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Research on Query Task Fragmentation in the Scenario of Storage and Compute Separation 2

Research on Query Task Fragmentation in the Scenario of Stor...

引用

2nd International conference on Intelligent Computing and Next Generation networks, ICNGN 2023

作者： Wang, Beilei Wang, Yulin Li, Yuanzhe Northeastern University Software College Shenyang China Northeastern University Medical and Bioinformatics Engineering College Shenyang China

ISBN: (纸本)9798350329070

The cloud-native database is one of the hottest topics in database research. Storage-compute separation architecture with cloud characteristics is designed to process complex and changeable workloads to reduce resource coupling for cloud-native databases. During processing query loads, the computing layer pushes the operator tasks in execution plans generated from loads down to the storage layer through the network. Query optimization techniques such as operator pushdown and parallel computing have been developed and perfected to improve query speed in storage and compute separation architecture. But at the same time, these technologies also increase the complexity of execution plans, leading to an increase in tasks. Moreover, the experiment shows that under the same query load, the cloud-native database executes a significantly greater number of tasks compared to the distributed database. Given the above phenomenon, the concept of query task fragmentation is proposed. From the view of operator pushdown and parallel computing query optimization, the presence of query task fragmentation is verified through experiments. Experimental results based on the TPC-H benchmark demonstrate that, compared to MySQL distributed cluster, the average number of operator tasks in the execution plan, generated through operator pushdown and parallel computing for the cloud-native databases, increases by 20.17% and 39.49%, respectively. Finally, the impact of task fragmentation is analyzed from the perspectives of technical research and the application of cloud-native databases. © 2023 IEEE.

关键词： Query processing

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Realistic Sketch Face Generation via Sketch-Guided Incomplete Restoration 29

Realistic Sketch Face Generation via Sketch-Guided Incomplet...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Liu, Zijie Gu, Chunhua Xie, Ying Zhang, Huichen Pei, Songwen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai China Traffic Management Research Institute of the Ministry of Public Security Key Laboratory of Ministry of Public Security for Road Traffic Safety Wuxi China

ISBN: (纸本)9798350330717

Generating realistic sketches of human faces is an important research direction in the fields of computer vision and computer graphics. However, generating high-quality sketch faces remains challenging, especially when dealing with incomplete input sketch images. In this paper, we propose a framework based on the U-Net generative network, which progressively optimizes network performance through a three-stage training strategy. We introduce the Sketch Guidance and Completion network (SGCN), which is capable of restoring missing features, completing blurry edges, and handling occluded regions in sketch images. By employing multiple discriminators and adversarial training strategies, we enhance the realism and preserve fine details in the generated results. Through experiments on publicly available sketch face datasets, we demonstrate significant improvements in reconstructing incomplete sketch faces using our approach. The generated images exhibit enhanced completeness, realism, and retain fine details and important facial features. Quantitative evaluations validate the effectiveness and superiority of our method. © 2023 IEEE.

关键词： Face synthesis GAN Sketch-based image synthesis

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Performance Evaluation of MindSpore and PyTorch based on Ascend NPU 29

Performance Evaluation of MindSpore and PyTorch Based on Asc...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Zhu, Zeling Wang, Bangchuan Yang, Chuying Zhu, Rui Zhou, Mingyao Zheng, Nenggan Ocean University of China School of Computer Science and Technology Qingdao China Zhejiang University Qiushi Academy for Advanced Studies School of Software Technology Hangzhou China Zhejiang University Qiushi Academy for Advanced Studies Hangzhou China Hangzhou Huawei Communication Technology Co. Ltd Hangzhou China

ISBN: (纸本)9798350330717

The development of deep learning depends on the support of deep learning processing units and frameworks. HUAWEI's Ascend Neural processing Unit (Ascend NPU), as a chip designed specifically for neural network computation acceleration, not only provides support for its self-developed framework MindSpore, but also provides adaptation for PyTorch. However, there is a lack of comparative evaluation research on MindSpore and other frameworks on Ascend NPU, making it difficult to understand their actual performance. Therefore, comprehensive evaluation experiments in both model level and operator level on MindSpore and PyTorch based on Ascend NPU are conducted in this paper, analyzing and comparing the performance of the two frameworks. With the conclusions analyzed on the model evaluation experiments and operator evaluation experiments, we provide references and suggestions for deep learning researchers and practitioners in terms of framework selection and optimization strategies for Ascend NPU. © 2023 IEEE.

关键词： Ascend NPU Deep Learning Framework High Performance Computing MindSpore PyTorch

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Performance study of HPC applications on an Arm-based cluster using a generic efficiency model 28

Performance study of HPC applications on an Arm-based cluste...

引用

28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Banchelli, Fabio Peiro, Kilian Querol, Andrea Ramirez-Gargallo, Guillem Ramirez-Miranda, Guillem Vinyals, Joan Vizcaino, Pablo Garcia-Gasulla, Marta Mantovani, Filippo Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781728165820

HPC systems and parallel applications are increasing their complexity. Therefore the possibility of easily study and project at large scale the performance of scientific applications is of paramount importance. In this paper we describe a performance analysis method and we apply it to four complex HPC applications. We perform our study on a pre-production HPC system powered by the latest Arm-based CPUs for HPC, the Marvell ThunderX2. For each application we spot inefficiencies and factors that limit their scalability. The results show that in several cases the bottlenecks do not come from the hardware but from the way applications are programmed or the way the system software is configured.

关键词： Performance analysis High Performance Computing parallel Applications Arm ThunderX2

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Exploring Wi-Fi Privacy Disclosure: A Novel Approach to User Identity Prediction based on Traffic Multi-level Information 29

Exploring Wi-Fi Privacy Disclosure: A Novel Approach to User...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Li, Yiwen Wang, Shanshan Chen, Zhenxiang Cao, Xueyang Han, Yadi University of Jinan School of Information Science and Engineering Jinan China Quan Cheng Laboratory Jinan China

ISBN: (纸本)9798350330717

Nowadays, people are accustomed to network communication via Wireless Fidelity (Wi-Fi) when using mobile phones and IoT devices in fixed places (such as homes and dormitories). This means that a large amount of Wi-Fi traffic data will be generated every day. This paper focuses on the leakage of user identity privacy in traffic, namely: can user identity attributes be inferred from real home Wi-Fi traffic data? If malicious attackers can obtain user identities so "conveniently", it means that the success rate of malicious activities such as phishing and fraud can be easily increased. Therefore, to explore this question, we propose a new method for predicting user attributes based on user network behavior similarity. Then we collected real world data for verification and compared it with other methods. Finally, this paper also analyzes the current response methods for Wi-Fi privacy disclosure and points out the problems and future research directions. © 2023 IEEE.

关键词： identity prediction network traffic privacy security

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A Dynamic Sharding Scheme For Blockchain based On Graph Partitioning 7

A Dynamic Sharding Scheme For Blockchain Based On Graph Part...

引用

7th IEEE International conference on Blockchain (IEEE Blockchain)

作者： Li, Huan Wang, Dongbin Zhi, Hui Wang, Yuchen Yang, Tao Song, Jiang Beijing Univ Post & Telecommun Sch Cyberspace Engn Beijing Peoples R China Natl Engn Lab Mobile Network Beijing Peoples R China TravelSky Technol Ltd Beijing Peoples R China Peng Cheng Lab Cyberspace Secur Res Ctr Shenzhen Peoples R China

ISBN: (纸本)9798350351606;9798350351590

Blockchain is a decentralized distributed ledger that is stored in a block chain, cannot be tampered with, is safe and trustworthy with the help of cryptography and consensus mechanism. However, the current performance and scalability of blockchain hinders its practical application in wider areas, so blockchain sharding technology was proposed. Sharding technology divides the blockchain into multiple shards. Each shard is responsible for processing a part of transactions and status. Multiple shards verify and process transactions in parallel, which improves the performance of the blockchain. Although sharding technology has potential in theory, in practical applications, it still faces a series of problems, such as shard load imbalance and a high proportion of cross-shard transactions. In order to solve the problems of unbalanced shard load and high proportion of cross-shard transactions in the blockchain sharding system, the DLDG (Dynamic Linear Deterministic Greedy) dynamic account division algorithm is proposed. DLDG constructs the shard load and cross-shard transaction ratio. The shard transaction rate balancing model periodically divides the account transaction network graph through historical transaction data, thereby achieving shard load balancing and reducing the cross-shard transaction rate. Experimental results show that this scheme alleviates performance problems caused by load imbalance and high cross-shard transaction rates.

关键词： blockchain sharding scalability

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Key Technologies of Media Big Data in-Depth Analysis System based on 5G Platform 5

Key Technologies of Media Big Data in-Depth Analysis System ...

引用

2022 5th International Symposium on Big Data and Applied Statistics, ISBDAS 2022

作者： Lin, Qiang Zhang, Xilin School of Maritime Economics and Management Dalian Maritime University Dalian116026 China Dalian University of Science and Technology Dalian116052 China

To meet the needs of large-scale users for personalized streaming media services with high speed, low delay, and high quality in a 5G mobile network environment, this paper studies the resource allocation mechanism of streaming media based on a 5G network from the perspective of user demand prediction, which can alleviate the pressure of mobile network, improve the utilization rate of streaming media resources and the quality of user service experience. The augmented reality visualization of large-scale social media data must rely on the computing power of distributed clusters. This paper constructs a distributed parallel processing framework in a high-performance cluster environment, which adopts a loosely coupled organizational structure. Each module can be combined, called, and expanded arbitrarily under the condition of following a unified interface. In this paper, the algebraic method of parallel computing algorithm is innovatively proposed to describe parallel processing tasks and organize and call large-scale data-parallel processing operators, which effectively supports the business requirements of large-scale parallel processing of large-scale spatial social media data and solves the bottleneck of large-scale spatial social media data-parallel processing. © Published under licence by IOP Publishing Ltd.

关键词： Wireless networks

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When CPN Meets AI: Resource Provisioning for Inference Query upon Computing Power network 29

When CPN Meets AI: Resource Provisioning for Inference Query...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Ji, Mingtao Qian, Zhuzhong Ye, Baoliu Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350330717

Performing machine learning inference at the network edge, named Edge Inference, showing benefits like low latency, reduced data traffic, and improved user privacy, has attracted massive attention. Computing Power network (CPN) creates opportunities for edge inference, but also poses multiple challenges for service providers, including computing power selections from different enterprises, the time-coupled decision for resource provisioning and the unpredictable CPN network status. To overcome these challenges, this study formulates a time-varying integer program problem whose goal is to minimize long-term costs, involving switching costs, operational costs, communication costs, and queuing costs. Then we design a group of polynomial-time online algorithms to make online decisions, by taking into account stochastic inputs. Our algorithms adaptively make control decisions by solving delicately constructed subproblems based on the inputs predicted via online learning. Specifically, we first obtain fractional solutions which are transformed into integers for deployment with expectations preserving. Furthermore, we conduct a rigorous proof and establish the competitive ratio which highlights the difference between the performance of our proposed algorithms and the offline optimum. Our comprehensive evaluations, using datasets from real systems, demonstrate that our algorithms outperform multiple alternatives, up to an average of 35% cost reduction, confirming the effectiveness of our algorithms. © 2023 IEEE.

关键词： Computing Power network Resource Provisioning

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ProMD: A Proactive Intrusion Response System for Enterprise network with Multi-Domain 21

ProMD: A Proactive Intrusion Response System for Enterprise ...

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： Leng, Siyuan Li, Fenghua Zhang, Lin Wen, Yitong Guo, Yunchuan Fang, Liang Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350329223

Enterprise networks are divided into sub-domain structures for resource aggregation and trust dependency. However, the complex domain structure results in delayed and incomplete defenses from Intrusion Response Systems(IRS). This paper proposes a proactive intrusion response system specifically designed for multi-domain networks called ProMD. A Bayesian Risk Graph is designed to analyze and evaluate the risks dynamically. The paper formalizes the selection of countermeasures as a multi-objective optimization problem (MOOP) and proves it is NP-Hard. Therefore a genetic-based heuristic algorithm OPGA is designed to generate optimal response policy sets and an unique policy is selected based on the Entropy Weight method. Experimental results demonstrate that the OPGA outperforms benchmark algorithms. © 2023 IEEE.

关键词： Risk assessment

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Rethinking Stateful Stream processing with RDMA 22

Rethinking Stateful Stream Processing with RDMA

引用

International conference on Management of Data (SIGMOD)

作者： Del Monte, Bonaventura Zeuch, Steffen Rabl, Tilmann Markl, Volker Tech Univ Berlin Berlin Germany DFKI GmbH Kaiserslautern Germany Potsdam Univ HPI Potsdam Germany

ISBN: (纸本)9781450392495

Remote Direct Memory Access (RDMA) hardware has bridged the gap between network and main memory speed and thus invalidated the common assumption that network is often the bottleneck in distributed data processing systems. However, high-speed networks do not provide "plug-and-play" performance (e.g., using IP-over-InfiniBand) and require a careful co-design of system and application logic. As a result, system designers need to rethink the architecture of their data management systems to benefit from RDMA acceleration. In this paper, we focus on the acceleration of stream processing engines, which is challenged by real-time constraints and state consistency guarantees. To this end, we propose Slash, a novel stream processing engine that uses high-speed networks and RDMA to efficiently execute distributed streaming computations. Slash embraces a processing model suited for RDMA acceleration and scales out by omitting the expensive data re-partitioning demands of scale-out SPEs. While scale-out SPEs rely on data re-partitioning to execute a query over many nodes, Slash uses RDMA to share mutable state among nodes. Overall, Slash achieves a throughput improvement up to two orders of magnitude over existing systems deployed on an InfiniBand network. Furthermore, it is up to a factor of 22 faster than a self-developed solution that relies on RDMA-based data repartitioning to scale out query processing.

关键词： stream management distributed and parallel databases

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