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λGrapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing 24

λGrapher: A Resource-Efficient Serverless System for GNN Se...

引用

33rd acm Web conference, WWW 2024

作者： Hu, Haichuan Liu, Fangming Pei, Qiangyu Yuan, Yongjie Xu, Zichen Wang, Lin National Engineering Research Center for Big Data Technology and System The Services Computing Technology and System Lab Cluster and Grid Computing Lab in the School of Computer Science and Technology Huazhong University of Science and Technology 1037 Luoyu Road Wuhan China Peng Cheng Laboratory Huazhong University of Science and Technology China School of Mathematics and Computer Science Nanchang University China Paderborn University Paderborn Germany

ISBN: (纸本)9798400701719

Graph Neural Networks (GNNs) have been increasingly adopted for graph analysis in web applications such as social networks. Yet, efficient GNN serving remains a critical challenge due to high workload fluctuations and intricate GNN operations. Serverless computing, thanks to its flexibility and agility, offers on-demand serving of GNN inference requests. Alas, the request-centric serverless model is still too coarse-grained to avoid resource waste. Observing the significant data locality in computation graphs of requests, we propose λGrapher, a serverless system for GNN serving that achieves resource efficiency through graph sharing and fine-grained resource allocation. "Grapher features the following designs: (1) adaptive timeout for request buffering to balance resource efficiency and inference latency, (2) graph-centric scheduling to minimize computation and memory redundancy, and (3) resource-centric function management with fine-grained resource allocation catered to the resource sensitivities of GNN operations and function orchestration optimized to hide communication latency. We implement a prototype of λGrapher based on the representative open-source serverless platform Knative and evaluate it with real-world traces from various web applications. Our results show that λGrapher can achieve an average savings of 61.5% in memory resource and 47.2% in computing resource compared with the state of the arts while ensuring GNN inference latency. © 2024 acm.

关键词： Efficiency

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Situating Network Infrastructure with People, Practices, and Beyond: A Community Building workshop 25

Situating Network Infrastructure with People, Practices, and...

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25th acm conference on computer-supported cooperative work and social computing, CSCW 2022

作者： Jang, Esther Han Beol Bidwell, Nicola Liu, Jen Sengers, Phoebe Bagalkot, Naveen Verdezoto, Nervo Densmore, Melissa Vigil-Hayes, Morgan Hasan, Shaddi Paul G. Allen School for Computer Science and Engineering SeattleWA United States International University of Management Windhoek Namibia Cornell University Information Science IthacaNY United States Cornell University Information Science and Science & Technology Studies IthacaNY United States Srishti Manipal Institute of Art Design and Technology Karnataka Bengaluru India Cardiff University School of Computer Science and Informatics Cardiff United Kingdom University of Cape Town Cape Town South Africa Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ United States Virginia Tech Department of Computer Science BlacksburgVA United States

ISBN: (纸本)9781450391900

Our world is now connected and even entangled in unprecedented ways through networked technologies. Yet pockets of unequal connectivity persist, and technical infrastructures for connectivity remain difficult to design and build even for experts. In this workshop we aim to bring together a global community of multi- and inter-disciplinary researchers and implementers working on infrastructure development and connectivity to explore the existing design challenges and opportunities for bringing technical dimensions of networked infrastructures in conversation with human-computer interaction (HCI) and the social science of infrastructure. We will share, assess and define research problems and resources for rethinking networked infrastructures from human-, community-, and society-centered perspectives, understanding them to be embedded with human values and biases. We particularly intend our collaborative work to support real-world connectivity initiatives, which have grown in critical importance over the pandemic years - especially projects in support of Global South communities. Concrete deliverables from the workshop will include: (1) an initial shared bibliography to help formalize the state of knowledge in our area, (2) an agenda of shared goals, challenges, and intentions in our field, (3) a compilation of resources to support future work, and (4) social and organizing infrastructures for continued communication and academic collaboration. © 2022 Owner/Author.

关键词： Human computer interaction

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Detecting Clickbait in Chinese social Media by Prompt Learning 26

Detecting Clickbait in Chinese Social Media by Prompt Learni...

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26th International conference on computer supported cooperative work in Design, CSCWD 2023

作者： Wu, Yin Cao, Mingpei Zhang, Yueze Jiang, Yong Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen China Peking University School of Electronic and Computer Engineering Shenzhen China Technical University of Munich Department of Informatics Munich Germany

ISBN: (纸本)9798350331684

Online social media has evolved into an essential collaboration system for information exchange. Clickbait refers to sentences containing tricks to attract users to click for commercial profit. In this work, we propose Part-of-speech Enhanced Prompt Learning (PEPL) method for clickbait detection. Our method introduce prompt learning into clickbait detection to stimulate the rich knowledge in pre-trained language models by adding task-specific guidance. Additionally, we design a part-of-speech enhanced prompt to realize grammatical features guided semantic understanding. We conduct extensive experiments to evaluate the performance. The results indicate that our method performs best in low-resource and full-scale scenarios and is still effective in extreme few-shot settings. We also perform ablation and generalization studies to demonstrate the necessity of the proposed components and that our method could be applied to enhance other pre-trained language models. Our method can be adopted in social media and assist downstream tasks to achieve better user-friendly collaboration systems. © 2023 IEEE.

关键词： Learning systems

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About the Blurring of work and Play: Organizational Dynamics Emerging in a First-Person Shooter Videogame 25

About the Blurring of Work and Play: Organizational Dynamics...

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25th acm conference on computer-supported cooperative work and social computing, CSCW 2022

作者： Boldi, Arianna Department of Psychology University of Torino Italy

ISBN: (纸本)9781450391900

The aim of this study is to investigate the organizational dynamics occurring in a multiplayer video game. The research involves E-sports professionals, streamers, and amateur players, who have different motives for playing and represent different "work-play"conditions, and explores how they differently enact organizational behaviors in the game. An ethnographic study within an Italian gaming community is currently in progress. I focus on "Call of Duty: Warzone", a First-Person Shooter Battle Royale game which requires players to enact organizational efforts in order to reach the in-game objectives (e.g., defeat the enemy team). The study uses i) semi-structured interviews with amateur players and participant observation conducted in the game environment played by the amateurs, ii-iii) observation of gaming sessions, analysis of online content and semi-structured interviews with reference to streamers and professionals, iv) analysis of communication exchanges of all three types of players during the gaming sessions. I expect that players belonging to different categories will enact distinct organizational behaviors and give rise to various organizational structures. A cross-comparison between them, which is missing in current literature, would clarify how different modalities of combining work and play impact on organizational behaviors and dynamics. © 2022 Owner/Author.

关键词： Dynamics

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BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance 26

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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26th International conference on computer supported cooperative work in Design, CSCWD 2023

作者： Wang, Feiyu Zhou, Jian-Tao Guo, Xu Inner Mongolia University College of Computer Science Inner Mongolia Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software China

ISBN: (纸本)9798350331684

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme's safety and do simulation experiments to show that the scheme has practicality. © 2023 IEEE.

关键词： Smart contract

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GraphCSR: A Space and Time-Efficient Sparse Matrix Representation for Web-scale Graph Processing 25

GraphCSR: A Space and Time-Efficient Sparse Matrix Represent...

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34th acm Web conference, WWW 2025

作者： Gan, Xinbiao Li, Tiejun Zhang, Qiang Wu, Guang Yang, Bo Gong, Chunye Liu, Jie Lu, Kai School of Computer National University of Defense Technology Changsha China National University of Defense Technology Changsha China

ISBN: (纸本)9798400712746

Graph data processing is essential for web-scale applications, including social networks, recommendation systems, and web of things (WoT) systems, where large, sparsely connected graphs dominate. Traditional sparse matrix storage formats like compressed sparse row (CSR) face significant memory and performance bottlenecks in distributed, federated, and edge-based computing environments, which are increasingly central to the web. To address this challenge, we propose GraphCSR, a novel storage format that clusters vertices with identical edge degrees and stores only the starting index of each group. This approach minimizes memory overhead and facilitates batch memory access while enhancing overall performance, making it particularly suitable for federated systems and resource-constrained edge nodes. Our experiments across various graph operations and large datasets show that GraphCSR achieves considerable memory savings and performance gains of large-scale, distributed graph processing. When deployed GraphCSR on two production-grade supercomputers demonstrating its potential for scaling web and WoT graph processing in large-scale distributed computing systems. © 2025 Copyright held by the owner/author(s).

关键词： Cluster computing

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Bi-population cooperative Moth-flame Optimization Algorithm for the Networking Mode Optimization 26

Bi-population Cooperative Moth-flame Optimization Algorithm ...

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26th International conference on computer supported cooperative work in Design, CSCWD 2023

作者： Cao, Shaorong Mao, Yuanyuan Li, Mingyang Xie, Weidong Gao, Chaochao State Grid Gansu Electric Power Company Lanzhou China

ISBN: (纸本)9798350331684

The moth-flame optimization (MFO) algorithm is extensively employed to attain the global optimization of the problem. The disadvantages of the original MFO algorithm include poor population variety, a sluggish rate of convergence, and an easy propensity to be drawn in by local optimum. This paper presents a bi-population cooperative moth-flame optimization algorithm (BCMFO) to address the issues. Utilizing the low discrepancy sequence (LDS), a random population with a uniform distribution is produced in the search space. Two subpopulations with similar sizes are updated using Gauss mutation and opposition learning. To improve the algorithm's ability to search globally, the elite method is used to eliminate the subpar solutions from the population. BCMFO is used to optimize the networking mode and is confirmed using the benchmark test suite in CEC 2017. Experimental results show that BCMFO outperforms state-of-the-art algorithms. © 2023 IEEE.

关键词： Global optimization

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Circle Back Next Week: The Effect of Meeting-Free Weeks on Distributed workers' Unstructured Time and Attention Negotiation 24

Circle Back Next Week: The Effect of Meeting-Free Weeks on D...

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CHI conference on Human Factors in computing Sytems (CHI)

作者： Ferguson, Sharon Massimi, Michael Univ Toronto Mech & Ind Engn Toronto ON Canada Slack Workforce Lab San Francisco CA USA

ISBN: (纸本)9798400703300

While distributed workers rely on scheduled meetings for coordination and collaboration, these meetings can also challenge their ability to focus. Protecting worker focus has been addressed from a technical perspective, but companies are now attempting organizational interventions, such as meeting-free weeks. Recognizing distributed collaboration as a sociotechnical challenge, we frst present an interview study with distributed workers participating in meeting-free weeks at an enterprise software company. We identify three orientations workers exhibit during these weeks: Focus, Collaborative, and Time-Bound, each with varying levels and use of unstructured time. These diferent orientations result in challenges in attention negotiation, which may be suited for technical interventions. This motivated a follow-up study investigating attention negotiation and the compensating mechanisms workers developed during meeting-free weeks. Our framework identifed tensions between the attention-getting and attention-delegation strategies. We extend past work to show how workers adapt their virtual collaboration mechanisms in response to organizational interventions.

关键词： computer supported-cooperative work workplace Meetings Focus Attention Negotiation

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Iterative Greedy Selection Hyper-heuristic with Linear Population Size Reduction 26

Iterative Greedy Selection Hyper-heuristic with Linear Popul...

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26th International conference on computer supported cooperative work in Design, CSCWD 2023

作者： Zhao, Fuqing Liu, Yuebao Xu, Tianpeng Lanzhou University of Technology School of Computer and Communication Lanzhou730050 China

ISBN: (纸本)9798350331684

Selecting appropriate algorithms for specific problems has become a significant challenge with the remarkable growth of heuristics and meta-heuristics. To address this challenge, an iterative greedy selection hyper-heuristic algorithm with linear population size reduction (LIGSHH) was proposed in this paper. Using an iterative greedy strategy to choose the high level of exploration, this heuristic selects the Low-Level Heuristics (LLHs) that best suit the current problem. Nine LLHs are specifically designed for continuous optimization problems. Additionally, the exploration and exploitation capabilities of the LIGSHH are balanced by reducing the population size linearly at different stages of the problem. The proposed LIGSHH algorithm and comparison algorithms are tested on the CEC2017 benchmark test suite, and the experimental results show that the LIGSHH algorithm outperforms other comparison algorithms. © 2023 IEEE.

关键词： Heuristic algorithms

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Shapley Value-based Contrastive Alignment for Multimodal Information Extraction 24

Shapley Value-based Contrastive Alignment for Multimodal Inf...

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32nd acm International conference on Multimedia, MM 2024

作者： Luo, Wen Xia, Yu Tianshu, Shen Li, Sujian State Key Laboratory of Multimedia Information Processing Peking University Beijing China School of Computer Science Peking University Beijing China Wangxuan Institute of Computer Technology Peking University Beijing China

ISBN: (纸本)9798400706868

The rise of social media and the exponential growth of multimodal communication necessitates advanced techniques for Multimodal Information Extraction (MIE). However, existing methodologies primarily rely on direct Image-Text interactions, a paradigm that often faces significant challenges due to semantic and modality gaps between images and text. In this paper, we introduce a new paradigm of Image-Context-Text interaction, where large multimodal models (LMMs) are utilized to generate descriptive textual context to bridge these gaps. In line with this paradigm, we propose a novel Shapley Value-based Contrastive Alignment (Shap-CA) method, which aligns both context-text and context-image pairs. Shap-CA initially applies the Shapley value concept from cooperative game theory to assess the individual contribution of each element in the set of contexts, texts and images towards total semantic and modality overlaps. Following this quantitative evaluation, a contrastive learning strategy is employed to enhance the interactive contribution within context-text/image pairs, while minimizing the influence across these pairs. Furthermore, we design an adaptive fusion module for selective cross-modal fusion. Extensive experiments across four MIE datasets demonstrate that our method significantly outperforms existing state-of-the-art methods. © 2024 acm.

关键词： Contrastive Learning

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