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DepotDB: Mitigate Write Amplification with Region Compaction on KV Stores for Periodic Write-Intensive Workloads 21

DepotDB: Mitigate Write Amplification with Region Compaction...

引用

21st IEEE international symposium on parallel and distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud Computing, 16th IEEE international Conference on Social Computing and Networking and 13th international Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Fan, Zhixin Zhang, Yaohui Zhou, Jiang Li, Bo Chen, Yong Wang, Weiping University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Texas Tech University TX United States

ISBN: (纸本)9798350329223

Modern key-value storage systems employ LSM-tree as the core data structure for data management, but often suffer from high write amplification. It can further lead to write stall in which application throughput dramatically drops to nearly zero. To solve the problem, existing works mainly focus on the key-value separation solution to reduce levels and compaction data amount, or adopt SSTables partition strategies for fine-grained merging. However, they still have limitations due to continuous, small compactions, especially in facing noticeably periodic write-intensive workloads. In this paper, we exploit non-volatile memory (NVM) to address the issue and propose DepotDB, a new LSM-tree based KV store with multi-tier DRAM-NVM-SSD architecture. DepotDB's design principles include performing fast and efficient region compaction to reduce write amplification, while deferring write stalls to periods of low write load for performance efficiency. To achieve it, DepotDB first organizes key-value pairs to reduce the depth of LSM-tree with one NVM level and one SSD level thus mitigating write amplification. Then it divides data in each level into various regions and conducts region compaction explicitly when workloads are light. This can further substantially decrease the frequency and data amount of compaction, while alleviating write stall. We implement DepotDB based on LevelDB and conduct extensive experiments on a hybrid system. Extensive experimental results show that DepotDB achieves 1.1-1.83 × higher random write performance and 1.7-2.3 × higher random read performance compared to the SOTA LSM-based KVS MatrixKV, respectively. Moreover, DepotDB reduces write amplification by 30% compared to MatrixKV, and reduces its average write latency by 60% correspondingly. © 2023 IEEE.

关键词： Compaction

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Drill: Log-based Anomaly Detection for Large-scale Storage systems Using Source Code Analysis

Drill: Log-based Anomaly Detection for Large-scale Storage S...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Di Zhang Chris Egersdoerfer Tabassum Mahmud Mai Zheng Dong Dai Computer Science Department University of North Carolina at Charlotte Department of Electrical and Computer Engineering Iowa State University

Large-scale storage systems, a critical part of modern computing systems, are subject to various runtime bugs, failures, and anomalies in production. Identifying their anomalies at runtime is thus critical for users and administrators. Since runtime logs record the important status of the systems, log-based anomaly detection has been studied extensively for timely identifying system malfunctions. However, existing log-based anomaly detection solutions share common limitations in representing log entries accurately and robustly, hence can not effectively handle log entries that were not seen in the historical logs, which is a common real-world scenario due to logs' inherent rarity and the continuous evolution of the systems. To address the issues of existing methods, we propose Drill, a new log pre-processing method to generate high-quality vector representation of runtime logs by leveraging both storage system-specific sentiment-classifying language models and log contexts built from the source code. Through extensive evaluations of two representative distributed storage systems (Apache HDFS and Lustre), we show that Drill can achieve up to 41% improvement when compared with state-of-the-art anomaly detection solutions, showing it is a promising solution for general anomaly detection.

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Fairness with adaptive weight in network attack detection

Fairness with adaptive weight in network attack detection

引用

international Conference on parallel and distributed systems (ICPADS)

作者： Xuezhi Wen Nan Wang Yuanlin Sun Fan Xu Dalin Zhang Xibin Zhao School of Software Engineering Beijing Jiaotong University Beijing China School of Computer Science and Technology University of Science and Technology of China Anhui China School of Software Engineering Tsinghua University Beijing China

Network attacks aim to exploit vulnerabilities inherent in network protocols, which is widely used in many real-world applications. In the process of network anomaly detection, most methods train the model by minimizing the average empirical risk of all samples. However, due to the uneven distribution of samples from different protocols, detection models tend to be biased against minority protocols groups. To address this issue, we propose an adaptive weight assignment method for network attack detection, which emphasizes more on error-prone samples in prediction and enhances adequate representation of minority groups for fairness. We conduct experiments on two widely used datasets, KDD and NSLKDD. According to the results, our method achieves better performance than state-of-the-art methods for classification and regression tasks, and is robust to label noise in the test dataset.

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Adaptive Spatially Aware I/O for Multiresolution Particle Data Layouts 35

Adaptive Spatially Aware I/O for Multiresolution Particle Da...

引用

35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Usher, Will Huang, Xuan Petruzza, Steve Kumar, Sidharth Slattery, Stuart R. Reeve, Sam T. Wang, Feng Johnson, Chris R. Pascucci, Valerio Univ Utah SCI Inst Salt Lake City UT 84112 USA Utah State Univ Logan UT 84322 USA Univ Alabama Birmingham Birmingham AL USA Oak Ridge Natl Lab Oak Ridge TN USA Lawrence Livermore Natl Lab Livermore CA USA

ISBN: (纸本)9781665440660

Large-scale simulations on nonuniform particle distributions that evolve over time are widely used in cosmology, molecular dynamics, and engineering. Such data are often saved in an unstructured format that neither preserves spatial locality nor provides metadata for accelerating spatial or attribute subset queries, leading to poor performance of visualization tasks. Furthermore, the parallel I/O strategy used typically writes a file per process or a single shared file, neither of which is portable or scalable across different HPC systems. We present a portable technique for scalable, spatially aware adaptive aggregation that preserves spatial locality in the output. We evaluate our approach on two supercomputers, Stampede2 and Summit, and demonstrate that it outperforms prior approaches at scale, achieving up to 2.5v faster writes and reads for nonuniform distributions. Furthermore, the layout written by our method is directly suitable for visual analytics, supporting low-latency reads and attribute-based filtering with little overhead.

关键词： parallel I/O Load Balancing

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Design of Transcranial Pulse Electrical Stimulator Based on Virtual Instrumentation

Design of Transcranial Pulse Electrical Stimulator Based on ...

引用

Information Science, parallel and distributed systems (ISPDS), international Conference on

作者： Weilong Ma Hongda Li School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

Transcranial electrical stimulation is a non-invasive neuromodulation technique that affects the brain's activity patterns by stimulating specific areas of the cerebral cortex with low-intensity electrical currents that modulate neuronal excitability and synaptic transmission [1] . Traditional transcranial stimulation instruments are expensive, bulky and have poor portability. In this paper, a new type of portable transcranial instrument is developed, using LabVIEW to develop the upper computer system, the output electrical stimulation parameters are controlled by the upper computer, and the lower computer uses the STM32 microcontroller with low power consumption as the main control chip, and the upper computer software displays the parameter information during operation. The experiments show that the instrument is characterized by strong controllability, high portability and stability, and has high practical value.

关键词： Information science Power demand Cerebral cortex Microcontrollers Instruments Electrical stimulation Controllability

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Smooth Switching Control Strategy of parallel-type Power Conversion System Off-grid

Smooth Switching Control Strategy of Parallel-type Power Con...

引用

IEEE international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Hao He Jingyuan Xu Hongyang Qing Chunjiang Zhang College of Electrical Engineering Yanshan University Qinhuangdao City China Changli County Power Supply Branch State Grid Jibei Electric Power Co. Ltd Qinhuangdao City China

The power conversion system (PCS) based on droop control is voltage control in parallel. The island and grid-connected operation of the PCS are controlled by turning off and on the grid-side static transfer switch (STS). When the grid fails suddenly, the STS breaking command is issued. The PCS runs in island through the breaking process, but a longer time voltage shock, power and frequency oscillation will occur after breaking process is over. In order to solve above problems and achieve smooth transition of parallel PCS from grid-connected to island mode, a new type feedback link of voltage and current controller is designed in this paper. It combines the differential and integral controllers with droop controller to achieve smooth switching from grid-connected to island mode. In addition, the stability of the improved system is analyzed through root locus. The simulation results verify that the proposed control strategy can achieve a smooth transition from grid-connection to island mode.

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The Metaverse: Challenges and Opportunities for AI to Shape the Virtual Future

The Metaverse: Challenges and Opportunities for AI to Shape ...

引用

ACIS international Conference on software engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Vincenzo De Masi Qinke Di Siyi Li Yuhan Song School of Creative and Culture Beijing Normal University - Hong Kong Baptist University United International College Zhuhai China School of Computer and Information Engineering Guangdong Songshan Polytechnic Shaoguan China Media Communication Hong Kong Baptist University Hong Kong China Fine Arts CGA New York Institute of Technology New York US

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

The metaverse, a concept of an immersive and interconnected virtual world, is rapidly gaining traction as the next frontier in digital interaction. This article explores the critical role of artificial intelligence (AI) in shaping the future of the metaverse by addressing key challenges such as interoperability and synchronization. Drawing on recent data and case studies, including Roblox’s success and the rise of AI-powered platforms like Crucible and Improbable, the article examines how AI can enable seamless experiences across virtual worlds and optimize network infrastructure for real-time, shared experiences. The article also discusses the ethical and social implications of AI in the metaverse and emphasizes the need for collaborative efforts to ensure responsible innovation. As the boundaries between the physical and virtual worlds blur, the AI-enabled metaverse has the potential to revolutionize various aspects of human life, from education and entertainment to scientific exploration and problem-solving.

关键词： Technological innovation Ethics Metaverse Shape Real-time systems Synchronization Problem-solving

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A Guaranteed Approximation Algorithm for Scheduling Fork-Joins with Communication Delay

A Guaranteed Approximation Algorithm for Scheduling Fork-Joi...

引用

international symposium on parallel and distributed Processing (IPDPS)

作者： Pierre-François Dutot Yeu-Shin Fu Nikhil Prasad Oliver Sinnen CNRS Inria Grenoble INP* LIG Univ. Grenoble Alpes Grenoble France Dept. of Electrical Computer & Software Engineering Parallel and Reconfigurable Computing Lab University of Auckland Auckland New Zealand

Scheduling task graphs with communication delay is a widely studied NP-hard problem. Many heuristics have been proposed, but there is no constant approximation algorithm for this classic model. In this paper, we focus on the scheduling of the important class of fork-join task graphs (describing many types of common computations) on homogeneous processors. For this sub-case, we propose a guaranteed algorithm with a $\left( {1 + \frac{m}{{m - 1}}} \right)$-approximation factor, where m is the number of processors. The algorithm is not only the first constant approximation for an important sub-domain of the classic scheduling problem, it is also a practical algorithm that can obtain shorter makespans than known heuristics. To demonstrate this, we propose adaptations of known scheduling heuristic for the specific fork-join structure. In an extensive evaluation, we then implemented these algorithms and scheduled many fork-join graphs with up to thousands of tasks and various computation time distributions on up to hundreds of processors. Comparing the obtained results demonstrates the competitive nature of the proposed approximation algorithm.

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An acceleration method of optical flow calculation for automotive ADAS 20

An acceleration method of optical flow calculation for autom...

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20th IEEE Int symposium on parallel and distributed Processing with Applicat / 15th IEEE Int Conf on Social Comp and Networking / 12th IEEE Int Conf on Big Data and Cloud Comp / 12th IEEE Int Conf on Sustainable Comp and Commun

作者： Chen, Wenjie Sun, Ruixin Chai, Zhilei East China Normal Univ MoE Engn Res Ctr Software Hardware Codesign Techn Shanghai Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Jiangsu Peoples R China

ISBN: (纸本)9781665464970

An essential component of advanced driver assistance systems (ADAS) is the real-time processing of various image types that are perceived by multiple sources. Although the optical flow is a crucial tool for determining how the camera (or vehicle) is moving in relation to its surroundings, due to the amount of computation involved, it is challenging to use it in real-time. Some of the existing fast optical flow calculation techniques are typically run on a server, which is large, expensive, and consumes a lot of power, making it difficult to mount on a real vehicle. This paper proposes an optimization technique appropriate for embedded GPUs mounted on vehicles that targets the pyramid LK optical flow method. A new dynamic window design strategy based on GPU architecture is proposed. By localizing the feature extraction algorithm to reduce the time-consuming sorting stage, improving the bidirectional constraint evaluation, and ending the iteration as soon as possible, the computational bit width is reduced, improving overall performance. Experiments with a real vehicle scene in the simultaneous localization and mapping (SLAM) system show that our approach meets the real-time requirement with 30 frames per second and improves performance by 3.8 times compared to the GPU-optimized version of OpenCV.

关键词： optical flow ADAS (advanced driver assistance system) GPU SLAM (simultaneous localization and mapping)

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Balance Graphs: An Aid for Studying Convolutional Neural Networks 24

Balance Graphs: An Aid for Studying Convolutional Neural Net...

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24th IEEE/ACIS international Winter Conference on software engineering, Artificial Intelligence, Networking and parallel/distributed Computing, SNPD 2022

作者： Embery, Lyell Ignatious, Eva Azam, Sami Jonkman, Miriam De Boer, Friso College of Engineering and It Charles Darwin University Casuarina Australia

ISBN: (纸本)9798350310412

Deep Learning Neural Networks offer a powerful tool to process visual data and to make decisions, but a limitation is its black box nature which offers low transparency to human examiners. This hurdle presents a particular challenge in using Neural Networks in safety-critical systems, which require high performance and transparency such as medical diagnosis of life-threatening diseases. This paper seeks to build and test a neural network for grading gliomas of comparable function and complexity to others in the field, then to apply Data Visualisation techniques to render the internal workings of the NN more understandable to a human observer. The purpose is to develop a system that can classify brain tumors into low-grade gliomas (LGG) and high-grade gliomas (HGG), to aid with diagnosis and prognosis The Brain Tumor Segmentation Challenge 2020 (BraTS2020) data set was used, with data categorised based on a combination of grade assigned in BraTS2020, and the labels in the segmentation data. As some categories are over-represented, methods were employed to ensure a better balance between different categories. Data augmentation was used to expand the limited number of scans in the BraTS2020. A 3D convolution neural network (CNN) was constructed to grade gliomas. With the method developed in this paper, an accuracy of 94.1% was achieved. A newly devised method to visually represent the weights of a convolution is explored. These graphs, called 'weight graphs' allow convolutions to be condensed into a visual medium. The weight graph is designed for easy visual interpretation of the weights assigned within a particular convolution. To overcome the limitations of weight graphs, an alternate graph was devised, called a balance graph, because it shows the overall balance of weights in a kernel, allowing for a quick impression of what effect a single kernel has. It is demonstrated that Balance Graphs improve the accessibility and transparency of the of the weights in convolut

关键词： Convolution

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