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Honeycomb: a peer-to-peer substrate for on-demand media streaming service 04

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Proceedings of the Second international conference on Parallel and Distributed Processing and Applications

作者： Dafu Deng Hai Jin Chao Zhang Hao Chen Xiaofei Liao Cluster and Grid Computing Lab. Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783540241287

Peer-to-Peer media streaming service has gained tremendous momentum in recent years. However, a number of challenges in Peer-to-Peer media streaming have not been addressed. In this paper, we propose a peer-to-peer substrate for supplying on-demand media streaming service, called Honeycomb, which mainly addresses on the key technical issue of: constructing a locality-aware P2P overlay network with high scalab.lity and manageability. Honeycomb can fully utilize the locality of underlying physical network so that the bandwidth consumption used for the overlay maintenance can be effectively saved and the QoS requirements for delivering media content can be easily satisfied.

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A Replication and Cache based Distributed Metadata Management system for Data grid

A Replication and Cache based Distributed Metadata Managemen...

引用

ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing (SNPD)

作者： Ke Shi Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

Metadata management is a key technique of data grid. This paper presents a replication and cache based metadata management system (RCMMS) to improve metadata management of Global Distributed Storage system (GDSS). Storage Service Provider (SSP) of GDSS has dedicated buffer to cache metadata. The design, implementation and evaluation of RCMMS are discussed in this paper. RCMMS provides efficient algorithms to manage highly dynamic metadata replicas. The evaluation demonstrates that replica plus cache metadata management system outperforms existing metadata management system of GDSS.

关键词： Least squares approximation technology management Buffer storage Cache storage Peer to peer computing Resource management Data communication Master-slave Software engineering Artificial intelligence

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Exploring the Impact of Processing Guarantees on Performance of Stream Data Processing

Exploring the Impact of Processing Guarantees on Performance...

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2017 17th IEEE International Conference on Communication technology (ICCT 2017)

作者： Syed Muhammad Abrar Akber Changfu Lin Hanhua Chen Fan Zhang Hai Jin Cluster and Grid Computing Lab Services Computing Technology and System Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology

Processing semantics ensure tuple processing guarantees during system recovery from failures. It determines whether particular tuples are re-processed or not upon system recovery. Processing semantics facilitate to maintain real-time response as well as correctness of results in stream data processing. Existing literature is in real deficit to provide sufficient resources exclusively on processing semantics. This work focuses on reviewing processing semantics in stream data processing *** article aims at exploring the impact of processing guaran-tees on performance of stream data processing. We summarize current research directions to guarantee processing semantics,based on prominent stream processing engines. Furthermore,we compare throughput for a couple of representative stream processing engines under all possible processing *** experiments show that at-most once processing guarantee provides the highest throughput among other guarantees. Lastly,challenges for further research are also discussed in this paper.

关键词： stream data processing processing semantic processing guarantee at-least once at-most once exactly-once

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M2CF-Net: A Multi-Resolution and Multi-Scale Cross Fusion Network for Segmenting Pathology Lesion of the Focal Lymphocytic Sialadenitis 1

M2CF-Net: A Multi-Resolution and Multi-Scale Cross Fusion Ne...

引用

1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Han, Hao Lu, Feng Deng, Yanhan Luo, Xiaofang Jin, Hai Tu, Wei Xie, Xia School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan430074 China Tongji Medical College Huazhong University of Science and Technology Tongji Hospital Wuhan430022 China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9798350358780

In the medical realm, the pivotal role of pathological Whole Slide Images (WSIs) in detecting cancer, tracking disease progression, and evaluating treatment efficacy is indisputable. Nevertheless, the identification and quantification of lesion areas in these gigapixel WSIs present a significant challenge due to their substantial size and the intricate details of lesions. To address these issues, we developed a novel multi-resolution and multi-scale cross fusion network (M2CF-Net), adept at managing large-scale pathological WSIs and capturing both fine details and context. Our model particularly focuses on segmenting local lymphocyte infiltration lesions in pathological WSIs of patients diagnosed with primary Sjogren's syndrome. By employing a patch-based training approach and combining interconnected elements via a multi-scale fusion technique, we enhance our model's capacity to detect and analyze structures and features in minor salivary gland section WSIs. Extensive experiments and ablation studies conducted on real-world clinical datasets affirm our model's superior accuracy in identifying lymphocyte-infiltrated regions over state-of-the-art models, with a performance improvement of up to 4.32% in terms of the Dice Similarity Coefficient. © 2023 IEEE.

关键词： Pathology

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X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics

X-Stream: A Flexible, Adaptive Video Transformer for Privacy...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Dou Feng Lin Wang Shutong Chen Lingching Tung Fangming Liu National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Paderborn University Germany National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Guangxi University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Peng Cheng Laboratory China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Video stream analytics (VSA) systems fuel many exciting applications that facilitate people’s lives, but also raise critical concerns about exposing too much individuals’ privacy. To alleviate these concerns, various frameworks have been presented to enhance the privacy of VSA systems. Yet, existing solutions suffer two limitations: (1) being scenario-customized, thus limiting the generality of adapting to multifarious scenarios, (2) requiring complex, imperative programming, and tedious process, thus largely reducing the usability of such systems. In this paper, we present X-Stream, a privacy-preserving video transformer that achieves flexibility and efficiency for a large variety of VSA tasks. X-Stream features three major novel designs: (1) a declarative query interface that provides a simple yet expressive interface for users to describe both their privacy protection and content exposure requirements, (2) an adaptation mechanism that dynamically selects the most suitable privacy-preserving techniques and their parameters based on the current video context, and (3) an efficient execution engine that incorporates optimizations for multi-task deduplication and inter-frame inference. We implement X-Stream and evaluate it with representative VSA tasks and public video datasets. The results show that X-Stream achieves significantly improved privacy protection quality and performance over the state-of-the-art, while being simple to use.

关键词： Privacy Transforms Streaming media Transformers Multitasking Real-time systems Task analysis

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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...

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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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Towards Stricter Black-box Integrity Verification of Deep Neural Network Models 24

Towards Stricter Black-box Integrity Verification of Deep Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： He, Chaoxiang Bai, Xiaofan Ma, Xiaojing Zhu, Bin B. Hu, Pingyi Fu, Jiayun Jin, Hai Zhang, Dongmei Huazhong University of Science and Technology Hubei Wuhan China Shanghai Jiao Tong University Shanghai China Microsoft Corporation Beijing China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9798400706868

Cloud-based machine learning services offer significant advantages but also introduce the risk of tampering with cloud-deployed deep neural network (DNN) models. Black-box integrity verification (BIV) allows model owners and end-users to determine if a cloud-deployed DNN model has been tampered with by examining only the top-1 lab.l responses. Fingerprinting generates fingerprint samples to query the model, achieving BIV with no impact on the model's accuracy. In this paper, we present BIVBench, the first comprehensive benchmark for BIV of DNN models. BIVBench covers 16 types of model modifications, providing extensive coverage of practical modification scenarios. Our analysis reveals that existing fingerprinting methods, which are typically focused on significant tampering, lack the sensitivity needed to effectively detect subtle yet common and potentially severe modifications. To address this limitation, we propose MiSentry (Model Integrity Sentry), a novel fingerprinting method that leverages meta-learning. MiSentry strategically incorporates a few subtly modified models into the meta-learning model zoo and maximizes the divergence of output predictions between the target model and the modified models in the model zoo to generate highly sensitive, generalizable, and effective fingerprint samples. Extensive evaluations using BIVBench demonstrate that MiSentry outperforms existing state-of-the-art methods overall and significantly surpasses them in detecting subtle modifications. The BIVBench and supplementary materials are availab.e at: https://***/CGCL-codes/BIVBench. © 2024 ACM.

关键词： Deep neural networks

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SOV: Service Oriented Virtualization Model for Medical Image grid

SOV: Service Oriented Virtualization Model for Medical Image...

引用

IEEE Workshop on Future Trends of Distributed computing systems

作者： Aobing Sun Hai Jin Qin Zhang Ran Zheng Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

The heterogeneity, distribution and dynamicity of web services hamper WSRF (Web service resource framework) of grid to exert its excellences in resource integrations. In this paper, we propose our SOV (service orient virtualization) model for medical image grid (MedImgrid), which creates a virtual middle layer for grid to simplify the transformation from users' business aims to executable web-service instances. The layer adopts semantic based cross clustering means to support approximate optimal service selection from WSS (Web service set) to break through the hard-encoded mode of web-service based applications. It provides service-independent composition mechanism and can adjust a service composition path even during runtime. SOV utilizes the web services as the joint-point of grid, semantic and virtualization technologies so as to conceal the heterogeneities of low-level resources and surpass the limits of tightly-coupled mode to service composition within grid environment.

关键词： Biomedical imaging Web services Service oriented architecture Resource virtualization Application virtualization Semantic Web technology management Logic programming Conferences Distributed computing

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Magnet: A novel scheduling policy for power reduction in cluster with virtual machines

Magnet: A novel scheduling policy for power reduction in clu...

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IEEE International Conference on cluster computing

作者： Liting Hu Hai Jin Xiaofei Liao Xianjie Xiong Haikun Liu Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

The concept of green computing has attracted much attention recently in cluster computing. However, previous local approaches focused on saving the energy cost of the components in a single workstation without a global vision on the whole cluster, so it achieved undesirable power reduction effect. Other cluster-wide energy saving techniques could only be applied to homogeneous workstations and specific applications. This paper describes the design and implementation of a novel approach that uses live migration of virtual machines to transfer load among the nodes on a multilayer ring-based overlay. This scheme can reduce the power consumption greatly by regarding all the cluster nodes as a whole. Plus, it can be applied to both the homogeneous and heterogeneous servers. Experimental measurements show that the new method can reduce the power consumption by 74.8% over base at most with certain adjustably acceptable overhead. The effectiveness and performance insights are also analytically verified.

关键词： Magnetic multilayers Workstations Servers Lead Magnetic separation Power demand Virtual machining

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WiWrite: Wi-Fi Based Handwriting Recognition like Playing Lego

WiWrite: Wi-Fi Based Handwriting Recognition like Playing Le...

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International Conference on Computer Communications and Networks (ICCCN)

作者： Jiang Xiao Huichuwu Li Yuxiang Liu Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Effective finger writing underpins semantic text input task for virtual reality applications. We demonstrate WiWrite, a novel finger writing system that leverages the ubiquitous wireless signals to enable character recognition and word estimation. WiWrite decomposes characters into basic strokes and distinguishes them by exploring the unique signal reflection pattern caused by finger movements. Apart from the prior arts relying on specialized sensors or wearable devices, WiWrite is device-free without any accessories in deed. Our real-world experiments in two different scenarios show that WiWrite succeeds in classifying 26 upper-case alphabets with 90.8% average accuracy and recognizing simple words with an average accuracy of 96.7%.

关键词： Writing Character recognition Wireless fidelity Hidden Markov models Sensors Filtering Noise reduction

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