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NEUKONFIG: Reducing Edge Service Downtime When Repartitioning DNNs 9

NEUKONFIG: Reducing Edge Service Downtime When Repartitionin...

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9th IEEE International conference on Cloud Engineering (IC2E)

作者： Majeed, Ayesha Abdul Kilpatrick, Peter Spence, Ivor Varghese, Blesson Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland

ISBN: (纸本)9781665449700

Deep neural networks (DNNs) may be partitioned across the edge and the cloud to improve the performance efficiency of inference. DNN partitions are determined based on operational conditions such as network speed. When operational conditions change DNNs will need to be repartitioned to maintain the overall performance. However, repartitioning using existing approaches, such as Pause and Resume, will incur a service downtime on the edge. This paper presents the NEUKONFIG framework that identifies the service downtime incurred when repartitioning DNNs and proposes approaches for reducing edge service downtime. The proposed approaches are based on `Dynamic Switching' in which, when the network speed changes and given an existing edge-cloud pipeline, a new edge-cloud pipeline is initialised with new DNN partitions. Incoming inference requests are switched to the new pipeline for processing data. Experimental studies are carried out on a lab-based testbed to demonstrate that Dynamic Switching reduces the downtime by at least an order of magnitude when compared to a baseline using Pause and Resume that has a downtime of 6 seconds. A trade-off in the edge service downtime and memory required is noted. The Dynamic Switching approach that requires the same amount of memory as the baseline reduces the edge service downtime to 0.6 seconds and to less than 1 millisecond in the best case when twice the amount of memory as the baseline is available.

关键词： Edge computing distributed DNN service downtime

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Learning distributed and fair policies for network load balancing as Markov potential game 22

Learning distributed and fair policies for network load bala...

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Proceedings of the 36th International conference on neural Information processing Systems

作者： Zhiyuan Yao Zihan Ding École Polytechnique Cisco Systems Princeton University

ISBN: (纸本)9781713871088

This paper investigates the network load balancing problem in data centers (DCs) where multiple load balancers (LBs) are deployed, using the multi-agent reinforcement learning (MARL) framework. The challenges of this problem consist of the heterogeneous processing architecture and dynamic environments, as well as limited and partial observability of each LB agent in distributed networking systems, which can largely degrade the performance of in-production load balancing algorithms in real-world setups. Centralised-training-decentralised-execution (CTDE) RL scheme has been proposed to improve MARL performance, yet it incurs - especially in distributed networking systems, which prefer distributed and plug-and-play design schemes - additional communication and management overhead among agents. We formulate the multi-agent load balancing problem as a Markov potential game, with a carefully and properly designed workload distribution fairness as the potential function. A fully distributed MARL algorithm is proposed to approximate the Nash equilibrium of the game. Experimental evaluations involve both an event-driven simulator and real-world system, where the proposed MARL load balancing algorithm shows close-to-optimal performance in simulations, and superior results over in-production LBs in the real-world system.

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Deep learning methods for malicious URL detection using embedding techniques as Logistic Regression with Lasso penalty and Random Forest

Deep learning methods for malicious URL detection using embe...

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International conference on Parallel distributed and Grid Computing (PDGC)

作者： Isha Thakur Kajal Panda Sanjeev Kumar Cyber Security Technology Division Centre for Development of Advanced Computing Mohali India

ISBN: (纸本)9781665454025

In the Internet ecosystem, URLs (Uniform Resource Locators) are widely used to propagate malicious infections through spam, spear-phishing, drive-by-download exploitation, malware embedding, etc. The blacklisting, pattern analysis, and signature-matching approaches are widely used to detect such threats. However, these techniques are very effective in detecting known types of malicious URLs but unable to detect new types of attacks launched by the malicious URLs. Moreover, traditional machine learning-based malicious URL detection is highly dependent upon manual feature engineering, which is a costly approach. , This research study proposes deep learning-based malicious URL detection with less dependent on manual feature engineering to overcome the above-mentioned challenges. The Natural Language processing (NLP) technique, i.e., Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer, along with the N-gram parameter, is used for extracting the features. Afterward, two feature selection techniques as Embedded methods are used - Logistic Regression with L1 (Lasso) penalty and Random Forest Selection. neural network models such as Deep neural networks (DNN), Long Short-Term Memory (LSTM), and Convolution neural networks (CNN) are constructed to detect malicious URLs. The experiment results demonstrate that the DNN model gives the best performance results as 96.95% accuracy, 99% precision, 100% recall, and 99% F1-score with logistic regression along with the L1 (Lasso) penalty as a feature selection method. Finally, this study compares state-of-art implemented deep learning methods for malicious URL detection.

关键词： Uniform resource locators Deep learning Biological system modeling neural networks Manuals Feature extraction Natural language processing

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Statistical Guarantees of distributed Nearest Neighbor Classification 34

Statistical Guarantees of Distributed Nearest Neighbor Class...

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34th conference on neural Information processing Systems (NeurIPS)

作者： Duan, Jiexin Qiao, Xingye Cheng, Guang Purdue Univ Dept Stat W Lafayette IN 47907 USA Binghamton Univ Dept Math Sci Binghamton NY USA

ISBN: (纸本)9781713829546

Nearest neighbor is a popular nonparametric method for classification and regression with many appealing properties. In the big data era, the sheer volume and spatial/temporal disparity of big data may prohibit centrally processing and storing the data. This has imposed considerable hurdle for nearest neighbor predictions since the entire training data must be memorized. One effective way to overcome this issue is the distributed learning framework. Through majority voting, the distributed nearest neighbor classifier achieves the same rate of convergence as its oracle version in terms of the regret, up to a multiplicative constant that depends solely on the data dimension. The multiplicative difference can be eliminated by replacing majority voting with the weighted voting scheme. In addition, we provide sharp theoretical upper bounds of the number of subsamples in order for the distributed nearest neighbor classifier to reach the optimal convergence rate. It is interesting to note that the weighted voting scheme allows a larger number of subsamples than the majority voting one. Our findings are supported by numerical studies.

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An approach towards AGV battery cell voltage prediction using DL and data pre-processing

An approach towards AGV battery cell voltage prediction usin...

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International Computer Sciences and Information Technologies (CSIT)

作者： Mykola Medykovskyy Rafal Cupek Olena Pavliuk Myroslav Mishchuk ACS Department Lviv Polytechnic National University Lviv Ukraine Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland

Modern methods for solving the AGV battery cell voltage prediction problem include a symbiosis of probabilistic and machine learning methods. In this study, we propose to use two RNN-based approaches with preliminary data analysis and processing. In the first approach, we make predictions based on smoothed data. Three filters were chosen for smoothing: exponential smoothing; moving average; weighted moving average. The moving average method showed the best results, but according to the Dickey-Fuller test, the predicted signal was still not stationary. The second approach was based on a centered stationary signal obtained by subtracting the value of the moving average from the original *** forecast accuracy was evaluated using the following errors: MAPE, RMSE, MSPE, RMSPE, MFE, SEBIAS, ME. Adequacy of the proposed neural network model based on the coefficient of determination R2. As can be seen from Table 2, a significant improvement in prediction accuracy is observed for the centered signal. The maximum error was reduced by 5.6 times R2 increasing by 0.0596. At the same time, both models are adequate.

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Joint Partitioning and Sampling Algorithm for Scaling Graph neural network

Joint Partitioning and Sampling Algorithm for Scaling Graph ...

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International conference on High Performance Computing

作者： Manohar Lal Das Vishwesh Jatala Gagan Raj Gupta Department of EECS Indian Institute of Technology Bhilai Raipur India

Graph neural network (GNN) has emerged as a popular toolbox for solving complex problems on graph data structures. Graph neural networks use machine learning techniques to learn the vector representations of nodes and/or edges. Learning these representations demands a huge amount of memory and computing power. The traditional shared-memory multiprocessors are insufficient to meet real-world data’s computing requirements; hence, research has gained momentum toward distributed *** the distributed GNN has the following challenges: (1) the input graph needs to be efficiently partitioned, (2) the cost of communication between compute nodes should be reduced, and (3) the sampling strategy should be efficiently chosen to minimize the loss in accuracy. To address these challenges, we propose a joint partitioning and sampling algorithm, which partitions the input graph with weighted METIS and uses a bias sampling strategy to minimize total communication *** implemented our approach using the DistDGL framework and evaluated it using several real-world datasets. We observe that our approach (1) shows an average reduction in communication overhead by 53%, (2) requires less partitioning time to partition a graph, (3) shows improved accuracy, (4) shows a speed up of 1.5x on OGB-Arxiv dataset, when compared to the state-of-the-art DistDGL implementation.

关键词： Training Costs High performance computing Memory management Graphics processing units Machine learning Benchmark testing

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License plate recognition system based on image recognition

License plate recognition system based on image recognition

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IEEE International Symposium on Parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Liang Wang Yimei Huanga Chengqun Liang Jinrong Zhou Taoqiang Zhu Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

As people’s living standards rise, efficient and intelligent traffic management system has become the demand of the society. Although there are many researches on vehicle license plate recognition technology in China, there are still many shortcomings in practical application. To solve the above problems, this paper uses OpenCV machine vision library based on Python development platform and convolutional neural network to study the process of license plate recognition, and proposes a vehicle license plate recognition system based on machine vision technology and pattern recognition method. System using digital image correlation algorithm to detect and locate license plate, based on the analysis to extract the edge of the information, character segmentation and character quantity calculation, at last, we use Convolutional neural network (CNN) for character recognition and classification in order to obtain accurate and clear plates in formation. At last, we use convolution neural network (CNN) for character recognition and classification in order to obtain accurate and clear plates information, its accuracy is as high as 98%, has the good recognition effect.

关键词： Image segmentation Image color analysis Image edge detection neural networks Object segmentation Feature extraction Convolutional neural networks

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neural Speech Separation Using Spatially distributed Microphones 21

Neural Speech Separation Using Spatially Distributed Microph...

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Interspeech conference

作者： Wang, Dongmei Chen, Zhuo Yoshioka, Takuya Microsoft One Microsoft Way Redmond WA 98052 USA

ISBN: (纸本)9781713820697

This paper proposes a neural network based speech separation method using spatially distributed microphones. Unlike with traditional microphone array settings, neither the number of microphones nor their spatial arrangement is known in advance, which hinders the use of conventional multi-channel speech separation neural networks based on fixed size input. To overcome this, a novel network architecture is proposed that interleaves inter-channel processing layers and temporal processing layers. The inter-channel processing layers apply a self-attention mechanism along the channel dimension to exploit the information obtained with a varying number of microphones. The temporal processing layers are based on a bidirectional long short term memory (BLSTM) model and applied to each channel independently. The proposed network leverages information across time and space by stacking these two kinds of layers alternately. Our network estimates time-frequency (TF) masks for each speaker, which are then used to generate enhanced speech signals either with TF masking or beamforming. Speech recognition experimental results show that the proposed method significantly outperforms baseline multi-channel speech separation systems.

关键词： distributed microphone array ad hoc array speech separation overlapped speech distant speech recognition

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A Comparative Study on Improving Word Embeddings Beyond Word2Vec and GloVe

A Comparative Study on Improving Word Embeddings Beyond Word...

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International conference on Parallel distributed and Grid Computing (PDGC)

作者： Rajdeep Biswas Suman De BITS Pilani & Data Scientist CXS APM SAP Labs India Pvt. Ltd. Bangalore India CCDS IIT Kharagpur & S4 Product Insights SAP Labs India Pvt. Ltd. Bangalore India

ISBN: (纸本)9781665454025

NLP or Natural Language processing in Machine Learning forms a subarea with linguistic roots that has applications in analyzing and predicting natural language data, namely speech and text. Deep neural networks are used in cutting-edge NLP, and the process includes several steps: data collection, preprocessing, language modeling, parsing, and prediction. This research will focus on the aspect of language modeling where words must be represented using embeddings. The study emphasizes improving post-embedding model performance, particularly for unsupervised models, although the whole word embeddings used by these models have remained relatively unchanged. Word2Vec, for example, employs an unsupervised shallow 2-layered network that has suited its function thus far, but making this network denser and adding supervised elements appears to be a viable means of increasing spatial-semantic embedding. A better representation early in the model pipeline will significantly help any computer understand the semantic similarity of the data it is examining, which will considerably enhance the performance of any model that employs the stated representation.

关键词： Symbiosis Computational modeling Semantics Pipelines neural networks Predictive models Linguistics

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DGD2: a linearly convergent distributed algorithm for high-dimensional statistical recovery 22

DGD2: a linearly convergent distributed algorithm for high-d...

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Proceedings of the 36th International conference on neural Information processing Systems

作者： Marie Maros Gesualdo Scutari School of Industrial Engineering Purdue University

ISBN: (纸本)9781713871088

We study linear regression from data distributed over a network of agents (with no master node) under high-dimensional scaling, which allows the ambient dimension to grow faster than the sample size. We propose a novel decentralization of the projected gradient algorithm whereby agents iteratively update their local estimates by a "double-mixing" mechanism, which suitably combines averages of iterates and gradients of neighbouring nodes. Under standard assumptions on the statistical model and network connectivity, the proposed method enjoys global linear convergence up to the statistical precision of the model. This improves on guarantees of (plain) DGD algorithms, whose iteration complexity grows undesirably with the ambient dimension. Our technical contribution is a novel convergence analysis that resembles (albeit different) algorithmic stability arguments extended to high-dimensions and distributed setting, which is of independent interest.

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