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Latency-Constrained Task Distribution in Multi-Access Edge computing Systems 11

Latency-Constrained Task Distribution in Multi-Access Edge C...

引用

11th IEEE international conference on Cloud networking (CloudNet)

作者： Ricardo, Guilherme Iecker Benhamiche, Amal Perrot, Nancy Carlinet, Yannick Orange Labs Chatillon France

ISBN: (数字)9781665486279

ISBN: (纸本)9781665486279

Multi-Access Edge computing (MEC) paradigm has been widely studied as a potential solution to cope with the challenges emerging from new generations of mobile networks. By processing applications' data closer to the users, service providers are able to offload origin servers and their underlying network infrastructure, which consequently reduces users' experienced latency. In this paper, we consider internet-based applications with strict latency tolerance which are primarily enabled by the MEC architecture, e.g., Virtual Reality (VR) control systems, cloud gaming, and smart healthcare. Moreover, nodes at the edge may host application-related tasks as well as assist in their provision. We introduce the Task Distribution (TD) problem, where the objective is to maximize the overall Quality of Service (QoS) while ensuring that tasks' latency requirements are satisfied. the TD problem is modeled as an Integer Programming problem, taking into account three components: (i) tasks' priority assignment, (ii) placement and (iii) routing through the MEC network. We propose to approach the TD problem through an approximation scheme, called Shortest-Flow Approximation (SFA), which considers pre-determined network flows selected according to a Betweenness-based criterion. In our experiments, we first extract useful insights on the TD problem that help characterize its solution. then, we evaluate SFA's performance empirically across different experimental settings, showing numerical results confirming its proximity to the optimal solution.

关键词： Edge computing Resource Allocation Quality of Service Operations Research Combinatorial Optimization

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Exploring the Impact of Serverless computing on Peer To Peer Training Machine Learning 11

Exploring the Impact of Serverless Computing on Peer To Peer...

引用

11th IEEE international conference on Cloud Engineering (IC2E)

作者： Barrak, Amine Trabelsi, Ranim Jaafar, Fehmi Petrillo, Fabio Univ Quebec Chicoutimi UQAC Dept Comp Sci & Math Chicoutimi PQ Canada ETS Ecole Technol Super Dept Genie Log Montreal PQ Canada

ISBN: (纸本)9798350343946

the increasing demand for computational power in big data and machine learning has driven the development of distributed training methodologies. Among these, peer-to-peer (P2P) networks provide advantages such as enhanced scalability and fault tolerance. However, they also encounter challenges related to resource consumption, costs, and communication overhead as the number of participating peers grows. In this paper, we introduce a novel architecture that combines serverless computing with P2P networks for distributed training and present a method for efficient parallel gradient computation under resource constraints. Our findings show a significant enhancement in gradient computation time, with up to a 97.34% improvement compared to conventional P2P distributed training methods. As for costs, our examination confirmed that the serverless architecture could incur higher expenses, reaching up to 5.4 times more than instance-based architectures. It is essential to consider that these higher costs are associated with marked improvements in computation time, particularly under resource-constrained scenarios. Despite the cost-time trade-off, the serverless approach still holds promise due to its pay-as-you-go model. Utilizing dynamic resource allocation, it enables faster training times and optimized resource utilization, making it a promising candidate for a wide range of machine learning applications.

关键词： Serverless FaaS Function as a Service P2P peer-to-peer architecture distributed Training Machine Learning

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Proof of Clustering: an efficient and reliable blockchain-based clustering framework 11

Proof of Clustering: an efficient and reliable blockchain-ba...

引用

11th IEEE/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2024

作者： Khenfouci, Yamina Challal, Yacine Algiers16270 Algeria University of Doha for Science and Technology College of Computing and IT Doha Qatar

ISBN: (纸本)9798350367300

We propose a novel blockchain-based collaborative clustering framework, designed to enhance the efficiency and reliability of data clustering by decentralizing the clustering process across multiple nodes within the blockchain network. the system incorporates a consensus mechanism called Proof of Clustering to achieve the accuracy and consistency of clustering. this results thereby in ensuring confidence and reliability of the final clusters and prevents any breaches or corruption. To improve the performance of clustering process in the decentralized blockchain environment, data parallelism is employed. through extensive experimentation and performance evaluation, we demonstrate the effectiveness and efficiency of proof of clustering consensus in various scenarios. Comparative studies with traditional centralized and distributed clustering methods showcase the superiority of our proposed solution by in terms of computational speed and resource utilization. PoC improves the Silhouette Score by 5.05% and reduces the Davies-Bouldin Index by 22.93%, indicating better cluster cohesion and separation in the PoC framework. © 2024 IEEE.

关键词： Artificial Intelligence Blockchain Clustering Consensus Protocols Machine Learning Proof of Useful Work

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HPopt: A Hybrid Parallel Optimization Scheduling Approach for distributed DNN Training 11

HPopt: A Hybrid Parallel Optimization Scheduling Approach fo...

引用

11th international conference on Advanced Cloud and Big Data, CBD 2023

作者： Weng, Caishan Li, Xinyang Zhang, Jinghui Yu, Hang Fujian Branch of China Telecom Corporation Limited Fuzhou China Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350345346

Recent deep learning relies on large-scale training of Deep Neural Networks (DNNs), which can be time-consuming and computationally intensive. To improve DNN training efficiency, GPU clusters have been used to perform distributed training. However, distributed training has many limitations, including synchronization overhead in data parallelism, computational bottlenecks in model parallelism, and memory constraints in pipeline parallelism, all of which hinder training efficiency. Recently, hybrid parallel distributed training emerged as a solution. However, it doesn't optimize the size of pipeline parallelism and data parallelism to further improve training efficiency. Our paper introduces HPopt, a method that combines data-parallel and pipeline-parallel training to enhance DNN training efficiency. We've designed a heuristic algorithm to improve scheduling decisions in hybrid parallelism. Experimental results on CIFAR-10 show that HPopt is 1.5 times faster than data parallelism and 4.3 times faster than Torchgpipe. © 2023 IEEE.

关键词： Deep neural networks

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Cascading Hypergraph Convolution Networks for Multi-Behavior Sequential Recommendation 11

Cascading Hypergraph Convolution Networks for Multi-Behavior...

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11th IEEE international conference on Behavioural and Social computing, BESC 2024

作者： Lu, Dan Zhang, Hao Li, Lijie Wu, Shiqing Xu, Guandong Harbin Engineering University Harbin China University of Technology Sydney Sydney Australia The Education University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798331531904

In current research on recommendation systems, exploring multi-behavior sequence recommendation has become a crucial topic. It is well known that user interactions on online platforms (such as social media websites, news aggregation applications) involve not only singular actions like reading or clicking but also various other behaviors such as commenting, sharing, and bookmarking. these diverse interactions reflect different facets of user preferences in item interaction sequences. therefore, understanding and integrating these diverse behaviors to effectively represent user preferences personalized is essential. To better utilize temporal information, we propose a Cascading Hypergraph Convolution Networks For Multi-Behavior Sequential Recommendation (CHMSR) framework. CHMSR aims to capture the underlying behavioral preferences in user sequence interactions and predict future user behaviors through cascading neural networks. Specifically, CHMSR first independently encodes each behavior sequence to extract user interests from the complex behavior relationships in the sequences. then, it aggregates user preferences for different items through hypergraph convolution to capture global preferences. Furthermore, we employ cascading neural networks in behavior chains to capture directional feature dependencies in multi-behavior sequences, propagating messages from upstream to downstream behaviors. In this way, CHMSR can comprehensively consider user interests in multi-behavior sequences and global preferences for items, thus providing more accurate and personalized recommendations. Our experimental results demonstrate that CHMSR significantly outperforms existing recommendation methods on various datasets, validating its effectiveness and practicality © 2024 IEEE.

关键词： Social networking (online)

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A Novel Task Offloading Scheme for Robotics Applications in Information Centric Networks 11

A Novel Task Offloading Scheme for Robotics Applications in ...

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IEEE 11th IFIP international conference on Performance Evaluation and Modeling in Wireless and Wired Networks (PEMWN)

作者： Sparapano, Daniele Vista, Francesco Benedetti, Paolo Piro, Giuseppe Grieco, Luigi Alfredo Politecn Bari Dept Elect & Informat Engn Bari Italy Consorzio Nazl Interuniv Telecomunicaz Parma Italy

ISBN: (数字)9783903176508

ISBN: (纸本)9783903176508

this contribution proposes a novel task offloading scheme for robotics applications in Information-Centric Networks. the reference scenario includes heterogeneous agents connected to various wireless Network Attachment Points, asking for computational services to Multi-access Edge computing servers deployed at the network edge. It also uses Information-Centric networking facilities to implement resource announcement, resource discovery, and service provisioning. the effectiveness of the conceived solution has been tested through experimental tests in realistic scenarios.

关键词： Information Centric networking Industry 4.0 Task offloading

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User-based I/O Profiling for Leadership Scale HPC Workloads 25

User-based I/O Profiling for Leadership Scale HPC Workloads

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26th international conference on distributed computing and networking, ICDCN 2025

作者： Yazdani, Ahmad Hossein Paul, Arnab K. Karimi, Ahmad Maroof Wang, Feiyi Butt, Ali Virginia Tech BlacksburgVA United States BITS Pilani KK Birla Goa India Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798400710629

I/O constitutes a significant portion of most of the application run-time. Spawning many such applications concurrently on an HPC system leads to severe I/O contention. thus, understanding and subsequently reducing I/O contention induced by such multi-tenancy is critical for the efficient and reliable performance of the HPC system. In this study, we demonstrate that an application's performance is influenced by the command line arguments passed to the job submission. We model an application's I/O behavior based on two factors: past I/O behavior within a time window and user-configured I/O settings via command-line arguments. We conclude that I/O patterns for well-known HPC applications like E3SM and LAMMP are predictable, with an average uncertainty below 0.25 (A probability of 80%) and near zero (A probability of 100%) within a day. However, I/O pattern variance increases as the study time window lengthens. Additionally, we show that for 38 users and at least 50 applications constituting approximately 93000 job submissions, there is a high correlation between a submitted command line and the past command lines made within 1 to 10 days submitted by the user. We claim the length of this time window is unique per user. © 2025 Copyright held by the owner/author(s).

关键词： High Performance computing

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Design of distributed Ultra-High-Definition Resolution Display System 11

Design of Distributed Ultra-High-Definition Resolution Displ...

引用

11th IEEE international conference on Computer Science and Network Technology, ICCSNT 2023

作者： Huang, Qiaojie Liu, Peiqiang Fu, Qiye Xu, Yunjian School of Intelligent Engineering Guangdong Agriculture Industry Business Polytechnic Guangzhou China China United Network Communications Corporation Limited GuangDong Branch Computing Network Research and Operation Department Guangzhou China

ISBN: (纸本)9798350311594

A practical and scalable ultra-high-definition resolution display system for multi-screen display wall is constructed based on multiple distributed low-definition display nodes, and the collaborative processing mechanism between distributed display nodes is the key to the performance of the display system. In order to improve the performance of image synchronization between distributed nodes, a node collaboration mechanism based on IEEE1588 time synchronization protocol and phase-locked loop is proposed. A synchronization processing system applied to ultra-high-definition resolution displays is designed, which not only shortens the image processing time but also improves the image synchronization processing performance. the system synchronization test results show that the maximum display frame difference between splicing display units is 0.47 frame and the average system delay is 98.67ms. the provided system can effectively shorten system data processing time, solve synchronization problems of ultra-high-definition resolution displays, meet requirements of large screen display images, and improve users' experience effect. © 2023 IEEE.

关键词： Synchronization

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Evaluation of Centralized, distributed and Federated Learning for IoT Intrusion Detection Systems 11

Evaluation of Centralized, Distributed and Federated Learnin...

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11th IEEE international conference on Intelligent computing and Information Systems, ICICIS 2023

作者： Elnakib, Omar Shaaban, Eman Mahmoud, Mohamed Emara, Karim Ain Shams University Faculty of Computer and Information Sciences Department of Computer Systems Cairo Egypt Tennessee Technological University Electrical and Computer Engineering CookevilleTN United States

ISBN: (纸本)9798350322101

Introducing smart intrusion detection systems (IDSs) in the IoT system was a crucial requirement in the last decades to secure these systems due to the high sensitivity of the sensed data. Smart IDS has often been built based on a machine/deep learning model trained at a centralized node in the cloud. However, the centralized learning approach violates data privacy since IoT nodes are required to share their data to the cloud to train the model. distributed and federated learning approaches were introduced to solve this issue by keeping data within the local network and letting the edge devices train the model. In this paper, a comparative study among centralized, distributed, and federated learning approaches is presented. First, a deep learning model is proposed to identify traffic behavior using CICIDS2017 dataset. the three learning approaches are then applied with the proposed model to evaluate the effect of each approach on the model accuracy. In the centralized learning approach, an F-Score of 98% can be achieved. Moreover, the dataset is split over ten simulated nodes with various data distribution and entropies to evaluate the distributed and federated learning approaches. In distributed learning, the F-Score ranges between 63% to 93% with an average of 78% over the ten nodes. In the federated learning, the F-Score ranges between 73% to 98% with an average of 89% over the ten nodes. this result confirms that IDSs based on federated learning are a promising solution for both accuracy and preserving data privacy. © 2023 IEEE.

关键词： Intrusion detection

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PhD Forum Abstract: Block-DDT: A Blockchain-Empowered IoT Device Data Trading Scheme in Cloud computing 11

PhD Forum Abstract: Block-DDT: A Blockchain-Empowered IoT De...

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11th ACM international conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys 2024

作者： Yang, Xu Xi’an Jiaotong University Shaanxi Xi’an China

ISBN: (纸本)9798400707063

the rapid growth of IoT devices generates valuable data. However, current centralized IoT device data trading solutions pose privacy and security risks. Emerging blockchain technology aims to address these issues by decentralizing data trading, reducing the risk of single points of failure, and enhancing user control over their data. In this paper, we propose a new blockchain-empowered IoT device data trading scheme, Block-DDT, to achieve secure and efficient IoT device data trading by combining privacy computing technology in each module to protect user identity and transaction information, while protecting user data privacy and ensuring transaction availability. We first design a distributed identity management module that utilizes DID to establish anonymous associations to protect user privacy and data ownership. then we propose a matching module to automatically match owners and consumers through smart contracts. Furthermore, we customize a data transaction module that utilizes threshold cryptography primitives to support the data management committee in completing encrypted data offline transactions in a consensus form. Finally, we evaluate Block-DDT through experimental analysis to confirm its efficiency. © 2024 Copyright held by the owner/author(s).

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