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Processing Power-based Task Scheduling Technique for Cloud-Fog Environment

Processing Power-based Task Scheduling Technique for Cloud-F...

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IEEE conference on Industrial Electronics and Applications (ICIEA)

作者： Norbu Z. Lepcha Malka N. Halgamuge Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Systems and Business Analytics RMIT University Melbourne Australia

ISBN: (纸本)9781665409858

Internet of Things (IoT) applications have emerged as a critical component in improving people’s quality of life. The increasing volume of data generated by IoT devices, on the other hand, places a strain on the resources of traditional cloud data centres. This makes cloud data centres unable to meet the demands of IoT applications. Due to resource constraints and the massive connectivity of IoT devices, efficient task (incoming request) allocation is critical for service providers. We create a novel processing power-based task scheduling technique that employs a broker manager to distribute tasks and the process strategy across multiple devices. This is important in cloud technology because it enables organisations to effectively scale their processes. We use multithreading to parallelize processes and evaluate the performance of the proposed model using existing task allocation algorithms (i) round-robin, (ii) weighted round-robin, (iii) random allocation, (iv) genetic algorithm, and (v) dynamic algorithm. Our results show that when there are few tasks and one cluster has more processing capacity than the others, a weighted round-robin algorithm works well. The effectiveness of allocating based on weight shows no improvement in task completion time as task size increases. Using our proposed model, we show that the dynamic algorithm outperforms all other algorithms for task scheduling by 1.37 times for all task sizes.

关键词： Performance evaluation Cloud computing Data centers Job shop scheduling Heuristic algorithms Clustering algorithms Dynamic scheduling

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T2C: A Multi-User System for Deploying DNNs in a Thing-to-Cloud Continuum

T2C: A Multi-User System for Deploying DNNs in a Thing-to-Cl...

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International conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Chia-Ying Hsieh Praveen Venkateswaran Nalini Venkatasubramanian Cheng-Hsin Hsu National Tsing Hua University Taiwan IBM Research USA University of California Irvine USA

The importance of IoT analytics in smart deploy-ments has resulted in an increased use of powerful Deep Neural Network (DNN) models to extract insights from the growing amount of IoT sensor data. Traditional approaches that entirely offload computation and model deployment to cloud servers have been shown to be inefficient due to network congestion and latency concerns. However, with the improved capabilities of IoT devices, it has now become possible to distribute and host DNNs across IoT devices, edge servers and the cloud. In this paper, we propose a multi-user system, called T2C, to dynamically choose, deploy, monitor and control DNN-driven IoT analytics in a thing-to-cloud continuum. T2C leverages strategies such as multi-task learning, hitchhiking, early exit, and dynamic reconfiguration, to maximize the number of served user requests while simultaneously satisfying accuracy and latency requirements. We propose a suite of deployment planning and reconfiguration algorithms to dynamically deploy and migrate DNN layers between IoT devices, edge servers, and the cloud. We implement T2C in a prototype testbed and show that our system: (i) achieves 6.8X throughput boost compared to baseline algorithms in the planning phase, and (ii) improves the satisfied ratio by up to 35% in the operation and reconfiguration phase.

关键词： Deep learning Cloud computing Runtime Heuristic algorithms Prototypes Multitasking Throughput

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IoT DDoS Attack Detection System Using Machine Learning Classification Techniques

IoT DDoS Attack Detection System Using Machine Learning Clas...

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Student conference on Research and Development (SCOReD)

作者： Aiman Saftwan Bin Sa’Idi Ameerah Muhsinah Binti Jamil Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

The Internet of Things (IoT) has experienced substantial expansion, facilitating the connection of a multitude of devices to the Internet and enhancing the level of automation in several aspects of our daily routines. Nevertheless, the expansion has also brought to light several weaknesses, most notably the occurrence of Distributed Denial of Service (DDoS) attacks. IoT devices, which frequently lack comprehensive security measures, are vulnerable to such attacks. The objective of this work is to explore the feasibility of employing machine learning (ML) classification techniques to identify DDoS attacks on IoT networks. This study focuses on three commonly employed machine learning classification techniques: (i) Decision Tree (DT), (ii) Random Forest (RF), and (iii) Naïve Bayes (NB). The models will undergo training using a dataset specifically designed for detecting and classifying DDoS attacks. The TON IoT (UNSWIoT20) dataset sourced from the School of Engineering at the University of New South Wales Canberra is included in the study as the selected dataset. Among the several datasets accessible from the source, the IoT Network dataset with a specific emphasis on DDoS attacks was deliberately selected. To determine the efficacy of the suggested solution, a comprehensive assessment of the dataset is conducted. The testing results are then compared and analyzed to determine the model with the utmost level of precision in detecting DDoS attacks. This paper is a valuable contribution to the subject of IoT security by offering possible efficient and resilient approaches to address DDoS attacks targeting IoT devices and networks. The detection system, which utilizes machine learning, provides a proactive approach to protection, thereby guaranteeing the security and dependability of Internet of Things (IoT) services. Additionally, it facilitates the sustained growth of the IoT ecosystem.

关键词： Radio frequency Biological system modeling Denial-of-service attack Classification algorithms Bayes methods Internet of Things Decision trees

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Exploring the Integration of Wearable Sensor Technologies in Professional Sports for Enhanced Athletic Performance and Injury Prevention

Exploring the Integration of Wearable Sensor Technologies in...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Kilaru Aswini Deepak Patidar Uma Reddy Amandeep Nagpal Mohamed I. Habelalmateen Praveen Institute of Aeronautical Engineering Dundigal Hyderabad Department of Electronics & Communication Engineering IES College Of Technology Bhopal M. P. India Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore Lovely Professional University Phagwara India The Islamic university Najaf Iraq Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The escalating demand for wearable technologies coupled with the pressing need for environmentally conscious solutions has spurred innovative methodologies. This study delves into the symbiotic relationship between biomimetic designs and advanced deep learning, envisioning a paradigm shift in the landscape of next-generation wearables. This project meticulously integrates biomimetic design with cutting-edge deep learning. Biomimicry improves wearable technology by taking ideas from nature. Strong deep learning algorithms based on artificial neural networks provide predictive analytics and user adaptation. To explore biomimetic setups and user activities in a controlled setting, many datasets were collected. Across several success metrics, the proposed method performs better. The accuracy increases greatly, demonstrating how effectively biomimetic concepts and deep learning techniques work together to improve accuracy and reliability. As smart technology evolves, this study ensures that new technologies are cutting-edge and environmentally friendly. Biomimetic designs and deep learning have revolutionized sustainable technologies. It promotes a more compassionate and eco-friendly future.

关键词： Deep learning Training Technological innovation Accuracy Biomimetics Wearable devices Intelligent sensors

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Network-Aware Optimization of Distributed Learning for Fog computing 38

Network-Aware Optimization of Distributed Learning for Fog C...

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39th IEEE International conference on Computer Communications (IEEE INFOCOM)

作者： Tu, Yuwei Ruan, Yichen Wagle, Satyavrat Brinton, Christopher G. Joe-Wong, Carlee Purdue Univ W Lafayette IN 47907 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Zoomi Inc Wayne PA USA

ISBN: (纸本)9781728164120

Fog computing promises to enable machine learning tasks to scale to large amounts of data by distributing processing across connected devices. Two key challenges to achieving this are (i) heterogeneity in devices' compute resources and (ii) topology constraints on which devices can communicate. We are the first to address these challenges by developing a network-aware distributed learning optimization methodology where devices process data for a task locally and send their learnt parameters to a server for aggregation at certain time intervals. Unlike traditional federated learning frameworks, our method enables devices to offload their data processing tasks, with these decisions determined through a convex data transfer optimization problem that trades off costs associated with devices processing, offloading, and discarding data points. We analytically characterize the optimal data transfer solution for different fog network topologies, showing for example that the value of a device offloading is approximately linear in the range of computing costs in the network. Our subsequent experiments on both synthetic and real-world datasets we collect confirm that our algorithms are able to improve network resource utilization substantially without sacrificing the accuracy of the learned model.

关键词： federated learning offloading fog computing

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Multicore processors and GPUs: the power of parallel computing in the Cloud 2

Multicore processors and GPUs: the power of parallel computi...

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conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications ii Part of SPIE Defense + Commercial Sensing conference

作者： Bennett, Kelly W. Robertson, James US Army Res Lab Sensors & Electron Devices Directorate Adelphi MD 20783 USA Clearhaven Technol LLC Severna Pk MD 21146 USA

ISBN: (纸本)9781510636040

Sensors used in intelligence, surveillance and reconnaissance (ISR) operations and activities have the ability to generate vast amounts of data. High-volume analytical capabilities are needed to process data from multi-modal sensors to develop and test complex computational and deep learning models in support of the U.S. Army Multi-Domain Operations (MDO). The Army Research Laboratory designs, develops and tests Artificial Intelligence and Machine Learning (AI/ML) algorithms employing large repositories of in-house data. To efficiently process the data as well as design, build, train and deploy models, parallel and distributed algorithms are needed. Deep learning frameworks provide language-specific, container-based building blocks associated with deep learning neural networks applied to specific target applications. This paper discusses applications of AI/ML deep learning frameworks and Software Development Kits (SDKs) and demonstrates and compares specific multi-core processor and NVidia Graphics Processing Unit (GPU) implementations for desktop and Cloud environments. Frameworks selected for this research include PyTorch and Matlab. Amazon Web Services (AWS) SageMaker was used to launch Machine Learning instances ranging from general purpose computing to GPU instances. Detailed processes, example code, performance enhancements, best practices and lessons learned are included for publicly available acoustic and image datasets. Research results indicate parallel implementations of data preprocessing steps saved significant time but more expensive GPUs did not provide any processing time advantages for the machine learning algorithms tested.

关键词： Amazon Web Services Cloud computing AL/ML algorithms Cloud-based GPU platform Big data Disruptive computing Technologies AWS SageMaker

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Mobility-Aware Workflow Offloading and Scheduling Strategy for Mobile Edge computing 19th

Mobility-Aware Workflow Offloading and Scheduling Strategy f...

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19th International conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Xu, Jia Li, Xuejun Liu, Xiao Zhang, Chong Fan, Lingmin Gong, Lina Li, Juan Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Deakin Univ Sch Informat Technol Geelong Vic Australia Wuhan Inst Technol Comp Sci & Technol Sch Wuhan Peoples R China

ISBN: (纸本)9783030389611;9783030389604

Currently, Mobile Edge computing (MEC) is widely used in different smart application scenarios such as smart health, smart traffic and smart home. However, smart end devices are usually constrained in battery and computing power, and hence how to optimize the energy consumption of end devices with intelligent task offloading and scheduling strategies under constraints such as deadlines is a critical yet challenging topic. Meanwhile, most existing studies do not consider the mobility of end devices during task execution but in reality end devices may need to be constantly moving in a MEC environment. In this paper, motivated by a patient health monitoring scenario, we propose a Mobility-Aware Workflow Offloading and Scheduling Strategy (MAWOSS) for MEC which provides a holistic approach that covers the workflow task offloading strategy, the workflow task scheduling algorithm and the workflow task migration strategy. Comprehensive experimental results show that compared with others, MAWOSS is able to achieve the optimal fitness with lower energy consumption and smaller workflow makespan under the deadlines.

关键词： Mobile Edge computing Mobility Workflow Task offloading Task scheduling

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A Dynamic Partitioning Framework for Edge-Assisted Cloud computing 20th

A Dynamic Partitioning Framework for Edge-Assisted Cloud Com...

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20th International conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Cao, Zhengjia Xiao, Bowen Duan, Haihan Yang, Lei Cai, Wei Chinese Univ Hong Kong Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China South China Univ Technol Guangzhou Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Edge computing is commonly adapted to reduce network delay and fluctuation in the traditional cloud-terminal architecture. Many research teams have been dedicated to algorithms and optimizations in the cloud-edge data cache and computation offloading schemes, while there is a blank in practical implementation to facilitate such a paradigm. In this work, we proposed a component-based framework that facilitates dynamic partitioning of a software program. We have designed and implemented the very first test-bed for further optimizing the components distribution strategy among cloud, edge, and terminal devices. Experiments have revealed the characteristics of components execution in the proposed architecture, showing that the system can improve computing performance under the real-world unstable network environments.

关键词： Cloud computing Edge computing Software decomposition Offloading Distributed system

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Two-Step FORCE Learning Algorithm for Fast Convergence in Reservoir computing 29th

Two-Step FORCE Learning Algorithm for Fast Convergence in Re...

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29th International conference on Artificial Neural Networks (ICANN)

作者： Tamura, Hiroto Tanaka, Gouhei Univ Tokyo Grad Sch Engn Tokyo 1138656 Japan Univ Tokyo Int Res Ctr Neurointelligence Tokyo 1130033 Japan

ISBN: (纸本)9783030616168;9783030616151

Reservoir computing devices are promising as energy-efficient machine learning hardware for real-time information processing. However, some online algorithms for reservoir computing are not simple enough for hardware implementation. In this study, we focus on the first order reduced and controlled error (FORCE) algorithm for online learning with reservoir computing models. We propose a two-step FORCE algorithm by simplifying the operations in the FORCE algorithm, which can reduce necessary memories. We analytically and numerically show that the proposed algorithm can converge faster than the original FORCE algorithm.

关键词： Reservoir computing FORCE learning Edge computing Nonlinear time series generation

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Edge-Assisted Federated Learning: An Empirical Study from Software Decomposition Perspective 20th

Edge-Assisted Federated Learning: An Empirical Study from So...

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20th International conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Shi, Yimin Duan, Haihan Chi, Yuanfang Gai, Keke Cai, Wei Chinese Univ Hong Kong Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China Univ British Columbia Vancouver BC Canada Beijing Insitute Technol Beijing Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Federated learning is considered to be a privacy-preserving collaborative machine learning training method. However, due to the general limitation of the computing ability of the terminal device, the training efficiency becomes an issue when training some complex deep neural network models. On the other hand, edges, the nearby stationary devices with higher computational capacity, might serve as a help. This paper presents the design of a component-based federated learning framework, which facilitates the offloading of training layers to nearby edge devices while preserving the users' privacy. We conduct an empirical study on a classic convolutional neural network to validate our framework. Experiments show that this method can effectively shorten the time cost for mobile terminals to perform local training in the federated learning process.

关键词： Federated learning Deep learning Mobile edge computing Program decomposition Distributed computing

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