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international conference on 6G Communications networking and Signal Processing, SGCNSP 2023

International Conference on 6G Communications Networking and...

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international conference on 6G Communications networking and Signal Processing, SGCNSP 2023

ISBN: (纸本)9789819778751

The proceedings contain 34 papers. The special focus in this conference is on 6G Communications networking and Signal Processing. The topics include: Secure Clustering and Routing Based on Modified Whale Optimization Algorithm in MANET;improved Whale Optimization Algorithm Based Energy Efficient Secure Routing in Mobile Wireless Sensor Networks;resource Allocation in Cloud computing Using Quasi-Opposition Learning Based Aquila Optimizer;the Quasi-Oppositional-Based Learning Aquila Optimization Routing Protocol in Energy-Aware Wireless Communication;exponential-Weighted Ant Lion Optimization Based Clustering Algorithm for Wireless Body Sensor Networks;Optimized Routing Strategy in Mobile Sink Based WSN Using Multi-objective Osprey Optimization Algorithm;the Coverage Optimization in Wireless Sensor Networks Using Improved Artificial Rabbit Optimization;improved Moth Flame Optimization Based Cluster Head Selection and Data Aggregation Using Machine Learning Approach;Multi-objective Deep Reinforcement Learning Approach for Multiple -Input/Multiple-output Routing in WSN;an Effective Energy Consumption and Weighted Clustering Using Improved K-Means Clustering;adaptive Weight Based Harris Hawks Optimization for Energy-Efficient Cluster Head Selection and Routing in Wireless Sensor Networks;An Optimal Cluster Head and Secure Route Path Selection Using Multi Objective—GWO Approach in Wireless Sensor Networks;peak-to-Average Power Ratio Reduction in Orthogonal Frequency Division Multiplexing Using Improved Butterfly Optimization Algorithm;energy Efficient-Based Cluster Head Selection and Routing Protocol for Wireless Sensor Networks Using Aquila Optimizer;ensemble Artificial Neural Network and Support Vector Machine Based Parameter Evaluation in Wireless Sensor Networks;distributed Denial of Service Attack Detection in IoT Utilizing Attention Mechanism Based Long Short-Term Memory;e-commerce Payment System Empowered by Blockchain for Enhanced Security.

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Q-Learning-Based Task Allocation for High-Speed distributed computing in Heterogeneous Clusters

Q-Learning-Based Task Allocation for High-Speed Distributed ...

引用

international conference on Information networking

作者： Seung Geon Yang Girim Kwon Seung-Chan Lim School of ICT Robotics and Mechanical Engineering Hankyong National University Anseong South Korea School of Electrical and Computer Engineering University of Seoul Seoul South Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

This paper proposes a Q-learning-based task allocation approach for wireless coded distributed computing systems with heterogeneous worker nodes. Task allocation in such systems is challenging due to the heterogeneity in computation and communication capabilities, leading to non-identically and independently distributed processing times across nodes. By modeling the task allocation problem as a Markov decision process and applying Q-learning, the master node learns to allocate tasks effectively, adapting to node heterogeneity and minimizing the average processing time. This approach highlights the potential of reinforcement learning to optimize distributed computing in heterogeneous environments.

关键词： Wireless communication distributed processing Adaptation models Q-learning Markov decision processes Computational modeling Delays Resource management distributed computing Optimization

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SLO-Aware Task Offloading Within Collaborative Vehicle Platoons 22nd

SLO-Aware Task Offloading Within Collaborative Vehicle Plato...

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22nd international conference on Service Oriented computing

作者： Sedlak, Boris Morichetta, Andrea Wang, Yuhao Fei, Yang Wang, Liang Dustdar, Schahram Qu, Xiaobo TU Wien Distributed Syst Grp Vienna Austria Tsinghua Univ Emerging Transportat Solut Beijing Peoples R China

ISBN: (纸本)9789819608072;9789819608089

In the context of autonomous vehicles (AVs), offloading is essential for guaranteeing the execution of perception tasks, e.g., mobile mapping or object detection. While existing work on offloading focused extensively on minimizing inter-vehicle networking latency, vehicle platoons (e.g., heavy-duty transport) present numerous other objectives, such as energy efficiency or data quality. To optimize these Service Level Objectives (SLOs) during operation, this work presents a purely Vehicle-to-Vehicle approach (V2V) for collaborative services offloading within a vehicle platoon. By training and using a Bayesian Network (BN), services can proactively decide to offload whenever this promises to improve platoon-wide SLO fulfillment;therefore, vehicles estimate how both sides would be impacted by offloading a service. In particular, this considers resource heterogeneity within the platoon to avoid overloading more restricted devices. We evaluate our approach in a physical setup, where vehicles in a platoon continuously (i.e., every 500 ms) interpret the SLOs of three perception services. Our probabilistic, predictive method shows promising results in handling large AV platoons;within seconds, it detects and resolves SLO violations through offloading.

关键词： Service Level Objectives Edge computing Intelligent Transportation Microservices Offloading Bayesian Networks

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Efficient Allocation of Image Recognition and LLM Tasks on Multi-GPU System 15th

Efficient Allocation of Image Recognition and LLM Tasks on...

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15th international conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Lawenda, Marcin Samborski, Krzesimir Khloponin, Kyrylo Szustak, Lukasz Poznan Supercomputing and Networking Center Jana Pawla II 10 Poznań61-139 Poland Czestochowa University of Technology Dąbrowskiego 69 Częstochowa42-201 Poland

ISBN: (纸本)9783031857027

This work is concerned with the evaluation of the performance of parallelization of learning and tuning processes for image classification and large language models. For machine learning model in image recognition, various parallelization methods are developed based on different hardware and software scenarios: simple data parallelism, distributed data parallelism, and distributed processing. A detailed description of presented strategies is given, highlighting the challenges and benefits of their application. Furthermore, the impact of different dataset types on the tuning process of large language models is investigated. Experiments show to what extent the task type affects the iteration time in a multi-GPU environment, offering valuable insights into the optimal data utilization strategies to improve model performance. Furthermore, this study leverages the built-in parallelization mechanisms of PyTorch that can facilitate these tasks. Furthermore, performance profiling is incorporated into the study to thoroughly evaluate the impact of memory and communication operations during the training/tuning procedure. Test scenarios are developed and tested with numerous benchmarks on the NVIDIA H100 architecture showing efficiency through selected metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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distributed Compute Offloading in Local Communications

Distributed Compute Offloading in Local Communications

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international conference on computing, networking and Communications (ICNC)

作者： Sameh Eldessoki Christian Hofmann Dimitrios Alanis Panagiotis Botsinis Tarik Tabet Apple

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Next-generation wireless networks 6G are tasked with accommodating diverse applications beyond traditional communications. This paper delves into the use-case of computation offload, a pivotal use case within the broader framework of beyond communications applications (e.g., sensing, localization, etc.). More specifically, it focuses on the local subnetwork and decentralized distributed compute use-cases, where the control of the computation offload procedure is not within a central entity. This is realized on top of the subnetwork’s architecture. In this work we propose a general architecture and procedural flow that allows for the realization of local subnetwork and decentralized distributed compute.

关键词： 6G mobile communication Location awareness Data privacy Wireless networks Authentication Computer architecture Sensors Security 3GPP Next generation networking

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Problem Specific Communications in distributed Quantum computing

Problem Specific Communications in Distributed Quantum Compu...

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Quantum Communications, networking, and computing (QCNC), international conference on

作者： Joyanta Basak Bing Wang Sanguthevar Rajasekaran School of Computing University of Connecticut USA

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

When all the qubits needed for solving a problem are not located in a single quantum computer, qubits from different quantum computers can be collectively utilized. In this case, quantum communication is needed for the multiple quantum computers to communicate with each other. Several studies address the problem of minimizing the number of quantum communications when evaluating a general quantum circuit. The solutions proposed typically involve solving some intractable problems. In this paper, we show that we can obtain much better solutions when we focus on solving specific problems (instead of seeking solutions for generic circuits). Specifically, we consider several fundamental quantum circuits and identify communication protocols that need a much smaller number of communication steps than those offered by generic solutions. Our work is in line with traditional parallel and distributed computing research where typically scientists focus on solving specific problems (such as sorting, matrix multiplication, network flow, etc.) in a parallel or distributed setting.

关键词： Quantum computing Protocols Source coding Qubit Search problems Complexity theory Quantum networks Quantum circuit Parallel algorithms Sorting

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distributed Mobile computing for Deep Learning Applications

Distributed Mobile Computing for Deep Learning Applications

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international conference on Information networking

作者： Seunghyun Lee Haesung Jo Jihyeon Yun Changhee Joo Department of Computer Science and Engineering Korea University Seoul Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

distributed computation is the widely used methodology to overcome challenges that the application covering multiple mobile devices mostly experiences, such as the high complexity of the computation and the resource limitation. By splitting the required computation and distribute the computation across the multiple devices, it can achieve lowered computation time and resource required per device and effective utilization in terms of the total resource management. This is risen as an appropriate approach to manage problems that recent applications with deep learning process have. Followed by the generalization of Internet of Things (IoT) and the development of data collecting technology, the deep learning process has to handle much larger dataset which makes it hard to be transferred through the network. This also leads to more complex computation that a single device may not be able to operate itself. In this paper, we consider the distributed computation applied in various fields, and how it is applied to distribute the deep learning process through observing researches studying about it.

关键词： Deep learning Data collection Market research Mobile handsets Complexity theory Internet of Things Resource management distributed computing Mobile computing

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Study on distributed Models Combining Method for Performance and Efficiency

Study on Distributed Models Combining Method for Performance...

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international conference on computing, networking and Communications (ICNC)

作者： Tsuchiya Takeshi Ryuichi Mochizuki Hiroo Hirose Hiroshi Ichikawa Institute for Data Science Education Tokyo International University Tokyo Japan MR Systems Nagano Japan Dept. of Applied Information Suwa University of Science Nagano Japan Otsuma Women’s University Tokyo Japan

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

The authors proposed a distributed machine learning method that generates and combines machine learning models created by group set of users in previous study. This paper discusses a method for selecting distributed models to efficiently construct machine learning models. First, a method for selecting models based on their similarity is discussed. The evaluation shows that selecting models only based on similarityis problematic. Next, to improve the performance of individual machine learning models for each set, we integrated pre-trained base learning models. However, we discover that its performance doesn’t improve considerably when compared to the typical one without pre-training.

关键词： Accuracy Computational modeling Machine learning Predictive models Data models Optimization

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distributed Edge Computation for Finding an Optimal Meeting Location

Distributed Edge Computation for Finding an Optimal Meeting ...

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international conference on computing, networking and Communications (ICNC)

作者： Matthew Chou

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

We consider the problem of finding a "fair" meeting place when S people want to get together. Specifically, we will consider the cases where a "fair" meeting place is defined to be either 1) a node on a graph that minimizes the maximum time/distance to each person or 2) a node on a graph that minimizes the sum of times/distances to each of the sources. In graph theory, these nodes are denoted as the center and centroid of a graph respectively. In this paper, we propose a novel solution for finding the center and centroid of a graph by using a multiple source alternating Dijkstra’s Algorithm. Additionally, we introduce a stopping condition that significantly saves on time complexity without compromising the accuracy of the solution. The results of this paper are a low complexity algorithm that is optimal in computing the center of S sources among N nodes. Furthermore, we show how the algorithm can be distributed between S edge devices in a wired/wireless network.

关键词： Accuracy Graph theory Computational efficiency Complexity theory Time complexity Optimization

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distributed Deep Reinforcement Learning Based Deterministic Task Offloading in End-Edge-Cloud Collaborative computing Networks 3rd

Distributed Deep Reinforcement Learning Based Deterministic ...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Yao, Yingfei Yao, Shunchun Zhou, Nan Liang, Xiaojun Yang, Chunhua Gui, Weihua South China University of Technology Guangzhou 510640 China Pengcheng Laboratory Shenzhen 518000 China Central South University Changsha 410083 China

ISBN: (纸本)9789819645022

In the industrial field, the requirements for deterministic and reliable communication cannot be satisfied by current end-edge-cloud collaborative computing (EECC) networks. Time-sensitive networking (TSN) technology not only provides bounded latency guarantees but also gives support to integrate different types of traffic on a single network. However, the development of TSN-based EECC networks remains in its early stage. In light of this, we propose an intelligent end-edge-cloud collaborative deterministic task offloading framework that combines EECC and TSN networks. Based on distributed Deep Reinforcement Learning (DDRL), a deterministic task offloading algorithm named DDRL-E2CDTO is introduced to address the joint optimization problem of TSN traffic scheduling and end-edge-cloud task offloading. This algorithm employs TSN traffic scheduling (TTS) networks and deterministic task offloading (DTO) networks to train distributed agents to make instant and efficient deterministic decisions of task offloading. Simulation experiments demonstrate that our proposed approaches can obtain significant performance achievements in acceleration of convergence speed and computation rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deterministic task offloading distributed deep reinforcement learning End-edge-cloud collaboration computing Industrial internet of things Time-sensitive network

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