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共 11234 条记录，以下是461-470 订阅

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Maximizing Influence of Nodes with Rapid Global Spread in Social Media Data Strea 22

Maximizing Influence of Nodes with Rapid Global Spread in So...

引用

22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Tian, Ze-Peng Shi, Lei-Lei Liu, Lu Han, Zi-Xuan Hao, Zhou Daniel Antonopoulos, Nick Jiangsu University School of Computer Science and Communication Engineering China University of Exeter Department of Computer Science United Kingdom University of Leicester School of Computing and Mathematical Science United Kingdom Edinburgh Napier University Principal's Office United Kingdom

ISBN: (纸本)9798331509712

Influence maximization plays a pivotal role in areas such as cybersecurity, public opinion management, and viral marketing. However, conventional methods for influence maximization grapple with challenges such as seed node clustering, information loss during influence propagation, and biases in overlapping influence consideration. In response to these issues, we introduce Fast Unfold Diffusion (FUD)- an influence maximization algorithm capitalizing on the uniform distribution characteristics in node space and the non-overlapping local features of nodes. This approach aims to facilitate swift and extensive global diffusion of node influence. The performance of our algorithm was validated through the execution of two hundred diffusion simulations, employing the Independent Cascade (IC) diffusion model across six real-world datasets. Compared to the latest algorithms, our algorithm offers substantial benefits in expanding the range of influence diffusion, the speed of influence diffusion and the stability of the algorithm. This not only effectively mitigates the problems found in traditional methodologies but also ensures a significant enhancement in the efficiency of influence maximization. © 2024 IEEE.

关键词： Expectation maximization algorithm

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Dynamic Task Scheduling for Coordinated Truck-Drone Parcel Delivery: A Hybrid Genetic Tabu Algorithm Approach 22

Dynamic Task Scheduling for Coordinated Truck-Drone Parcel D...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Li, Ruitai Yuan, Quan Liao, Tongxin Luo, Lijun Hu, Menglan Lai, Pan Zhang, Xiao South-Central Minzu University College of Computer Science China State Ethnic Affairs Commission Key Laboratory of Cyber-Physical Fusion Intelligent Computing China Huazhong University of Science and Technology School of Electronic Information and Communications China Hubei Key Laboratory of Smart Internet Technology China

ISBN: (纸本)9798331509712

With the rapid growth of the on-demand economy, logistics companies and merchants increasingly struggle to meet customer demands in dynamic and uncertain conditions. This paper studies the coordinated delivery of parcels by trucks and drones under such demands, proposing a Dynamic Task Scheduling Algorithm based on Hybrid Genetic Tabu algorithm (DTSAGT) for route optimization. Simulating dynamic customer demands with a Poisson distribution and statistical methods, the algorithm addresses timeliness issues due to variations in customer needs. It optimizes drone path planning and task allocation considering drone endurance and payload limits to minimize total delivery time. The algorithm includes three steps: initial solution construction, iterative optimization, and dynamic operations. Experimental results show that DTSAGT reduces the total service time by 15.05%, 34.13%, and 35.71% on average compared to the baseline algorithms. This paper's contribution is the combination of hybrid genetic and tabu search algorithms applied to dynamic task scheduling in truck-drone delivery, enhancing logistics efficiency. © 2024 IEEE.

关键词： Poisson distribution

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Highly Scalable Large-Scale Asynchronous Graph Processing using Actors 23

Highly Scalable Large-Scale Asynchronous Graph Processing us...

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23rd IEEE/ACM international symposium on Cluster, Cloud and Internet computing (CCGrid)

作者： Elmougy, Youssef Hayashi, Akihiro Sarkar, Vivek Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350302080

With the accelerating growth of Big Data, real-world graph processing applications now need to tackle graphs with billions of vertices and trillions of edges, thereby increasing the demand for effective solutions to application scalability. Unfortunately, current approaches to implementing these applications on modern HPC systems exhibit poor scale-out performance with increasing numbers of nodes. The scalability challenges for these applications are driven by large data sizes, synchronization overheads, and fine-grained communications with irregular data accesses and poor locality. This paper presents the scalability of a novel Actor-based programming system, which provides a lightweight runtime that supports fine-grained asynchronous execution and automatic message aggregation atop a Partitioned Global Address Space (PGAS) communication layer. Evaluations of the Jaccard Index and PageRank applications on the NERSC Perlmutter system demonstrate nearly perfect scaling up to 1, 000 nodes and 64K cores (one-third of the targeted 3000-nodes for Perlmutter). In addition, our Actor-based implementations of Jaccard Index and PageRank executed with parallel efficiencies of 85.7% and 63.4% for the largest run of 64K cores. This performance represents a 29.6x speedup relative to UPC and OpenSHMEM versions of PageRank.

关键词： parallel Graph Processing Actors Selectors distributed Systems Shared Memory PGAS OpenSHMEM

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The 33rd Heterogeneity in computing Workshop (HCW)

The 33rd Heterogeneity in Computing Workshop (HCW)

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

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Research and Application of Three-Dimensional Point Cloud Data Analysis Technology for Smart Grid Transmission Data Based on Cloud computing 3rd

Research and Application of Three-Dimensional Point Cloud Da...

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3rd international symposium on New Energy and Electrical Technology (ISNEET)

作者： Guo, Jinchao Rao, Chengcheng Chen, Yun Guangdong Power Grid Co Ltd Machine Patrol Management Ctr Guangzhou 521000 Peoples R China

ISBN: (纸本)9789819905553;9789819905539;9789819905522

Big data is an important product of the information age. Integrating big data into smart grid applications and correctly grasping the key technologies of big data can effectively promote the sustainable development of power industry and the construction of strong smart grid. As far as modern smart grid is concerned, this is both an opportunity and a challenge. 3D point cloud data processing is the core content of reverse engineering technology. As an important data processing step in the preprocessing stage of 3D point cloud, point cloud registration plays an extremely important role in obtaining the complete 3Dcoordinates of the measured target surface. However, at present, the registration speed, accuracy and reliability of various registration algorithms still need to be improved. Cloud computing technology integrates several cheap ordinary PCs into a cloud computing cluster, which realizes the safe storage and efficient processing of massive data. Therefore, consider combining cloud computing with data mining algorithm to solve the problem of massive data conversion in smart grid. In this paper, cloud computing technology is introduced into the smart grid condition monitoring field. By introducing distributed file system, improving traditional density clustering algorithm and parallel design, the storage and clustering division of big data in condition monitoring are effectively solved, which provides a feasible method for the application of cloud computing in condition monitoring field.

关键词： Cloud computing Smart grid Transmission data 3D point cloud data analysis technology

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Teaching Performance Metrics in parallel computing Courses

Teaching Performance Metrics in Parallel Computing Courses

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Sandino Vargas-Pérez Department of Computer Science Kalamazoo College Kalamazoo MI USA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

As high-performance computing technologies advance, the significance of parallel programming in various domains is becoming increasingly evident since it allows us to harness the power of heterogeneous computing and solve complex problems more efficiently. However, for students to master this type of computation and be able to apply it in different contexts, it requires understanding how measuring and optimizing parallel code impacts its performance. This paper presents an approach to enhancing students' comprehension of parallel performance metrics through an interactive exercise that complements lectures on parallel performance and improves assessment.

关键词： Measurement distributed processing Data analysis parallel programming High performance computing Conferences Education

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A distributed Storage System for System Logs Based on Hybrid Compression Scheme 21

A Distributed Storage System for System Logs Based on Hybrid...

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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： Chang, Baoming Zhou, Fengxi Wang, Zhaoyang Wen, Yu Zhang, Boyang 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

Modern enterprises are facing a massive threat from Advanced Persistent Threats (APTs), which have risen to be one of the most dangerous challenges in recent years. Since system logs capture the complex causality dependencies between system entities, they have become the primary data source for countering APTs. However, as modern computer systems get more complicated, system logs can pile up in large quantities. Besides, APTs are sophisticated and persistent cyber attacks that can remain hidden in the target for a long time and constantly steal private data. System logs need to be collected and stored for a long duration to enable a complete analysis of APTs. Such a vast amount of log data is challenging for enterprises to store and manage. There are two mainstream solutions for reducing storage overhead. Data compression methods provide an intuitive idea. However, they are designed for general text and lack optimization for system logs. Another solution considers log reduction, which removes redundant system events recorded in system logs by predefined rules. Unfortunately, they are tailored for specific kinds of redundant information, resulting in limited applicability. Realizing that these two solutions adopt two distinct perspectives to reduce storage overhead, they are complementary. Data compression methods shrink the size of log data from their binary form. Log reduction starts from the semantic information of system logs and removes redundant information to reduce storage overhead. Combining both methods maximizes storage efficiency. In this paper, we propose a distributed storage system based on a hybrid compression scheme. To address the above deficiencies, we first identify and merge redundant system events by analyzing and tracing the information flow rather than based on rules. Then, we apply log parsing to preprocess log entries for further storage efficiency. Besides, we design a distributed architecture to optimize compression and eliminate repeated

关键词： Digital storage

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RSCache: A Tail Latency Friendly Cache Based on NVMe SSDs 22

RSCache: A Tail Latency Friendly Cache Based on NVMe SSDs

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Lu, Jincheng Cai, Miao Ye, Baoliu Hohai University College of Computer Science and Software Engineering Nanjing China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798331509712

High fan-out requests are prevalent in systems employing multi-tier architectures. These requests are divided into several sub-requests for parallel processing. However, a high fan-out request must await all sub-requests to be completed before returning, but the processing times of sub-requests are unpredictable due to their differences in characteristics, such as data volume and data popularity. Meanwhile, existing SSD-based caches struggle to adjust request processing speeds to ensure timely handling. As a result, some sub-requests are delayed, affecting the overall latency and causing long tail latency *** paper proposes RSCache, a tail latency-friendly cache based on NVMe SSDs. RSCache combines the NVMe Weighted Round-robin (WRR) arbitration mechanism with a priority-based scheduling mechanism to enable differentiated request processing. We propose a fan-out size-based priority assignment strategy along with a latency-aware sub-request sorting method. They collaborate to prioritize sub-requests according to their impacts on tail latency and schedule them to NVMe priority queues with various processing speeds, effectively reducing the processing time variations among sub-requests. In addition, we balance the load across queues to avoid congestion with a novel feedback mechanism. We implement the prototype of RSCache based on SPDK. Our experiments demonstrate that RSCache reduces tail latency by up to 40% and improves throughput by two times compared to state-of-the-art SSD-based cache designs. © 2024 IEEE.

关键词： parallel architectures

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EMOSS'22: Workshop on Emerging Open Storage Systems and Solutions for Data Intensive computing 31

EMOSS'22: Workshop on Emerging Open Storage Systems and Solu...

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31st international symposium on High-Performance parallel and distributed computing, HPDC 2022

作者： Lofstead, Jay Maltzahn, Carlos Jimenez, Ivo Sandia National Laboratories AlbuquerqueNM United States University of California Santa CruzCA United States

ISBN: (纸本)9781450391993

The P-RECS workshop focuses heavily on practical, actionable aspects of reproducibility in broad areas of computational science and data exploration, with special emphasis on issues in which community collaboration can be essential for adopting novel methodologies, techniques and frameworks aimed at addressing some of the challenges we face today. The workshop brings together researchers and experts to share experiences and advance the state of the art in the reproducible evaluation of computer systems, featuring contributed papers and invited talks. © 2022 Owner/Author.

关键词： Digital storage

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Brief Announcement: distributed Algorithms for Minimum Dominating Set Problem and Beyond, a New Approach 36

Brief Announcement: Distributed Algorithms for Minimum Domin...

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36th international symposium on distributed computing, DISC 2022

作者： Alipour, Sharareh Salari, Mohammadhadi Tehran Institute for Advanced Studies Iran Simon Fraser University Burnaby Canada

ISBN: (纸本)9783959772556

In this paper, we study the minimum dominating set (MDS) problem and the minimum total dominating set (MTDS) problem. We propose a new idea to compute approximate MDS and MTDS. This new approach can be implemented in a distributed model or parallel model. We also show how to use this new approach in other related problems such as set cover problem and k-distance dominating set problem. © Sharareh Alipour and Mohammadhadi Salari.

关键词： distributed computer systems

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