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Efficient. Scalable and Robust Data Shuffle Service for distributed MapReduce computing on Cloud 24

Efficient. Scalable and Robust Data Shuffle Service for Dist...

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24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Gu, Rong Huang, Xu Dai, Haipeng Geng, Xiaoyu Chen, Xiaofei Huang, Yihua Xiao, Fu Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9798350319934

distributed Map Reduce computing frameworks, such as Hadoop, Spark, and Flink, are widely used in various domains which face big data challenges. Inside Map Reduce, Shuffle is a critical stage that bridges up the Map stage and Reduce stage through data partition and network transmission under the hood. With the rapidly growing scale of data and clusters, the all-to-all data transfer in Shuffle becomes inefficient and frequently fails, making Shuffle the bottleneck of many big data processing jobs. To improve the Shuffle efficiency, we propose a hybrid Shuffle model based on file pre-merging and partition reorganization mechanisms. To address the Shuf-fle stability issue, we design a novel data transmission model based on the replication mechanism. Finally, to support scalable Shuffle with high performance, we present a Shuffle parallelism degree tuning approach based on machine learning and searching techniques. We have implemented the proposed Shuffle models and strategies into the widely-used MapReduce-based big data platform Spark for evaluation. Experimental evaluation shows that, compared with the state-of-the-art systems, our proposed methods outperform them by 23% on average. As a practical application case, the proposed Shuffle service has been running in the real-world environment of ByteDance. Inc over one year with significant performance improvement. © 2022 ieee.

关键词： Data transfer

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distributed Trust Evaluation Mechanism of LEO Satellites for 6G Network 23

Distributed Trust Evaluation Mechanism of LEO Satellites for...

引用

23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Hui Shi, DongCong Zhou, Ruijin Liao, Dan Zhang, Ming Zhou, Yuliang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Chengdu Research Institute University of Electronic Science and Technology of China Chengdu China School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665494571

In 6G network, the LEO satellite communication is popular because of the lower development costs and lower transmission delay. However, due to the instability, openness and exposure of links between satellites, LEO satellite is vulnerable to internal attacks during data routing. Considering that trust management has a better performance in solving the internal attacks between satellite communications, this paper proposed a new distributed trust evaluation mechanism (DTEM) of LEO satellites based on D-S evidence theory. In the proposed trust evaluation mechanism, it evaluates the trust degree of satellite nodes by direct trust, indirect trust and aggregate trust. Then it detects the malicious satellite nodes for 6G networks and takes corresponding isolation measures. The simulations verifies that the proposed trust evaluation mechanism has superior performance in packet delivery rate and packet loss rate for data routing. © 2021 ieee.

关键词： 6G mobile communication Satellites Smart cities Evidence theory Low earth orbit satellites Packet loss Routing

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Optimal Trajectory Planning and Task Assignment for UAV-assisted Fog computing 24

Optimal Trajectory Planning and Task Assignment for UAV-assi...

引用

作者： Liu, Shuaijun Yin, Jiaying Zeng, Zishu Wu, Jingjin BNU-HKBU United International College Department of Statistics and Data Science Zhuhai519087 China The University of Hong Kong Li Ka Shing Faculty of Medicine Pokfulam Hong Kong School of Engineering The Hong Kong University of Science and Technology Kowloon Hong Kong Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science China

ISBN: (纸本)9798350319934

Fog computing is an emerging distributed computing model for the realization of the Internet of Things (IoT). It extends computing and caching functions to the edge of wireless networks. Unmanned Aerial Vehicles (UAVs) provide effective support for fog computing, as UAVs can not only act as a relay between mobile users and physically remote edge devices to avoid costly long-range wireless communications, but also are equipped with computing facilities that can take over certain tasks. In this paper, we aim to optimize the energy efficiency of a fog computing system assisted by a single UAV, by planning the trajectories of the UAV and assigning computing tasks to different devices including the UAV itself. We propose two algorithms based on the classical Ant Colony and Particle Swarm Optimization techniques, and solve the problem by continuous convex approximation. Different from most existing studies where the trajectories are assumed to be straight lines, we account for the effect of obstacles, such as buildings, and deliberately avoid them during the trajectory planning phase. We demonstrate, through extensive simulation experiments, that our proposed approach can achieve significantly better energy efficiency compared to existing benchmark algorithms. © 2022 ieee.

关键词： Fog computing

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The Fourth international Workshop on "Wireless sensors and Drones in Internet of Things" (Wi-DroIT 2022) Message from the Workshop Chairs

Proceedings - 18th Annual International Conference on Distri...

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Proceedings - 18th Annual international conference on distributed computing in sensor systems, dcoss 2022 2022年 XVIII页

作者： Sorbelli, Francesco Betti Saad, Walid Wang, Lu

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Message from the IoTI4 2022 Workshop Chairs

Proceedings - 18th Annual International Conference on Distri...

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Proceedings - 18th Annual international conference on distributed computing in sensor systems, dcoss 2022 2022年 XVII页

作者： Lagkas, Thomas Sarigiannidis, Panagiotis Soldatos, John International Hellenic University Greece University of Western Macedonia Greece Netcompany-Intrasoft Luxembourg

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BYEE: Batch Fully Private Scheme for Coded distributed Matrix Multiplication 24

BYEE: Batch Fully Private Scheme for Coded Distributed Matri...

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作者： Li, Guojing Wang, Jin Zou, Xiaotian Li, Lingzhi Gu, Fei School of Computer Science and Technology Soochow University Suzhou215006 China Collaborative Innovation Center of Novel Software Technology and Industrialization China

ISBN: (纸本)9798350319934

Recently, user privacy in distributed computing has received increasing attention. Matrix multiplication is one of the fundamental high-frequency operations in distributed machine learning (e.g., gradient descent, linear regression). This paper studies the batch Fully Private distributed Matrix Multiplication (FPMM) problem. In batch FPMM, the user aims to compute multiple desired matrix multiplications from two public libraries which are stored across all workers. Batch FPMM requires that colluding workers cannot obtain the indexes of all desired ma-trices in the two public libraries (i.e., privacy constraint). In this paper, we first propose the Bath fullY privatE schemE (BYEE) to encode multiple desired matrix multiplications together based on the construction of bilinear complexity. We then perform a solid theoretical analysis to prove the privacy and decodability of BYEE. Multi-aspect analysis and extensive simulation results illustrate that BYEE can significantly reduce the computation latency than state-of-the-art schemes that do not jointly encode multiple desired matrix multiplications, especially when the number of desired matrix multiplications and the number of colluding workers are large. © 2022 ieee.

关键词： Libraries

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t-path Based Reliable and Real-time Routing in Battery-Free Wireless sensor Network 23

t-path Based Reliable and Real-time Routing in Battery-Free ...

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作者： Chen, Kunyi Gao, Hong Cheng, Siyao Chen, Quan Li, Jianzhong Harbin Institute of Technology China Guangdong University of Technology China

ISBN: (纸本)9781665494571

Wireless sensor Networks are deployed to sense data from the physical world and transmit these data to the server(s). To reduce the maintenance cost of replacing batteries, Energy-Harvesting nodes are developed which can get recharged from Solar Power, Radio Frequency, and other environmental energy resources. We term the network consist of energy-harvesting nodes as Battery-Free WSNs (BF-WSNs). Environmental energy is inherently time-varying and exhibits a high degree of heterogeneity. These features bring challenges to routing design. In this paper, we focus on the reliability and real-time issues and propose an opportunistic strategy as time synchronization is hard and expensive to reach in BF-WSN. We first model BF-WSNs as a directed weighted multigraph and propose a novel definition called t-path to describe the time-varying availability feature of BF-nodes. Further, we show that the problem is NP-Hard. We present a centralized approximate algorithm and prove that the ratio bound is 2 under two conditions. This paper also presents a distributed algorithm. Comprehensive simulations have been performed to evaluate the performance of the proposed algorithms and the results show that the efficiency of our algorithms. © 2021 ieee.

关键词： Wireless sensor networks Simulation Reliability theory Approximation algorithms Routing Reliability engineering Real-time systems

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distributed Consensus Problem in Federated Learning Paradigm 23

Distributed Consensus Problem in Federated Learning Paradigm

引用

作者： Yan, Xin Qin, Yiming Hu, Xiaodong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China The University of Hong Kong The Faculty of Education Hong Kong Hong Kong

ISBN: (纸本)9781665494571

Federated learning (FL) framework facilitates more and more applications of deep learning algorithms on the existing network architectures, where the model parameters are aggregated in a centralized manner. However, some of FL participants are often inaccessible, such as in a power shortage or device dormant state. That will force us to explore the probability that the parameter aggregation is operated in an ad hoc manner, which is based on consensus computing. In this paper, we first propose a novel FL paradigm, which supports an ad hoc operation mode for FL participants. Second, a discrete-time dynamic equation and its control law are formulated to satisfy the demands from FL framework, with a quantized caching scheme designed to mask the uncertainties from asynchronous updates and measurement noises. Then, the consensus conditions and the convergence of the consensus protocol are deduced analytically, and a strategy of quantized caching to optimize the convergence speed is provided. Last, the theoretical results are validated by numerical simulations. © 2021 ieee.

关键词： Uncertainty Smart cities Delay effects Packet loss Collaborative work Particle measurements Numerical simulation

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Aware: Adaptive distributed Training with Computation, Communication and Position Awareness for Deep Learning Model 24

Aware: Adaptive Distributed Training with Computation, Commu...

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作者： Zeng, Yan Yi, Guangzheng Yin, Yuyu Wu, Jiyang Xue, Meiting Zhang, Jilin Wan, Jian Zhang, Yunquan School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing China

ISBN: (纸本)9798350319934

The accuracy of the neural networks can usually be improved by increasing the size of the dataset and the layers or operators of the network, as it has strong composability. But, it makes a challenge to train these models efficiently, due to the limited resources of a single device, such as memory, computational resource and so on. For this challenge, it has become an inevitable trend to automatically implement model parallelism across multiple devices. This paper proposes Aware, an adaptive distributed parallel training method, to search distributed parallel strategy automatically for deep learning model. It firstly proposes an operator fusion strategy with computation and communication awareness to simplify the computational graph of deep learning model. And then, it introduces position-aware graph embedding algorithm to extract the structural features of models, which can make the searched parallel strategies to transplant to other networks with similar structures. On this basis, we use reinforcement learning algorithm to search distributed parallel strategy automatically. This paper makes experiments with neural networks such as Inception V3, NMT, GNM, NasNet and ResNet, and uses CIFAR10 and PTB datasets to compare the two methods of Hierarchical and Placeto. The results show that compared with Placeto, Aware achieves up to 5% reductions in runtime. Aware achieves more than 8% reductions in runtime compared with Hierarchical. Moreover, it has better transplantation and generalization capabilities, and supports pre-training for large-scale network parallel strategy search and accelerates convergence. © 2022 ieee.

关键词： Deep learning

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A live context model for semantic reasoning in IoT applications

A live context model for semantic reasoning in IoT applicati...

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20th ieee international conference on Pervasive computing and Communications (ieee PerCom)

作者： Del Gaudio, Daniel Ariguib, Boshra Bartenbach, Arne Solakis, Georgios Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany

ISBN: (纸本)9781665416474

Nowadays, the rapid increase of IoT devices and the corresponding high volume of produced data leads to a higher complexity of data analysis and context reasoning tasks. In the IoT, monitoring the context of an application, i.e., the involved devices sensors and actuators as well as their surroundings, is of vital importance. Ontologies can be used to model such context, nevertheless, they usually do not consider live information like sensor values and monitoring data. Yet, this is necessary to capture the full context of an IoT application. In this paper, we propose a context model that includes the current state of devices, composing their static information, such as device names, and live information gathered from monitoring systems and sensors. We further propose an extendable architecture to enrich the ontology with static and live data. Our approach can be extended by integrating different data sources and extending the context model.

关键词： Internet of Things context modeling ontologies

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