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Optimization of RDMA-based HDFS Data Distribution Mechanism 17th

Optimization of RDMA-Based HDFS Data Distribution Mechanism

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17th IFIP WG 10.3 International conference on network and parallel Computing, NPC 2020

作者： Zhang, Xiao Liu, Binbin Zhao, Junhao Dong, Cong School of Computer Science Northwestern Polytechnical University Xian Shaanxi710129 China

ISBN: (纸本)9783030794774

Hadoop distributed File System (short for HDFS) is a high availability file system designed to run on commodity hardware. It uses multiple replicas to ensure high reliability, and many data are transmitted between storage nodes. The performance of data transmission has a great impact on the latency of writing operations. Remote Direct Memory Access (short for RDMA) is a protocol with low latency and high through which is running on the Infiniband network. When HDFS runs on the Infiniband network, the default protocol IPoIB can not take advantage of the high-speed network. The latency of the writing process is similar to a TCP/IP network. In this paper, we present a new RDMA-based HDFS writing mechanism. It enables DataNodes to read data parallelly from the Client through RDMA. And by using RDMA primitive, the transmission latency is slower than the original TCP/IP protocol. The experiments show that our approach reduces the latency of the writing process by 10.11%–40.81% compared with the original HDFS. © 2021, IFIP International Federation for Information processing.

关键词： HIgh speed networks

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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...

引用

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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HIGH-PERFORMANCE COMPUTING PROBABILISTIC FRACTURE MECHANICS IMPLEMENTATION FOR GAS TURBINE ROTOR DISKS ON distributed ARCHITECTURES INCLUDING GRAPHICS processing UNITS (GPUS)

HIGH-PERFORMANCE COMPUTING PROBABILISTIC FRACTURE MECHANICS ...

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ASME Turbomachinery Technical conference and Exposition (Turbo Expo) - Sustainable Energy - Accelerating the Transition by Advancing Turbine Technology

作者： Gajjar, Mrugesh Amann, Christian Kadau, Kai Siemens Technol & Serv Pvt Ltd Bangalore Karnataka India Siemens Energy Mulheim Germany Siemens Energy Charlotte NC USA

ISBN: (纸本)9780791885031

We present an efficient Monte Carlo based probabilistic fracture mechanics simulation implementation for heterogeneous high-performance (HPC) architectures including CPUs and GPUs. The specific application focuses on large heavy-duty gas turbine rotor components for the energy sector. A reliable probabilistic risk quantification requires the simulation of millions to billions of Monte Carlo (MC) samples. We apply a modified Runge-Kutta algorithm in order to solve numerically the fatigue crack growth for this large number of cracks for varying initial crack sizes, locations, material and service conditions. This compute intensive simulation has already been demonstrated to perform efficiently and scalable on parallel and distributed HPC architectures including hundreds of CPUs utilizing the Message Passing Interface (MPI) paradigm. In this work, we go a step further and include GPUs in our parallelization strategy. We develop a load distribution scheme to share one or more GPUs on compute nodes distributed over a network. We detail technical challenges and solution strategies in performing the simulations on GPUs efficiently. We show that the key computation of the modified Runge-Kutta integration step speeds up over two orders of magnitude on a typical GPU compared to a single threaded CPU. This is supported by our use of GPU textures for efficient interpolation of multi-dimensional tables utilized in the implementation. We demonstrate weak and strong scaling of our GPU implementation, i.e., that we can efficiently utilize a large number of GPUs/CPUs in order to solve for more MC samples, or reduce the computational turn-around time, respectively. On seven different GPUs spanning four generations, the presented probabilistic fracture mechanics simulation tool ProbFM achieves a speed-up ranging from 16.4x to 47.4x compared to single threaded CPU implementation.

关键词： Fracture Mechanics Fatigue Crack Growth Forging Flaws Probabilistic Monte Carlo GPU Computing High-Performance Computing Monte Carlo Simulation

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WiBWi: Encoding-based Bidirectional Physical-Layer Cross-Technology Communication between BLE and WiFi 27

WiBWi: Encoding-based Bidirectional Physical-Layer Cross-Tec...

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27th IEEE International conference on parallel and distributed Systems, ICPADS 2021

作者： Shu, Yuanhe Wang, Jingwei Kong, Linghe Yu, Jiadi Yang, Guisong Cai, Yueping Wang, Zhen Khan, Muhammad Khurram Shanghai Jiao Tong University China University of Shanghai for Science and Technology China Chongqing University China Instrumentation Technology and Economy Institute China King Saud University Saudi Arabia

ISBN: (纸本)9781665408783

The booming of mobile technologies and Internet of Things (IoTs) have facilitated the explosion of wireless devices and brought convenience to people's daily lives. Coming with the explosive growth of wireless devices, incompatibility of heterogeneous wireless technologies hindered the growing demands for everything connected. And spectrum sharing among heterogeneous wireless technologies has led to severe Cross-Technology Interference (CTI), which is a vital obstacle for network reliability and spectrum utilization. Researches in recent years have shown that Cross-Technology Communication (CTC) turns out to be a promising solution with broad perspective for the coexistence of heterogeneous wireless technologies. However, due to the physical layer incompatibility of WiFi and Bluetooth Low Energy (BLE), the researches about CTC between these two most wildly used wireless technologies are limited by now. In this paper, we propose WiBWi, a payload encoding-based bidirectional CTC scheme between BLE and WiFi, which can achieve near-optimal throughput and powerful robustness. For uplink, i.e., BLE to WiFi communication, WiBWi leverages a novel extended WiFi preamble detection rule and probabilistic inference based encode mapping to achieve fast and reliable communication. For downlink, i.e., WiFi to BLE communication, WiBWi introduces an encoding mapping scheme in the sight of BLE receiver with little modification to accomplish high throughput and robustness. Extensive evaluation shows that WiBWi can offer near-optimal throughput (near the maximum throughput of BLE) and extremely low bit error rate (less than 1%). © 2021 IEEE.

关键词： Internet of things

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A Graph Convolution Neural network based Method for Insider Threat Detection

A Graph Convolution Neural Network Based Method for Insider ...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Kexiong Fei Jiang Zhou Lin Su Weiping Wang Yong Chen Fan Zhang School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Computer Science Texas Tech University Lubbock USA Information Technology Center China Mobile Beijing China

In this research, we propose Log2Graph, a new insider threat detection method based on graph convolution neural network (GCN). This method first retrieves the corresponding logs and features from log files through feature extraction. Specifically, we use an auxiliary feature of anomaly index to describe relationship between entities, such as users and hosts, instead of establish complex connections between them. Second, these logs and features are augmented through a combination of oversampling and downsampling, to prepare for the next-stage supervised learning process. Third, we use three elaborated rules to construct the graph of each user by connecting the logs according to chronological and logical relationship. At last, the graph convolution neural network constructed is used to detect insider threats. Our validation and evaluation results confirm that Log2Graph can greatly improve the performance of detecting insider threats compared against baseline and existing methods.

关键词： Knowledge engineering Convolution Neural networks Supervised learning Feature extraction Time measurement Data models

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Big Data Classification Model and Algorithm based on Double Quantum Particle Swarm Optimization

Big Data Classification Model and Algorithm Based on Double ...

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Control, Electronics and Computer Technology (ICCECT), IEEE International conference on

作者： Duan Jingbo Guangdong Business and Technology University Guangdong China Philippine Christian University Center for International Education Manila Philippine

For the traditional big data classification models and algorithms that have the drawback of long processing time, the use of intelligent algorithms to model identification of multivariable systems has become a mainstream research direction. Discussion This paper proposes an efficient solution for data-parallel optimized computing. The problems in the identification of multi-variable systems that integrate historical big data and intelligent algorithms are analyzed, and the impact of input variables on output is quantified. Build a big data classification model, mine valid data points, arrange data combination methods, divide data local connection methods, obtain local node micro-cluster data, calculate redundant data and invalid data increment values in node data groups, and reconstruct central node sample algorithm, adjust the integrated data classification strategy, optimize and update the data integration classification method. Design the experiment, simulate the experimental environment, and verify that the proposed model calculation method can shorten the data processing time in practical application, which has practical research value. Through the design of the overall process of the algorithm, data classification to form a data set, and then describe the steps of the algorithm, and finally verify the feasibility of the algorithm through simulation experiments, which can not only greatly reduce the communication cost between network nodes, but also greatly improve the global mining accuracy and improve the query efficiency in distributed data, It has good utilization value in the analysis of massive data information.

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Word Embedding-based Context-sensitive network Flow Payload Anomaly Detection 3

Word Embedding-based Context-sensitive Network Flow Payload ...

引用

3rd International conference on Applied Machine Learning, ICAML 2021

作者： Li, Yizhou Wang, Yijie Cheng, Li Xu, Hongzuo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665421256

Payload anomaly detection can discover malicious beliaviors tiidden in network packets. It is liard to liandle payload due to its various possible characters and complex semantic context, and tlius identifying abnormal payload is also a non-trivial task. Prior art only uses the n-gram language model to extract features, which directly leads to ultra-high-dimensional feature space and also fails to capture the context semantics fully. Accordingly, this paper proposes a word embedding-based context-sensitive network flow payload anomaly detection method (termed WECAD). First, WECAD obtains the initial feature representation of the payload through the word embedding-based method. Then, we propose a corpus pruning algorithm, which appUes the cosine similarity clustering and frequency distribution to prune inconsequential characters. We only keep the essential characters to reduce the calculation space. Subsequently, we propose a context learning algorithm. It employs the co-occurrence matrix transformation technology and introduces the backward step size to consider the order relationship of essential characters. Comprehensive experiments on real-world intrusion detection datasets validate the effectiveness of our method. © 2021 IEEE

关键词： Embeddings

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A Dual-stage Attention based SDN Traffic Prediction Method

A Dual-stage Attention Based SDN Traffic Prediction Method

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Chenjing Gao Junli Wang Chungang Yan Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China

Traffic matrix is the main research object of traffic prediction in software-defined networking. Accurate and timely traffic matrix prediction plays an important role in avoiding network congestion. While various methods have been proposed in previous studies to solve the SDN traffic prediction problem, few of them consider both inter- and intra-flow features. In this paper, we propose a dual-stage attention based Traffic Prediction method for the SDN traffic matrix prediction task. First, our method uses temporal pattern attention as the inter-flow attention mechanism before the encoding stage to realize adaptive feature extraction. Then, temporal attention is introduced as the intra-flow attention mechanism before the decoding stage to capture long-term temporal dependencies. Finally, we use the autoregressive module to handle the highly dynamic SDN traffic volume. Experimental results show that our proposed SDN traffic prediction method can capture more traffic features and improve the SDN traffic matrix prediction performance to some extent.

关键词： network topology Time series analysis Prediction methods Machine learning Feature extraction Graph neural networks Encoding

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Retrofitting High Availability Mechanism to Tame Hybrid Transaction/Analytical processing 15

Retrofitting High Availability Mechanism to Tame Hybrid Tran...

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USENIX Annual Technical conference / 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI)

作者： Shen, Sijie Chen, Rong Chen, Haibo Zang, Binyu Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Artificial Intelligence Lab Minist EducEngn Res Ctr Domain Specif Operating Shanghai Peoples R China

ISBN: (纸本)9781939133229

Many application domains can benefit from hybrid transaction/analytical processing (HTAP) by executing queries on real-time datasets produced by concurrent transactions. However, with the increasingly speedy transactions and queries thanks to large memory and fast interconnect, commodity HTAP systems have to make a tradeoff between data freshness and performance degradation. Fortunately, we observe that the backups for high availability in modern distributed OLTP systems can be retrofitted to bridge the analytical queries and transactions in HTAP workloads. In this paper, we present VEGITO, a distributed in-memory HTAP system that embraces freshness and performance with the following three techniques: (1) a lightweight gossip-style scheme to apply logs on backups consistently;(2) a block-based design for multi-version columnar backups;(3) a two-phase concurrent updating mechanism for the tree-based index of backups. They collectively make the backup fresh, columnar, and fault-tolerant, even facing millions of concurrent transactions per second. Evaluations show that VEGITO can perform 1.9 million TPC-C NEWORDER transactions and 24 TPC-H-equivalent queries per second simultaneously, which retain the excellent performance of specialized OLTP and OLAP counterparts (e.g., DrTM+H and MonetDB). These results outperform state-of-the-art HTAP systems by several orders of magnitude on transactional performance, while just incurring little performance slowdown (5% over pure OLTP workloads) and still enjoying data freshness for analytical queries (less than 20 ms of maximum delay) in the failure-free case. Further, VEGITO can recover from cascading machine failures by using the columnar backup in less than 60 ms.

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ECRaft: A Raft based Consensus Protocol for Highly Available and Reliable Erasure-Coded Storage Systems 27

ECRaft: A Raft Based Consensus Protocol for Highly Available...

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27th IEEE International conference on parallel and distributed Systems, ICPADS 2021

作者： Xu, MingWei Zhou, Yu Qiao, Yuan Yuan Xu, Kai Wang, Yu Yang, Jie School of Artificial Intelligence Beijing University of Posts and Telecommunications China Technology Research Institute Aisino Corporation Beijing China

ISBN: (纸本)9781665408783

Erasure-coded redundancy is a fault-tolerant method with low-cost storage overhead. It only stores data fragments and parity fragments rather than full data across the cluster. The write process of erasure-coded data can be asynchronous or synchronous. For synchronous write process, data are encoded when written to servers. The common method doing the process needs to confirm that each coded-fragment of the data is stored in a different server to maintain the best fault tolerance. This method underperforms in terms of availability, and also fails to achieve good performance because any failure of servers will shortly disturb the write process. Some consensus protocols such as RS- Paxos and CRaft, which are based on Paxos and Raft, can solve above problems by providing fault-tolerant ability for systems. However, RS-Paxos cannot achieve the same liveness as Paxos. CRaft still adopts full data redundancy to keep the same liveness as Raft when there are not enough healthy servers. Therefore, to solve the availability problem during synchronous erasure-coded data write process, we present a novel protocol ECRaft based on Raft. It always uses erasure-coded redundancy when the ratio of erasure-coded data fragments to parity fragments is bigger than 1. It also can reach the same liveness as Raft. With state machine purge, storage redundancy can be reduced to the extent that typical erasure-coded storage systems can achieve. We build a key-value store based on ECRaft to evaluate it. In our experiments, compared with CRaft using complete-entry replication, ECRaft can save 63 % of storage, increase write throughput by 28.2 %, and reduce write latency by 19 %. © 2021 IEEE.

关键词： Fault tolerance

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