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G-Update: A Group-based Update Scheme for Heterogenous Erasure-coded Storage systems

G-Update: A Group-based Update Scheme for Heterogenous Erasu...

引用

ieee international conference on High Performance computing and Communications (HPCC)

作者： Xuyu Zhao Bing Zhu Zhiwei Zeng Weiping Wang Kenneth W. Shum School of Computer Science and Engineering Central South University School of Science and Engineering Chinese University of Hong Kong (Shenzhen)

Erasure codes generate redundant encoded chunks to improve data reliability by encoding original data, among which wide stripe achieves extreme low storage redundancy to improve data storage efficiency. When original data need to be update, erasure codes spend long update time and high network consumption to update every encoded chunk. the frequent update of stored data in distributed storage systems has posed great challenges to the timeliness of data update. However, state-of-the-art works have mainly considered homogeneous storage environment. therefore we mainly consider the scheme of data update in the heterogeneous storage environment for multiple node update in wide stripe. In this paper, we propose a _Qroup-based Update scheme for heterogenous erasure-coded storage systems, called G- Update. G- Update divides update process into three stages: (i) grouping stage, which groups updated data nodes and parity nodes together with parity nodes as the group center, (ii) intra-group update stage, which transmits update deltas with maximum bandwidth link within the group, and (iii) inter-group update stage, which exchanges update deltas between groups. To evaluate the performance of G-Update, we conduct simulation experiments on different update schemes. Results show that G-Update can reduce the update time by 15.59% and 81.60% on average compared with S-Update and T-Update respectively.

关键词： Codes Redundancy distributed databases Memory Bandwidth Encoding Reliability

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the effect of symmetric block ciphers on WSN performance and behavior

The effect of symmetric block ciphers on WSN performance and...

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ieee international conference on Wireless and Mobile computing, Networking And Communications (WiMob)

作者： Ch. P. Antonopoulos Ch. Petropoulos K. Antonopoulos V. Triantafyllou N. S. Voros Technological Educational Institute of Mesolonghi Department of Telecommunication Systems and Networks National Road Antiriou Nafpaktou Nafpaktos Greece

Nowadays Wireless sensor Networks are increasingly accepted as a reliable solution to highly demanding and critical application scenarios such as military and medical environments, where security support is an absolute prerequisite. However, supporting security implies the execution of cipher algorithms posing significant overheads on WSN nodes, which suffer from scarce resource availability. Moreover, the system wide overheads imposed by requirements related to network performance and behavior are not adequately addressed in current literature. In that direction, this paper intends to evaluate these effects for critical network parameters. the results presented are based on existing results reported in literature measurements concerning the performance overhead imposed by widely utilized encryption algorithms that have been developed for prominent WSN platforms. To evaluate the measurements, the execution performance of three popular cipher algorithms, has been integrated in Omnet++/MiXiM, a well known and widely utilized WSN network simulator. As part of this work, critical insights are provided concerning the system wide effect of deploying security algorithms which are not taken into account when focusing solely in the security algorithm measurements. Furthermore, important trade-offs are provided both qualitatively and quantitatively.

关键词： Delay Wireless sensor networks Encryption Algorithm design and analysis Payloads

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BTDFS: B+ Tree-Based distributed File Storing Mechanism for Improving Small File Storage

BTDFS: B+ Tree-Based Distributed File Storing Mechanism for ...

引用

ieee international conference on High Performance computing and Communications (HPCC)

作者： Jingyu Zhang Jiachen Tian Lailong Luo School of Computer & Communication Engineering Changsha University of Science & Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China

Hadoop is currently the most popular big data processing architecture, which provides a processing framework for managing and analyzing massive data. Hadoop distributed File System (HDFS) is the core component for storing data in the Hadoop systems. It performs well when storing and managing large-sized files. However, it shows extremely bad performance when dealing with a large number of small files, which is reflected in the consumption of a large amount of memory space of the NameNode node and the low efficiency of accessing small files. Archive file is proposed to solve this problem. It combines small files into larger merged files for storage, and uses index files to record the relevant information of small files in the merged files. However, archive files (e.g., Hadoop Archive, MapFile) all require additional processing and multiple I/O operations to obtain index information before actually accessing the file content, which reduces the efficiency of file access. this paper proposes a novel distributed file storing mechanism called B+ Tree-Based distributed File Storing mechanism (BTDFS), which combines a large number of small files into a merged file, records the index information of the small files in the merged file, and organizes the index information into a B+ tree for storage. Using BTDFS, while reducing the metadata consumption of the NameNode node, improves the access performance of a single small file with additional abilities, which provides convenience for analyzing a large number of small files. Finally, this paper designs corresponding experiments for the proposed BTDFS, and tests and analyzes the memory usage in N ameN ode and the access performance of small files. the experimental results show that the proposed BTDFS can effectively reduce the memory usage of the NameNode, and the efficiency of accessing small files is improved compared with state-of-the-art methods.

关键词： File systems Memory management distributed databases Big Data Metadata Indexing

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A Layer-Based Sparsification Method For distributed DNN Training

A Layer-Based Sparsification Method For Distributed DNN Trai...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Yanqing Hu Qing Ye Zhongyu Zhang Jiancheng Lv Sichuan University Chengdu China

With the increasing size of Deep Neural Networks (DNNs) and datasets, DNN training will consume a lot of time. Various distributed strategies have been utilized to speed up the training process. Nevertheless, the frequent communication between different computational nodes dramatically limits the scale of the cluster. Currently, many gradient compression methods (e.g., Sparsification, Quantization, Low-Rank) have been proposed to optimize the transmission process. Since each layer needs to be processed and compressed respectively before transmission, a rash of time-consuming tensor operations is demanded. To alleviate this issue, we propose a layer-based sparsification method, which stems from the observation that the learning efficiency and convergence speed of different layers of DNN is different during the training process. Instead of compressing all gradients in an iteration, some layers are preferentially selected using a well-designed sliding window, which largely avoids unnecessary tensor operations. the sliding window is adjusted according to the dynamic characteristics of the parameter during the DNN training. Extensive experiments on one Text task and two Image Classification tasks are conducted to validate the efficiency of the proposed method in the same environment configuration, where the gradient compression adopts the layer-based sparsification and the other three comparative state-of-the-art methods. the total training time, communication time, and model accuracy are collected and analyzed. Sufficient experimental results demonstrate that both training and communication time can be greatly reduced, which is especially up to 60% in some cases. Additionally, despite slightly decreased within 1% model quality, the results indicate that the improvement in throughput can be as high as 50%.

关键词： Training Image coding Tensors Quantization (signal) Computational modeling throughput Data models

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PC-BNA: Parallel Convolution Binary Neural Network Accelerator

PC-BNA: Parallel Convolution Binary Neural Network Accelerat...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Haihui Liu Yuang Zhang Xiangwei Zheng Mengxin Yu Baoxu An Xiaojie Liu Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology School of Information Science and Engineering Shandong Normal University Jinan China

Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dataset is obviously low, and the input layer uses 32-bit floating point to avoid excessive precision loss, which requires additional computing units and increases the computational burden. therefore, it is very important to improve the input layer, save computing resources and reduce precision loss. In this paper, we propose a parallel convolution binary neural network accelerator architecture (PC-BNA). Based on our proposed BNN model, we design an efficient field programmable gate array (FPGA) based accelerator. the input of the first layer is binarized, the traditional binary convolution layer is replaced by a parallel binary convolution, and the network building blocks are improved. the experimental results show that the proposed PC-BNA has higher accuracy and better performance on CIFAR-10. the image recognition accuracy is as high as 91.4%, which is superior to the state-of-art of the BNN accelerator. Compared to the state-of-the-art BNN model, when using the same model size, the use of look-up-table (LUT) saves 9.08%, and the digital signal processor (DSP) saves 27.7%. the results suggest that it is promising for PC-BNA in future high performance mobile artificial intelligence applications.

关键词： Performance evaluation Convolution Neural networks Digital signal processors Speech recognition Logic gates Mobile handsets

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Performance evaluation of job scheduling algorithms in P2P distributed systems

Performance evaluation of job scheduling algorithms in P2P d...

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the international conference on Informatics and systems (INFOS)

作者： Tarek Helmy Elsayed Sallam College of Computer Science and Engineering King Fahd University of Petroleum and Minerals Dhahran 31261 Mail Box 413 Saudi Arabia Faculty of Engineering Department of Computers & Automatic Control Engineering Tanta University Egypt

Peer-to-Peer (P2P) distributed systems have received considerable attention mainly due to their potential for high-performance and high-reliability. Job scheduling in P2P distributed systems is one of the most important issues. In this paper, we have evaluated the performance of four job scheduling algorithms named Random, Round Robin, Number of Connections and Server Load in P2P distributed computing environment. the aim of the performance evaluation is to provide a comprehensive view of what performance can be expected from these algorithms in P2P systems in terms of CPU utilization, task processing, traffic received and traffic sent at different load scenarios. We used OPNET modeler to build the simulation model for FTP application on the P2P distributed systems. the output of the performance comparison illustrates different QoS (Quality of Service) aspects of these basic algorithms under different workloads and traffic settings. the results show that the performance of each algorithm changes according to the operational environment setting. the simulation results show that the Server Load-based scheduling algorithm has the best performance among the four algorithms followed by the Number of Connections-based scheduling algorithm. Simulations also show that the Server Load scheduling policy is not suitable for heavy load situations and the Random algorithm is suitable for some settings, especially in the low load situation.

关键词： Portable document format ieee Xplore

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FNotify: A Low-Latency and Scalable Publish/Subscribe System using RDMA

FNotify: A Low-Latency and Scalable Publish/Subscribe System...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Yusen Xu Sijie Shen Mingcong Han Rong Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University Shanghai China

Publish/subscribe systems are widely used as messaging middleware in various scenarios, such as online business, financial trading, and social media. However, the network overhead intro-duced by the pub/sub system can severely impact application performance. Especially in the latency-sensitive applications of the data center, optimizing the latency of publishing or delivering messages of the pub/sub system is particularly important. RDMA network has gained popularity in relevant applications due to its low latency, high bandwidth and ability to bypass the CPU. Never-theless, it is still challenging to design and optimize the pub/sub system using RDMA. through an in-depth analysis of RDMA features, we design and implement FNotify, a high-performance RDMA-accelerated pub/sub system. FNotify optimizes latency and CPU load using in-memory message management and one-sided RDMA primitives for message collection. FNotify also proposes a topic-based partitioning scheme to achieve good scal-ability. We conducted a comprehensive evaluation and analysis on an 8-node RDMA cluster to verify the validity of our design by comparing the performance of FNotify with Apache Kafka (a state-of-the-art pub/sub system) in a typical pub/sub application scenario and various micro-benchmarks. Experimental results show that FNotify improves overall performance by $1.5\times$ to $3.9\times$ and reduces end-to-end latency by more than $20\times$ compared to Apache Kafka.

关键词： Data centers Publishing Social networking (online) Scalability Semantics systems architecture Low latency communication

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IoT–based tire pressure monitoring system for air and temperature pressure using MPX5500D and LM35 sensor

IoT–based tire pressure monitoring system for air and tempe...

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international conference on computing, Engineering, and Design (ICCED)

作者： Agung Pangestu Iing Sodikin Muhammad Yusro Rum Sapundani Rosyid Ridlo Al Hakim Sinka Wilyanti Electrical Engineering Department Faculty of Engineering and Computer Science Jakarta Global University Depok Indonesia Department of Electronic Engineering Faculty of Engineering State University of Jakarta Jakarta Indonesia Electromedical Engineering Technology Faculty of Health Science University of Muhammadiyah Purwokerto Purwokerto Indonesia Graduate Program in Electrical Engineering Faculty of Engineering and Computer Science Jakarta Global University Depok Indonesia

ISBN: (纸本)9781665453905

Research has been carried out to monitor vehicle tires before they are used and can reduce damage, including overcoming vehicle fuel waste because air pressure is continuously monitored. this research aims to utilize the MPX5500DP sensor as an air pressure device, the LM35 sensor as a temperature reader, and a buzzer based on IoT to build a tire pressure monitoring system (TPMS). the MPX5500DP and LM35 sensor inputs to the Arduino Uno microcontroller are distributed by the NodeMCU, fitted with a Wi-Fi module. the Blynk application sends and displays the data on a smartphone using the IoT-based. Based on this research, data on the percentage of errors in monitoring air pressure and tire temperature on vehicles were obtained by comparing the data to the pressure gauge and thermometer: 1—the results of the average reading of the sensor error value. MPX5500DP air pressure against pressure gauge is 5.3%. 2—the average reading of the LM35 sensor error value on the temperature thermometer is 6.8%. With this research, the air pressure and temperature in the tires can be monitored in real-time via a smartphone using the IoT-based.

关键词： Temperature measurement Temperature distribution thermometers Microcontrollers Tire pressure Real-time systems Pressure gauges

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Real-Time Analysis of a Novel Approach for Localization and Tracking of a Mobile Node

Real-Time Analysis of a Novel Approach for Localization and ...

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international conference on Wireless Communications, Networking and Mobile computing (WiCom)

作者： Muhammad Haroon Siddiqui Muhammad Rehan Khalid Electrical Engineering Department National University of Science and Technology Pakistan

ISBN: (纸本)9781612846842

this paper investigates the performance of a modified particle filter algorithm for tracking and localization of a mobile node. A modified particle filter algorithm is used to track the mobile node, with the aim to reduce the execution time. Tracking is done through trilateration as well as particle filter algorithm. Wi-Fi signal is used to get initial guess of the position of mobile node in x-y coordinates system. Performance is evaluated interms of execution time and root mean square error (RMSE). Commercially available software "Wireless Mon" was used to read the WiFi signal strength from the WiFi card. Visual C++ version 6 was used to interact with this software to read only the required data from the log-file generated by "Wireless Mon" software. this work does not rely on only the simulation model to evaluate the performance of the proposed technique. Experimental real-time analysis are carried out to identify practical limitations of the proposed scheme and to ensure a fair evaluation under realistic operating conditions.

关键词： ieee 802.11 Standards Mobile nodes Particle filters Wireless sensor networks Wireless communication Real-time systems

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FedTADBench: Federated Time-series Anomaly Detection Benchmark

FedTADBench: Federated Time-series Anomaly Detection Benchma...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Fanxing Liu Cheng Zeng Le Zhang Yingjie Zhou Qing Mu Yanru Zhang Ling Zhang Ce Zhu College of Computer Science Sichuan University China School of Information and Communication Engineering University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China College of Polymer Science and Engineering Sichuan University China West China School of Public Health Sichuan University China

Time series anomaly detection strives to uncover potential abnormal behaviors and patterns from temporal data, and has fundamental significance in diverse application scenarios. Constructing an effective detection model usually requires adequate training data stored in a centralized manner, however, this requirement sometimes could not be satisfied in realistic scenarios. As a prevailing approach to address the above problem, federated learning has demonstrated its power to cooperate with the distributed data available while protecting the privacy of data providers. However, it is still unclear that how existing time series anomaly detection algorithms perform with decentralized data storage and privacy protection through federated learning. To study this, we conduct a federated time series anomaly detection benchmark, named FedTADBench, which involves five representative time series anomaly detection algorithms and four popular federated learning methods. We would like to answer the following questions: (l)How is the performance of time series anomaly detection algorithms when meeting federated learning? (2) Which federated learning method is the most appropriate one for time series anomaly detection? (3) How do federated time series anomaly detection approaches perform on different partitions of data in clients? Numbers of results as well as corresponding analysis are provided from extensive experiments with various settings. the source code of our benchmark is publicly available at https://***/fanxindiu2020/FedTADBench.

关键词： Data privacy Federated learning Source coding Time series analysis distributed databases Training data Memory

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