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Research on Efficient parallelization of Spectral Clustering Algorithm based on Big Data

Research on Efficient Parallelization of Spectral Clustering...

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Electrical Engineering, Big Data and Algorithms (EEBDA), IEEE International conference on

作者： Chaoyue Wei Tianjin University of Technology Tianjin China

Cluster analysis is an important research direction in data mining. In the past decades, a large number of clustering algorithms have been developed, among which spectral clustering has been widely used because of its excellent performance in nonlinear separable data. In addition, with the continuous development of the Internet, more and more data are generated in the network, forming big data. Therefore, how to apply spectral clustering algorithm to big data and mine useful information has become a very important research topic. With the explosion of data scale, some industries have reached terabytes or even petabytes of data. The storage capacity and computing capacity of a single computer can no longer meet the requirements. At the same time, the spectral clustering algorithm will spend a lot of time in the process of processing massive data, which seriously limits the efficiency of the cluster analysis of mass data by the clustering algorithm in tomorrow. Therefore, improving the computational efficiency of the spectral clustering algorithm has become a research focus. In addition to using sampling method to optimize spectral clustering algorithm and reduce its computational complexity, with the emergence and wide application of various distributed parallel computing frameworks such as MapReduce and Spark, researchers have gradually focused on the distributed parallelization of spectral clustering algorithm. In the big data environment, the parallelization of spectral clustering algorithm is realized. It can not only obtain better clustering effect, but also improve the efficiency of spectral clustering algorithm for largescale cluster analysis. For the massive data of various industries, how to improve the large-scale clustering of spectral clustering algorithm. It is of great practical significance to analyze efficiency so as to improve work efficiency. According to the problem of long running time of spectral clustering algorithm in processing large-scale dat

关键词： Industries Heuristic algorithms Linear regression Clustering algorithms parallel processing Big Data Partitioning algorithms

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Should PARSEC Benchmarks be More Parametric? A Case Study with Dedup 27

Should PARSEC Benchmarks be More Parametric? A Case Study wi...

引用

27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Maron, Carlos A. F. Vogel, Adriano Griebler, Dalvan Fernandes, Luiz Gustavo Pontifical Catholic Univ Rio Grande do Sul PUCRS Sch Technol Porto Alegre RS Brazil Tres de Maio Fac SETREM LARCC Tres De Maio Brazil

ISBN: (纸本)9781728116440

parallel applications of the same domain can present similar patterns of behavior and characteristics. Characterizing common application behaviors can help for understanding performance aspects in the real-world scenario. One way to better understand and evaluate applications' characteristics is by using customizable/parametric benchmarks that enable users to represent important characteristics at run-time. We observed that parameterization techniques should be better exploited in the available benchmarks, especially on stream processing domain. For instance, although widely used, the stream processing benchmarks available in PARSEC do not support the simulation and evaluation of relevant and modern characteristics. Therefore, our goal is to identify the stream parallelism characteristics present in PARSEC. We also implemented a ready to use parameterization support and evaluated the application behaviors considering relevant performance metrics for stream parallelism (service time, throughput, latency). We choose Dedup to be our case study. The experimental results have shown performance improvements in our parameterization support for Dedup. Moreover, this support increased the customization space for benchmark users, which is simple to use. In the future, our solution can be potentially explored on different parallel architectures and parallel programming frameworks.

关键词： Benchmark testing parallel processing Microsoft Windows Streaming media Computer architecture Measurement Throughput

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New Scheduling Strategy based on Multi-Criteria Decision Algorithm 27

New Scheduling Strategy based on Multi-Criteria Decision Alg...

引用

27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Menouer, Tarek Darmon, Patrice UMANIS Levallois Perret France

ISBN: (纸本)9781728116440

This paper presents a new scheduling strategy proposed to optimize the scheduling of several containers submitted online by users in a private infrastructure of nodes i.e. a cloud platform. In the literature, several scheduling frameworks and studies are proposed. The majority of these works use a scheduling strategy based on one criterion, such as Spread and Bin Packing strategies. The Spread strategy consists to select the node having the least number of executed containers to balance the containers load between all nodes of the infrastructure. The Bin Packing strategy consists to select the most compacted node in terms of resources to reduce the number of used nodes of the infrastructure. However, the submitted containers are defined according to a multi-criteria, such as the number of used CPUs and the used memory size. The state of each node is also defined according to a multi-criteria, such as the number of executed containers, the number of available CPUs and the size of available memory. The novelty of our scheduling strategy is to choose the node that executes a container by combining the Spread and the Bin Packing principles using the Technique for the Order of Prioritisation by Similarity to Ideal Solution (TOPSIS) algorithm. TOPSIS is a multi-criteria decision analysis algorithm. Our proposed strategy is implemented in Docker Swarm. Docker swarm is an important container scheduler framework developed by Docker. Experiments demonstrate the potential of our strategy under different scenarios.

关键词： Container Technology Cloud Computing Scheduling Strategy

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3DEP: A Efficient Routing Algorithm to Evenly Distribute Traffic over 3D network-on-Chips 27

3DEP: A Efficient Routing Algorithm to Evenly Distribute Tra...

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27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Vahdatpanah, Fatemeh Elahi, Mahdi Kashi, Somayeh Taheri, Ebadollah Patooghy, Ahmad Iran Univ Sci & Technol Sch Comp Engn Tehran Iran Univ Cent Arkansas Dept Comp Sci Conway AR 72035 USA

ISBN: (纸本)9781728116440

Due to high manufacturing cost of Through Silicon Via (TSV) in 3D network-on-Chips (NoCs), not every router is vertically connected. In most 3D NoCs only a subset of TSVs are contrived which results into incomplete 3D NoCs in the vertical dimension. This irregularity introduces new complexity in the design of efficient routing algorithms for partially connected 3D NoCs. In this paper, we propose an efficient routing algorithm to evenly distribute traffic in incomplete 3D NoCs. The proposed algorithm uses turn model analysis to categorize layers, columns, and rows of the NoC into different groups. Then, specific turns are prohibited in each group such that the whole routing is deadlock free, livelock free, and independent of the location of TSVs over the network. By finding the best combination for prohibited turns over the network, we limited the number of required virtual channels to two virtual channel per each physical channel. Simulation results show that the proposed partially adaptive routing has improved packet latency by 32.8% in comparison with Elevator-First algorithm. The advantage of the proposed algorithm is that it shows more improvements on the packet latency and network throughput when the size of network grows.

关键词： Turn Model Routing Algorithm 3D network-on-Chip Deadlock Partially Adaptive

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Subdomain Adaptive Learning network for Cross-Domain Human Activities Recognition Using WiFi with CSI 27

Subdomain Adaptive Learning Network for Cross-Domain Human A...

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

作者： Li, Lin Wang, Lei Han, Bin Lu, Xinxin Zhou, Zhiyi Lu, Bingxian School of Software Dalian University of Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China

ISBN: (纸本)9781665408783

WiFi-based human activity recognition has been widely used in many fields such as health diagnosis, intrusion detection and smart home. Most existing recognition methods can achieve a satisfying accuracy only in one domain, but low accuracy occurs when models are trained in source domain but are used in target domain. Meanwhile, considering finetuning network directly is impossible or easy to overfit with limited labeled target data, transfer learning based methods with domain adaptive layers are proposed to solve above problems but just aligning marginal distribution, which may lose massive fine-grained features. based on this, we present an end-to-end deep subdomain adaptive network based activities recognition (DSANAR) using Channel State Information (CSI) that aligns marginal and matches conditional distribution simultaneously for more fine-grained features in each category of relevant subdomains based on a local maximum mean discrepancy (LMMD). Besides, by using a joint cross-entropy and an adaptive loss as training loss, DSANAR outperforms other state-of-art methods on an autonomous dataset with average 95.6% cross-domain accuracy. © 2021 IEEE.

关键词： Channel state information

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Pragma-oriented parallelization of the Direct Sparse Odometry SLAM Algorithm 27

Pragma-oriented Parallelization of the Direct Sparse Odometr...

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27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Pereira, C. Falcao, G. Alexandre, L. A. Univ Coimbra Dept Elect & Comp Engn Inst Telecomunicacoes Coimbra Portugal Univ Beira Interior Dept Informat Covilha Portugal Inst Telecomunicacoes Covilha Portugal

ISBN: (纸本)9781728116440

Monocular 3D reconstruction is a challenging computer vision task that becomes even more stimulating when we aim at real-time performance. One way to obtain 3D reconstruction maps is through the use of Simultaneous Localization and Mapping (SLAM), a recurrent engineering problem, mainly in the area of robotics. It consists of building and updating a consistent map of the unknown environment and, simultaneously, saving the pose of the robot, or the camera, at every given time instant. A variety of algorithms has been proposed to address this problem, namely the Large Scale Direct Monocular SLAM (LSD-SLAM), ORB-SLAM, Direct Sparse Odometry (DSO) or parallel Tracking and Mapping (PTAM), among others. However, despite the fact that these algorithms provide good results, they are computationally intensive. Hence, in this paper, we propose a modified version of DSO SLAM, which implements code parallelization techniques using OpenMP, an API for introducing parallelism in C, C++ and Fortran programs, that supports multi-platform shared memory multi-processing programming. With this approach we propose multiple directive-based code modifications, in order to make the SLAM algorithm execute considerably faster. The performance of the proposed solution was evaluated on standard datasets and provides speedups above 40% without significant extra parallel programming effort.

关键词： parallel Computing Open Multi-processing (OpenMP) Multiprocessing Simultaneous Localization and Mapping (SLAM)

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TCN-Log2Vec: A Comprehensive Log Anomaly Detection Framework based on Optimized Log Parsing and Temporal Convolutional network

TCN-Log2Vec: A Comprehensive Log Anomaly Detection Framework...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International conference on

作者： Zhe Jiang Yali Gao Jie Yuan Kaiguo Yuan Xiaoyong Li Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education College of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

Logs that record execution information and system status, are useful for anomaly detection. Traditionally, developers manually checked logs through keyword search or rule matching. However, as the number of logs increases, this approach becomes unrealistic. More and more methods of log anomaly detection based on machine learning and deep learning have been proposed. In order to detect log anomalies accurately and effectively, this paper proposes a comprehensive framework, namely TCN-Log2Vec. based on Drain, a prevalent and effective method to extract the log templates from the original logs, TCN-Log2Vec has done some optimization work in log parsing. It also captures sequence information, quantitative information, and semantic information from the original log. Meanwhile, it designs the anomaly detection module based on TCN (Temporal Convolutional network) to achieve parallel processing. Our log parsing method achieves 99% accuracy on HDFS datasets and 94% accuracy on BGL datasets. We compare our anomaly detection framework with other advanced methods including DeepLog, LogAnomaly, and LogRobust, the experiment results show that our model has better performance.

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USING parallel distributed processing TO REDUCE THE COMPUTATIONAL TIME OF DIGITAL MEDIA SIMILARITY MEASURES 17th

USING PARALLEL DISTRIBUTED PROCESSING TO REDUCE THE COMPUTAT...

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17th IFIP WG 11.9 International conference on Digital Forensics

作者： Lim, Myeong Jones, James Information Technology from George Mason University Fairfax VA United States Digital Forensics and Director of the Criminal Investigations and Network Analysis Center at George Mason University Fairfax VA United States

ISBN: (纸本)9783030883812;9783030883805

Digital forensic practitioners are constantly challenged to find the best allocation of their limited resources. While automation will continue to partially mitigate this problem, the preliminary question about which media should be prioritized for examination is largely unsolved. Previous research has developed methods for assessing digital media similarity that may aid in prioritization decisions. Similarity measures may also be used to establish links between media and, by extension, the individuals or organizations associated with the media. However, similarity measures have high computational costs that delay the identification of digital media warranting immediate attention and render link establishment across large collections of data impractical. This chapter presents and validates a method for parallelizing the computations of digital media similarity measures to reduce the time requirements. The proposed method partitions digital media and distributes the computations across multiple processors. It then combines the results as an overall similarity measure that preserves the accuracy of the original method executed on a single processor. Experiments on a limited dataset demonstrate reductions of up to 51% in processing time. The reductions vary based on the number of partitions chosen and specific digital media being examined, suggesting the need for additional testing and optimization strategies.

关键词： Drive similarity sector hashes Jaccard index parallel computation

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Deep Reinforcement Learning based Dynamic Channel Bonding for Wi-Fi networks

Deep Reinforcement Learning based Dynamic Channel Bonding fo...

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

作者： Hao Chen Peng Liu Lizhao You Ziyang Guo Jiajun Luo Xinghua Sun Taotao Wang Liqun Fu Department of Information and Communication Engineering Xiamen University China Shenzhen Research Institute of Xiamen University Shenzhen China Wireless Technology Lab 2012 Laboratories Huawei Technologies Co. Ltd China School of Electronics and Communication Engineering Sun Yat-sen University China College of Electronics and Information Engineering Shenzhen University Shenzhen China

This paper presents a decentralized Deep Reinforcement Learning (DRL)-based dynamic channel bonding (DCB) algorithm (i.e., drlDCB) for Wi-Fi networks. Most existing RL-based channel bonding algorithms either are centralized (i.e., requiring a central controller and global information for training), or can easily fall into local optimum (e.g., over-using channels for demand) as the number of stations increases. Moreover, they may converge slowly due to the use of measured throughput or delay over a duration as reward. In contrast, drlDCB is a purely distributed algorithm where each access point gets trained using local information, and addresses the local optimum and the slow convergence problems through the following new designs: 1) a new piecewise throughput model to avoid occupying too many channels if demand is satisfied, and 2) a new reward design using estimated throughput. The use of estimated throughput allows per-frame adaptation and fast convergence speed, while the accurate estimation generalizes the application scenarios, allowing an offline-trained model to be used in a dynamic environment. Simulation results in the static scenario with fixed loads and the dynamic scenario with random loads show that drlDCB outperforms state-of-the-art DCB algorithms in terms of network throughput, delay and convergence speed.

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Catalina: In-Storage processing Acceleration for Scalable Big Data Analytics 27

Catalina: In-Storage Processing Acceleration for Scalable Bi...

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27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Torabzadehkashi, Mahdi Rezaei, Siavash Heydarigorji, Ali Bobarshad, Hosein Alves, Vladimir Bagherzadeh, Nader Univ Calif Irvine Engn Sch EECS Irvine CA 92717 USA NGD Syst Inc Irvine CA 92618 USA Univ Calif Irvine Donald Bren Sch ICS Irvine CA USA

ISBN: (纸本)9781728116440

Cloud applications are increasingly playing a crucial role in big data analytics. New use cases such as autonomous cars and edge computing call for novel approaches mixing heterogeneous computing and machine learning. These applications typically process petabyte-scale datasets, therefore, requiring low-power and scalable storage providing low-latency and high-throughput data access. While data centers have been focusing on migrating from legacy HDDs and SATA SSDs by deploying high-throughput and low-latency NVMe SSDs, the data bottlenecks appear as capacity scales. One approach to tackle this problem is to enable processing to happen within the storage device -in-storage processing ( ISP)-eliminating the need to move the data. In this paper, we investigated the deployment of storage units with embedded low-power application processors along with FPGA-based reconfigurable hardware accelerators to address both performance and energy efficiency. To this purpose, we developed a high-capacity solid-state drive ( SSD) named Catalina equipped with a quad-core ARM A53 processor running a Linux operating system along with a highly efficient FPGA accelerator for running applications in-place. We evaluated our proposed approach on a case study application for a similarity search library called Faiss.

关键词： Computational storage In-storage processing near-data processing System-on-Chip FPGA Accelerator Big data

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