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Accelerating Big Data Application by Eliminating Redundancy on Hadoop Cluster

Accelerating Big Data Application by Eliminating Redundancy ...

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international Conference on parallel and distributed systems (ICPADS)

作者： Kelun Lei Shaokang Du Xin You Zhibo Xuan Haoran Kong Hailong Yang Jing Shang Zhiwen Xiao Zhihui Wu Zhongzhi Luan Depei Qian School of Computer Science and Engineering Beihang University Beijing China China Mobile Information Technology Center China

Big data applications are widely adopted to mine valuable information from a tremendous amount of industry data, which is commonly represented as a series of map-reduce operations. Among various map-reduce frameworks, Hadoop is most commonly adopted for data processing at large scale. Although Hadoop eases the development of highly scalable distributed big data applications, inefficient implementation due to poor coding practice and deep software abstractions can cause severe performance issues such as unreasonable slowdown, high response latency, and waste of computing resources, which can lead to unsatisfactory serving delay or significant maintenance cost. In this paper, we first categorize three common types of redundant patterns in big data applications. Then we propose a tool-assisted optimization workflow to detect the redundant patterns automatically, which profiles the application by sampling hardware performance monitoring units. Moreover, we present a profiling visualization method that can help to pinpoint the redundant codes. Based on these approaches, we optimize several big data applications by eliminating redundancies, yielding up to 14.8% performance improvement.

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DW-LRC: A Dynamic Wide-stripe LRC Codes for Blockchain Data Under Malicious Node Scenarios

DW-LRC: A Dynamic Wide-stripe LRC Codes for Blockchain Data ...

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international Conference on parallel and distributed systems (ICPADS)

作者： Mizhipeng Zhang Chentao Wu Jie Li Minyi Guo Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Blockchain is a decentralized digital ledger system that can be used in many fields. However, the traditional approach of storing blockchain data (full repliction) incurs expensive storage costs. As a result, researchers have proposed erasure coding methods to reduce storage costs. Existing erasure coding methods are all based on RS codes. Since blockchain systems typically have a large number of nodes, it is necessary to use wide-stripe RS codes to store blocks, which results in significant overhead during the recovery processes. Because widestripe RS codes require accessing a large number of nodes for decoding, this means significant network and I/O *** solve the above problem, we propose DW-LRC, which is a dynamic wide-stripe Local Reconstruction Code (LRC) based methods in permissioned blockchain systems. DW-LRC predicts malicious nodes using node reputation, and then selects an appropriate variant of LRC code to lower storage and recovery costs compared to traditional erasure coding. To demonstrate the effectiveness of DW-LRC, we conduct several experiments on the open source blockchain software tendermint. The results show that, compared to the state-of-the-art erasure coding methods, DW-LRC reduced the average recovery latency by 36.4% and improved the block access throughput by 32.3%.

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Dual-view Traffic Identification for Open Source Proxy software through Early Flows

Dual-view Traffic Identification for Open Source Proxy Softw...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yuwei Xu Yunpeng Bai Yuquan Zhang Yige Song Qiao Xiang Guang Cheng School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Open Source Proxy software (OSPS) provides privacy protection for users accessing the Internet by constructing a private anonymizing network. However, there is a growing concern about whether OSPS can actually prevent privacy leaks as it claims. Researchers have attempted to use AI-based techniques to identify OSPS, but there are two shortcomings in the current studies. First, there is no complete public dataset to support the identification tasks for different requirements. The existing datasets do not cover the most commonly used OSPS tools and their typical configurations. Second, with the introduction of deep learning techniques, the models continue to become complex, resulting in significant computational overhead. Using early flows for identification may make the model lighter, but result in weaker representations and lower classification performance. To address the above shortcomings, we have carried out pioneering work on OSPS traffic identification through early flows. First, we collect the access traffic of three OSPS tools and create a dataset with 8 protocol configurations. Second, we present an innovative Dual-View Identification (DVI) method for OSPS traffic. By considering both static and dynamic views, DVI effectively characterizes early flows and achieves accurate classification through feature fusion. In the static view, spatial distribution features are extracted by representing the early flows as a grayscale picture. In the dynamic view, spatial features and temporal correlations are represented using a flow with multiple packets, similar to a video with multiple frames. Comparative experiments show that DVI achieves over 90% accuracy and F1 scores in all three tasks, which greatly improves its ability to identify different protocol configurations and access sites. Besides, DVI outperforms 5 state-of-the-art methods and achieves low parameters and FLOPS through early flows.

关键词： Privacy Protocols Accuracy Graphical models Computational modeling Gray-scale Feature extraction software Internet Protection

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PC-BNA: parallel Convolution Binary Neural Network Accelerator 24

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

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

ISBN: (纸本)9798350319934

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. © 2022 IEEE.

关键词： Convolution

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Slim-MY: A Lightweight Network for Power Line Defect Detection with FPGA Acceleration

Slim-MY: A Lightweight Network for Power Line Defect Detecti...

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international Conference on parallel and distributed systems (ICPADS)

作者： Xiaoyang Zhang Wenjie Chen Zihao Zhou Zhuowen Duan MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

The power line is a crucial component of the power system. However, long-term exposure to the natural environment can lead to various defects, such as burrs, corrosion, and corona. These defects seriously threaten the safe operation of the power system. Traditional manual inspections and image processing methods are time-consuming, labor-intensive, and have lower accuracy. In light of these, we propose a lightweight network called Slim-MY. It utilizes lightweight feature extraction and fusion modules, significantly reducing computational and parameter overhead while ensuring defect detection accuracy. Furthermore, to accelerate the post-processing process, we propose the FP-NMS algorithm. It parallelizes the sorting and overlapping box removal parts of the NMS algorithm, making it more suitable for deployment on devices that support parallel computing, such as FPGA. By testing on our dataset of power line defects, the Slim-MY network achieves a detection accuracy and computation of 92.7% and 1.1 GFLOPs respectively, which outperforms most other networks. By accelerating on the KV260 FPGA, the Slim-MY network achieves an inference speed of 94 fps, and the FP-NMS algorithm’s execution speed is 14.8 times that of the GTX 1080Ti. It indicates the feasibility and efficiency of the method we propose in power line defect detection tasks.

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Efficient Asynchronous GCN Training on a GPU Cluster 27

Efficient Asynchronous GCN Training on a GPU Cluster

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27th IEEE international Conference on parallel and distributed systems, ICPADS 2021

作者： Zhang, Yi Goswami, Dhrubajyoti Concordia University Department of Computer Science and Software Engineering Montreal Canada

ISBN: (纸本)9781665408783

Research on Graph Convolutional Networks (GCNs) has increasingly gained popularity in recent years due to the powerful representational capacity of graphs. A common assumption in traditional synchronous parallel training of GCNs using multiple GPUs is that load is perfectly balanced. However, this assumption may not hold in a real-world scenario where there can be imbalances in workloads among GPUs for various reasons. In a synchronous parallel implementation, a straggler in the system can limit the overall speed up of parallel training. To address these performance issues, this research investigates approaches for asynchronous decentralized parallel training of GCNs on a GPU cluster. The techniques investigated are based on graph clustering and the Gossip protocol. The research specifically adapts the approach of Cluster GCN, which uses graph partitioning for SGD based training, and combines with a gossip algorithm specifically designed for a GPU cluster to periodically exchange gradients among randomly chosen partners (GPUs). In addition, it incorporates a work pool mechanism for load balancing among GPUs. The gossip algorithm is proven to be deadlock free. The implementation is performed on a deep learning cluster with 8 Tesla V100 GPUs per compute node, and PyTorch and DGL as the software platforms. Experiments are conducted on different benchmark datasets. The results demonstrate superior performance with similar accuracy scores, as compared to traditional synchronous training which uses 'all reduce' to synchronously accumulate parallel training results. © 2021 IEEE.

关键词： Graphics processing unit

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Reinforcement Learning based Scheduling Optimization Mechanism on Switches

Reinforcement Learning based Scheduling Optimization Mechani...

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international Conference on parallel and distributed systems (ICPADS)

作者： Xijia Lu Xingwei Wang Jie Jia Xue Wang Min Huang Software College Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

In the data center network, mixed flows which have contradictory service requirements are transmitted simultaneously. Switches usually aggregate similar flows to the same queue after flow classification and schedule them using fair queuing and its extension schemes capable of flow isolation. These schemes implement diverse bandwidth allocation by assigning different weights to queues. Existing solutions rely on rich statistics such as packet arrival rate and delay to realize dynamic bandwidth allocation. However, many statistics are difficult to accurately measure or even obtain in real switches due to resource limitations. Providing differentiated services for mixed flows under such restrictions is still a challenge. To solve this issue, this paper proposed a reinforcement learning-based scheduling optimization (RLSO) mechanism. First, mixed flows scheduling is modeled as the Markov decision process (MDP) and Q-learning is used to find the approximate optimal solution with a few statistics. Second, the solution space is compressed to reduce the complexity of the algorithm and adapt to the limited performance of switches. Finally, the performance of the proposed mechanism is evaluated on a hardware testbed with workloads that include coarsegrained and fine-grained flows. The results show that RLSO can effectively schedule mixed flows.

关键词： Schedules Q-learning Prototypes Packet loss Markov processes Channel allocation Throughput

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A Cooperative Multi-AUV Mobile Scheme with Optical Communication via DDPG Approach

A Cooperative Multi-AUV Mobile Scheme with Optical Communica...

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international Conference on parallel and distributed systems (ICPADS)

作者： Mengzhen Li Hanjiang Luo Xiang Li Rukhsana Ruby Hang Tao Kaishun Wu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Hong Kong University of Science and Technology (Guangzhou)

To implement high-speed wireless communication from the deep ocean to the sea surface leveraging multiple autonomous underwater vehicles (AUVs) with underwater wireless optical communication (UWOC) technique is an emerging and promising technology that enables real-time data collection for accurate underwater exploration and monitoring, e.g., coordinated moving targets tracking. However, multi-hop UWOC is more susceptible to beam misalignment and positional uncertainty caused by external interference in the harsh marine environments. To address these challenges, we design a cooperative movement scheme for multiple AUVs based on deep reinforcement learning (DRL) approach to realize robust and reliable optical communication under mobile target tracking scenarios. In this scheme, we first model the optical channel with optical noise and then analyze the link performance of multiple AUVs to meet the bit error rate (BER) requirements. Afterwards, we map the cooperative optical communication problem to a Markov decision process (MDP) by incorporating the extended Kalman filter (EKF) technique to enhance effective communication. Finally, we propose a cooperative control strategy for multiple mobile AUVs based on deep deterministic policy gradient (DDPG). Through extensive simulations, it is demonstrated that the proposed algorithm is effective in achieving reliable underwater optical communication under mobile scenarios.

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Implementation and Performance Evaluation of Memory System Using Addressable Cache for HPC Applications on HBM2 Equipped FPGAs 28th

Implementation and Performance Evaluation of Memory System...

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28th international European Conference on parallel and distributed Computing , Euro-Par 2022

作者： Fujita, Norihisa Kobayashi, Ryohei Yamaguchi, Yoshiki Boku, Taisuke Center for Computational Sciences University of Tsukuba Tsukuba Japan Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031312083

When we apply field programmable gate arrays (FPGAs) as HPC accelerators, their memory bandwidth presents a significant challenge because it is not comparable to those of other HPC accelerators. In this paper, we propose a memory system for HBM2-equipped FPGAs and HPC applications that uses block RAMs as an addressable cache implemented between HBM2 and an application. This architecture enables data transfer between HBM2 and the cache bulk and allows an application to utilize fast random access on BRAMs. This study demonstrates the implementation and performance evaluation of our new memory system for HPC and HBM2 on an FPGA. Furthermore, we describe the API that can be used to control this system from the host. We implement RISC-V cores in an FPGA as controllers to realize fine-grain data transfer control and to prevent overheads derived from the PCI Express bus. The proposed system is implemented on eight memory channels and achieves 102.7 GB/s of the bandwidth. It overcomes the memory bandwidth of conventional FPGA boards with four channels of DDR4 memory despite using only 8 of 32 channels of the HBM2. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data transfer

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DNS Covert Channel Attack Detection Based on Spatio-Temporal Feature Fusion

DNS Covert Channel Attack Detection Based on Spatio-Temporal...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Wei You Chunlei Zhao Chao Shan Guozhong Dong Jie Yu Yong Guan Tianjin Key Laboratory of Intelligent Computing and Software New Technology Tianjin University of Technology Tianjin China Hubei Branch of National Computer Network Emergency Response Technical Team Coordination Center of China HuBei China Department of New Networks Pengcheng Laboratory Shenzhen China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

An increasing number of Advanced Persistent Threat (APT) organizations are utilizing DNS (Domain Name System) covert channels to evade network intrusion detection systems and establish private authoritative servers for remote attacks. However, current detection models for DNS covert channels often have poor performance and limited generalization ability. To address these challenges, we propose a DNS covert channel attack detection method based on spatiotemporal feature fusion. This method combines the Interaction timing sequence features of DNS traffic with the spatial statistical features of domain names, and constructs a detection model called ConvSLSTM, which integrates a Convolutional Neural Network (CNN) with a stacked Long Short Term Memory (LSTM) network. CNN extracts local spatial features from traffic, while stacked LSTM captures Interaction timing sequence features, significantly improving detection accuracy. This article uses three typical public datasets for experiments, which contain a total of 440000 DNS covert channel attack data. Comparative experiments show that our method is significantly superior to existing methods and exhibits stronger generalization ability in detecting DNS covert channel attacks constructed by unknown tools.

关键词： Accuracy Protocols Feature extraction Timing Spatiotemporal phenomena Domain Name System Convolutional neural networks Servers Computer crime Long short term memory

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