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Secure Coded Computation for Efficient distributed Learning in Mobile IoT 18

Secure Coded Computation for Efficient Distributed Learning ...

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18th Annual IEEE international conference on Sensing, Communication, and networking (SECON)

作者： Yang, Yilin D'Oliveira, Rafael G. L. El Rouayheb, Salim Yang, Xin Seferoglu, Hulya Chen, Yingying Rutgers State Univ New Brunswick NJ 08854 USA MIT Cambridge MA 02139 USA Univ Illinois Chicago IL USA

ISBN: (纸本)9781665441087

distributed computation plays an essential role in cloud and edge computing. Data such as images, audio, and text can be represented as matrices to facilitate efficient computation, especially in the domains of distributed machine learning, computer vision, and signal processing. Many coded computation algorithms have been proposed for big data applications to securely partition and distribute matrices to parallel worker devices. However, these proposals have yet to he adapted for mobile platforms beyond theoretical means. Mobile IoT networks can greatly benefit from secure distributed computing, however, commercial devices such as smartphones and tablets are much more limited in resources compared to platforms in data centers, requiring special design considerations. We investigate existing distribution schemes from an operational complexity and security viewpoint and study their perlbrmance in several mobile IoT networks, identifying performance bottlenecks in regards to communication and computation costs. From our findings, we propose new, scalable algorithms optimized to handle the unique constraints of mobile loT. Extensive evaluations of our proposals on publicly available image classification datasets show how distributed learning can be specially optimized to enhance runtime and battery performance on mobile loT by over 10x.

关键词： distributed computing coded computations edge device mobile IoT

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Demo: Application Monitoring as a Network Service 41

Demo: Application Monitoring as a Network Service

引用

41st IEEE international conference on distributed computing Systems (ICDCS)

作者： Elsaadawy, Mona Fesselier, Laetitia Kemme, Bettina McGill Univ Sch Comp Sci Montreal PQ Canada

ISBN: (纸本)9781665445139

The recent rise of cloud applications, representing large complex modern distributed services, has made performance monitoring a major issue and a critical process for both cloud providers and cloud customers. Many different monitoring techniques are used such as tracking resource consumption, performing application-specific measures or analyzing message exchanges. Typically the collected data is logged at the host on which the application is deployed, then either analyzed locally or forwarded to a remote analysis host. In contrast, this demonstration paper presents a Monitoring as a Service prototype that uses the advances in Software Defined networking (SDN) to move some of the logging functionality into the network. The core of our MaaS is implemented as a virtual network function where agents are co-located with software switches in order to extract performance metrics from the message flows between components in a non-intrusive manner and send the calculated measures to the clients for visualization in near real-time. The MaaS has a lot of flexibility in how it is deployed and does not require to instrument software or platforms. In our demo we show the tool in action demonstrating how users can choose to monitor different service types and performance metrics in a user-friendly manner.

关键词： Application performance monitoring Software defined networking Network function virtualization

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LIDC: A Location Independent Multi-Cluster computing Framework for Data Intensive Science

LIDC: A Location Independent Multi-Cluster Computing Framewo...

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High Performance computing, networking, Storage and Analysis, SC-W: Workshops of the international conference for

作者： Sankalpa Timilsina Susmit Shannigrahi Computer Science Department Tennessee Tech Cookeville TN

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Scientific communities are increasingly using geographically distributed computing platforms. The current methods of compute placement predominantly use logically centralized controllers such as Kubernetes (K8s) to match tasks to available resources. However, this centralized approach is unsuitable in multi-organizational collaborations. Furthermore, workflows often need to use manual configurations tailored for a single platform and cannot adapt to dynamic changes across *** work introduces a decentralized control plane for placing computations on geographically dispersed compute clusters using semantic names. We assign semantic names to computations to match requests with named Kubernetes (K8s) service endpoints. We show that this approach provides multiple benefits. First, it allows placement of computational jobs to be independent of location, enabling any cluster with sufficient resources to execute the computation. Second, it facilitates dynamic compute placement without requiring prior knowledge of cluster locations or predefined configurations.

关键词： High performance computing conferences Semantics Decentralized control Collaboration Manuals Complexity theory distributed computing

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Chorus: More Efficient Machine Learning on Serverless Platform 35th

Chorus: More Efficient Machine Learning on Serverless Platfo...

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35th international conference on Database and Expert Systems Applications (DEXA)

作者： Yang, Guang Liu, Jie Wang, Shuai Qu, Muzi Ye, Dan Zhong, Hua Chinese Acad Sci Inst Software Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Syst Software Inst Software Beijing 100190 Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Nanjing 211135 Peoples R China

ISBN: (纸本)9783031683084;9783031683091

Executing machine learning jobs on serverless platform could gain higher performance in a simplified manner. But current solutions have not fully utilized the flexibility of serverless computing. The widely used Bulk Synchronous Parallel (BSP) mode has significant resource waste, the parameter server nodes suffer bottleneck pressure from both networking and performance aspects. This paper presents Chorus, a machine learning framework on serverless platform based on the parameter server architecture. In Chorus we propose the Lambda Synchronous Parallel (LSP) model to coordinate Lambda workers. It simultaneously utilizes functions with different resource level to collaborate and dynamically adjusts resource allocation to maintain balance of model training. To alleviate the bottleneck pressure on the parameter server, we build a buffer system in memory database to exchange gradient data between the parameter server and workers. We set up multiple buffer slots in the buffer system to alleviate network pressure and design a buffer merging strategy to disperse computational pressure of parameter server among Lambda workers. In the experiments on different ML algorithms with different synchronous parallel models, Chorus shows outstanding performance improvements and budget-saving capacities.

关键词： Serverless Machine Learning distributed computing

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Network Resilience and Secure Confidentiality in Industrial-IoT through Software-Defined networking

Network Resilience and Secure Confidentiality in Industrial-...

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international conference on computing and networking Technology (ICCNT)

作者： Anarghya Singh Rakesh Salam Daya Sagar Gupta School of Computer Science Engineering and Technology Bennett University Greater Noida Uttar Pradesh Department of Mathematics Indian Institute of Technology Patna Bihta Bihar

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The ever-expanding Industrial Internet of Things (IIoT) demands highly resilient and secure network architectures. While the execution of Software-Defined networking (SDN) tackles the issue of heterogeneity in IIoT networks by the provision of centralized control and programmability, its scalability is exacerbated in large-scale IIoT deployments. This paper highlights the vitality of a distributed control plane architecture for SDN to overcome the scalability issues, thereby bolstering its efficiency. Furthermore, we propose a (t, n) threshold paradigm which ascertains that only authorized entities can access private data. The proposed protocol promotes reliability and data privacy, paving the way for secure operation within convoluted IIoT networks.

关键词： Performance evaluation Protocols Smart cities Scalability Prevention and mitigation Decentralized control Reliability Software defined networking Industrial Internet of Things Resilience

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A Solution for conference Room Cluster Audio System Based on distributed Architecture

A Solution for Conference Room Cluster Audio System Based on...

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ACIS international conference on Software Engineering, Artificial Intelligence, networking, and Parallel/distributed computing (SNPD)

作者： Xiaoyi Xu Xin Zhang Zenglai Wei School of Music and Recording Arts Communication University of China Beijing China School of Computer and Cyber Sciences Communication University of China Beijing China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

With the rapid development of information technology, people have put forward higher requirements for the audio-visual experience and usage functions of conference spaces. The traditional, single-function conference room configuration can no longer adapt to the diverse needs of modern work and interactive activities. How to achieve efficient management and control, seamless interconnection, and resource sharing of audio systems across spaces, while ensuring the acoustic characteristics and flexibility of each independent space, has become a core issue that needs to be urgently resolved in building a multi-hall, multifunctional conference room cluster. Based on the audio system project of the Academic Center of the Communication University of China, this paper proposes a solution for a conference room cluster audio system based on a distributed architecture. This solution not only overcomes the limitations of traditional systems in terms of scalability, collaborative work, and resource scheduling but also promotes lossless transmission, real-time processing, and adaptive configuration of audio signals. Thus, it ensures the consistency and high quality of the audio experience within the entire cluster, providing practical guidance for the design and optimization of future conference room audio systems.

关键词： Technological innovation Federated learning Audio systems Scalability Computer architecture Aerospace electronics Propagation losses

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Proceedings of EduHPC 2020: Workshop on Education for High Performance computing, Held in conjunction with SC 2020: The international conference for High Performance computing, networking, Storage and Analysis

Proceedings of EduHPC 2020: Workshop on Education for High P...

引用

2020 IEEE/ACM Workshop on Education for High Performance computing, EduHPC 2020

ISBN: (纸本)9781665422963

The proceedings contain 8 papers. The topics discussed include: applying parallel and distributed computing curriculum to cyber security courses;peachy parallel assignments (EduHPC 2020);lightning talks of EduHPC 2020;extending *** as an online self-learning platform for compiler development;teaching software sustainability for high performance computing at ATPESC;towards generic parallel programming in computer science education with Kokkos;and trying to do it all in a single course: a surprisingly good idea.

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Research on Fine-grained Security Sharing Model of Medical Information Based on Blockchain 29

Research on Fine-grained Security Sharing Model of Medical I...

引用

29th IEEE international conference on Parallel and distributed Systems, ICPADS 2023

作者： Cui, Bo Chen, Zeyu Inner Mongolia University Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer Science Hohhot China

ISBN: (纸本)9798350330717

In light of the vulnerabilities in traditional medical information systems, including the susceptibility to malicious tampering of patients' medical records, opaque access decisions, and challenges in data sharing, this paper introduces a fine-grained security sharing model for medical information based on blockchain. Firstly, it leverages blockchain technology in conjunction with attribute-based access control to automate multi-authority fine-grained access control through smart contracts. Secondly, it categorizes sensitive attributes of different users and electronic medical records into distinct levels, implementing personalized privacy protection within the transaction workflow using local differential privacy policies and varying privacy budgets. Finally, a prototype system, built upon a consortium blockchain, was developed and tested using a dataset of 100,000 real-world electronic medical records. Experimental results demonstrate the model's capability to facilitate stable and reliable transactions between electronic medical record publishers and requestors while ensuring privacy protection. © 2023 IEEE.

关键词： blockchain data sharing differential privacy dynamic access control

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Deep Reinforcement Learning Based Resource Allocation for Heterogeneous Networks 17

Deep Reinforcement Learning Based Resource Allocation for He...

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17th international conference on Wireless and Mobile computing, networking and Communications (WiMob)

作者： Yang, Helin Zhao, Jun Lam, Kwok-Yan Garg, Sahil Wu, Qingqing Xiong, Zehui Nanyang Technol Univ Strateg Ctr Res Privacy Preserving Technol & Syst Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ Quebec Ecole Technol Super Elect Engn Dept Montreal PQ Canada Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Singapore Univ Technol & Design Pillar Informat Syst Technol & Design Singapore Singapore

ISBN: (纸本)9781665428545

This paper investigates the problem of distributed resource management (i.e., joint device association, spectrum allocation, and power allocation) in two-tier heterogeneous networks without any central controller. Considering the fact that the network is highly complex with large state and action spaces, a multi-agent dueling deep-Q network-based algorithm combined with distributed coordinated learning is proposed to effectively learn the optimized intelligent resource management policy, where the algorithm adopts dueling deep network to learn the action-value distribution by estimating both the state-value and action advantage functions. Under the distributed coordinated learning manner and dueling architecture, the learning algorithm can rapidly converge to the optimized policy. Simulation results demonstrate that the proposed distributed coordinated learning algorithm outperforms other existing learning algorithms in terms of learning efficiency, network data rate, and QoS satisfaction probability.

关键词： Heterogeneous wireless networks distributed resource management dueling deep reinforcement learning

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Introduction to Parallel and distributed Programming using N-Body Simulations

Introduction to Parallel and Distributed Programming using N...

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High Performance computing, networking, Storage and Analysis, SC-W: Workshops of the international conference for

作者： Alexander Van Craen Marcel Breyer Dirk Pflüger IPVS SC University of Stuttgart Stuttgart Germany

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

This paper describes how we use n-body simulations as an interesting and visually compelling way to teach efficient, parallel, and distributed programming. Our first course focuses on bachelor students introducing them to algorithmic complexities and their implications in real-world problems and state-of-the-art tools like Git, remote development, or C++, by simulating the collision of two galaxies. This project teaches the mapping of mathematical functions into code, efficient implementations, and the pitfalls around complexity and scaling. Our second course targets master students introducing them to intra- and inter-node parallelization. Here, the students simulate our solar system using OpenMP and MPI. The master students further deepen their knowledge of parallelization and scientific computing by choosing custom projects like simulating water molecules or implementing an interactive live visualization using *** courses are structured such that they can easily be adapted by other instructors. All our material is publicly available at https://***/orgs/SCTeaching-NBody.

关键词： Visualization Codes Scientific computing Linux High performance computing C++ languages Programming Complexity theory Solar system Videos

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