) University of Gabes Tunisia Computer Science and Telecommunications Department Laboratory of Modeling Analysis and Control of Systems (MACS) Higher Institute of Applied Sciences and Technology Zarroug Gafsa Gafsa University Tunisia Department of Computer Science College of Computing and Information Technology Shaqra University Saudi Arabia InnoV’COM Laboratory-Sup’Com University of Carthage Tunisia

ISBN: (数字)9798331540173

ISBN: (纸本)9798331540180

This paper is designed to provide a comprehensive overview of the latest developments in fault tolerance methods for cloud computing. Maintaining high availability and reliability of cloud environments requires fault tolerance. This paper explores fault tolerance in the context of cloud computing and discusses recent challenges and innovations in the field. Moreover, it examines the ongoing research efforts to improve fault-tolerance architectures. At the end of the paper, the paper presents system-level metrics that are relevant to fault tolerance.

关键词： Measurement Surveys cloud computing Fault tolerance Technological innovation Scalability Fault tolerant systems Computer architecture Software Software reliability

Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Imran Ahmed Misbah Ahmad Muftooh Ur Rehman Siddiqi Abdellah Chehri Gwangill Jeon School of Computing and Information Science Anglia Ruskin University Cambridge U.K. Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol U.K. Department of Animal and Agriculture Hartpury University Gloucester U.K. College of Engineering and Physical Sciences Aston University Birmingham U.K. Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON Canada Department of Embedded Systems Engineering Incheon National University Incheon South Korea

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology.

关键词： Computational modeling Object detection Edge computing Image edge detection Artificial intelligence Computer architecture Data models Accuracy Servers Safety

Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospects

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IEEE Access 2025年 13卷 95753-95796页

作者： Ahmed Fime, Awal Mahmud, Saifuddin Das, Arpita Islam, Md. Sunzidul Kim, Jong-Hoon Southern Illinois University School of Computing CarbondaleIL62901 United States Bradley University Department of Computer Science and Information Systems PeoriaIL61625 United States The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX76019 United States Khulna University of Engineering and Technology Department of Computer Science and Engineering Khulna9203 Bangladesh Kent State University Advanced Telerobotics Research Laboratory Department of Computer Science KentOH44240 United States

Automatic scene generation is an essential area of research with applications in robotics, recreation, visual representation, training and simulation, education, and more. This survey provides a comprehensive review of the current state-of-the-arts in automatic scene generation, focusing on techniques that leverage machine learning, deep learning, embedded systems, and natural language processing (NLP). We categorize the models into four main types: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion Models. Each category is explored in detail, discussing various sub-models and their contributions to the field. We also review the most commonly used datasets, such as COCO-Stuff, Visual Genome, and MS-COCO, which are critical for training and evaluating these models. Methodologies for scene generation are examined, including image-to-3D conversion, text-to-3D generation, graph-based methods, and interactive scene generation. Evaluation metrics such as Fréchet Inception Distance (FID), Kullback-Leibler (KL) Divergence, Inception Score (IS), Intersection over Union (IoU), and Mean Average Precision (mAP) are discussed in the context of their use in assessing model performance. The survey identifies key challenges and limitations in the field, such as maintaining realism, handling complex scenes with multiple objects, and ensuring consistency in object relationships and spatial arrangements. By summarizing recent advances and pinpointing areas for improvement, this survey aims to provide a valuable resource for researchers and practitioners working on automatic scene generation. © 2013 IEEE.

关键词： Deep learning

Including frameworks of public health ethics in computational modelling of infectious disease interventions

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arXiv 2025年

作者： Zarebski, Alexander E. Tellioglu, Nefel Stockdale, Jessica E. Spencer, Julie A. Khuda Bukhsh, Wasiur R. Miller, Joel C. Zachreson, Cameron School of Mathematics & Statistics University of Melbourne Australia Pandemic Sciences Institute University of Oxford United Kingdom MRC Biostatistics Unit University of Cambridge United Kingdom Department of Infectious Diseases University of Melbourne The Peter Doherty Institute for Infection and Immunity MelbourneVIC Australia Department of Mathematics Simon Fraser University Burnaby Canada Information Systems and Modeling Group Los Alamos National Laboratory Los AlamosNM United States School of Mathematical Sciences University of Nottingham United Kingdom Department of Mathematical and Physical Sciences La Trobe University Australia Australian Centre for AI in Medical Innovation Australia School of Computing and Information Systems University of Melbourne ParkvilleVIC Australia

Decisions on public health interventions to control infectious disease are often informed by computational models. Interpreting the predicted outcomes of a public health decision requires not only high-quality modelling, but also an ethical framework for assessing the benefits and harms associated with different options. The design and specification of ethical frameworks matured independently of computational modelling, so many values recognised as important for ethical decision-making are missing from computational models. We demonstrate a proof-of-concept approach to incorporate multiple public health values into the evaluation of a simple computational model for vaccination against a pathogen such as SARS-CoV-2. By examining a bounded space of alternative prioritisations of values (outcome equity and aggregate benefit) we identify value trade-offs, where the outcomes of optimal strategies differ depending on the ethical framework. This work demonstrates an approach to incorporating diverse values into decision criteria used to evaluate outcomes of models of infectious disease interventions. Copyright © 2025, The Authors. All rights reserved.

关键词： Computational cost