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19th European Conference on Computer Systems, EuroSys 2024

作者： Lu, Fangming Wei, Xingda Huang, Zhuobin Chen, Rong Wu, Minyu Chen, Haibo Institute of Parallel and Distributed Systems Seiee Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China University of Electronic Science and Technology of China China Institute of Parallel and Distributed Systems Germany

ISBN: (纸本)9798400704376

Serialization and deserialization play a dominant role in the state transfer time of serverless workflows, leading to substantial performance penalties during workflow execution. We identify the key reason as a lack of ability to efficiently access the (remote) memory of another function. We propose RMMap, an OS primitive for remote memory map. It allows a serverless function to directly access the memory of another function, even if it is located remotely. RMMap is the first to completely eliminates serialization and deserialization when transferring states between any pairs of functions in (unmodified) serverless workflows. To make remote memory map efficient and feasible, we co-design it with fast networking (RDMA), OS, language runtime, and serverless platform. Evaluations using real-world serverless workloads show that integrating RMMap with Knative reduces the serverless workflow execution time on Knative by up to 2.6 × and improves resource utilizations by 86.3%. © 2024 ACM.

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False-positive probability and compression optimization for tree-structured bloom filters

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ACM Transactions on Modeling and Performance Evaluation of Computing Systems 2016年第4期1卷 1–39页

作者： Fu, Yongquan Biersack, Ernst National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Sanyi Road Changsha Hunan Province410073 China CAIPY Valbonne06560 France

Bloom filters are frequently used to to check the membership of an item in a set. However, Bloom filters face a dilemma: the transmission bandwidth and the accuracy cannot be optimized simultaneously. This dilemma is particularly severe for transmitting Bloom filters to remote nodes when the network bandwidth is limited. We propose a novel Bloom filter called BloomTree that consists of a tree-structured organization of smaller Bloom filters, each using a set of independent hash functions. BloomTree spreads items across levels that are compressed to reduce the transmission bandwidth need. We show how to find optimal configurations for BloomTree and investigate in detail by how much BloomTree outperforms the standard Bloom filter or the compressed Bloom filter. Finally, we use the intersection of BloomTrees to predict the set intersection, decreasing the false-positive probabilities by several orders of magnitude compared to both the compressed Bloom filter and the standard Bloom filter. © 2016 ACM 2376-3639/2016/09-ART19 $15.00

关键词： Genetic algorithms

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FedEAE: Federated Learning Based Privacy-Preserving Event Argument Extraction 12th

FedEAE: Federated Learning Based Privacy-Preserving Event Ar...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Hu, Fei Dong, Shenpo Chang, Tao Zhou, Jie Li, Haili Wang, Jingnan Chen, Rui Liu, Haijiao Wang, Xiaodong National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783031446955

Benefiting from Pre-trained Language Model (PLM), Event Argument Extraction (EAE) methods have achieved SOTA performance in general scenarios of Event Extraction (EE). However, with increasing concerns and regulations on data privacy, aggregating distributed data among different institutions in some privacy-sensitive territories (e.g., medical record analysis, financial statement analysis, etc.) becomes very difficult, and it’s hard to train an accurate EAE model with limited local data. Federated Learning (FL) provides promising methods for a large number of clients to collaboratively learn a shared global model without the need to exchange privacy-sensitive data. Therefore, we propose a privacy-preserving EAE method named FedEAE based on FL to solve the current difficulties. To better adapt to federated scenarios, we design a dataset named FedACE generated from the ACE2005 dataset under IID and Non-IID for our experiments. Extensive experiments show that FedEAE achieves promising performance compared to existing baselines, thus validates the effectiveness of our method. To the best of our knowledge, FedEAE is the first to apply FL in the EAE task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Extraction

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Partial clones for stragglers in MapReduce

Partial clones for stragglers in MapReduce

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International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Li, Jia Wang, Changjian Li, Dongsheng Huang, Zhen National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783662462478

Stragglers can temporize jobs and reduce cluster efficiency seriously. Many researches have been contributed to the solution, such as Blacklist[8], speculative execution[1, 6], Dolly[8]. In this paper, we put forward a new approach for mitigating stragglers in MapReduce, name Hummer. It starts task clones only for high-risk delaying tasks. Related experiments have been carried and results show that it can decrease the job delaying risk with fewer resources consumption. For small jobs, Hummer also improves job completion time by 48% and 10% compared to LATE and Dolly. © Springer-Verlag Berlin Heidelberg 2015.

关键词： MapReduce

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Providing information services for wireless sensor networks through cloud computing

Providing information services for wireless sensor networks ...

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2012 7th IEEE Asia-Pacific Services Computing Conference, APSCC 2012

作者： You, Pengfei Peng, Yuxing Gao, Hang National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9780769548975

Wireless sensor networks (WSN) is a critical technology for information gathering covering many areas, including health-care, transportation, air traffic control and environment monitoring. Despite wide use, the fast increasing data emanating from WSN is not fully utilized due to the limitation for structure of WSN itself. Along with the further development of WSN, the data form which is not be efficiently managed and applied to supply information services for users. As the emerging IT technology, cloud computing supplies powerful utilization ability for IT resources, which makes many traditional applications migrate to cloud computing. In this paper, we propose a framework integrating cloud computing paradigm and WSN, which fully uses data process ability and service model for cloud computing. In the framework, data form WSN are efficiently utilized and managed, depending on which, information services for WSN are well provided to users. © 2012 IEEE.

关键词： Virtualization

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An integration framework of cloud computing with wireless sensor networks

An integration framework of cloud computing with wireless se...

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7th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2012

作者： You, Pengfei Li, Huiba Peng, Yuxing Li, Ziyang National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology 410073 Changsha China

ISBN: (纸本)9789400758568

Wireless sensor networks (WSN) is a key technology extensively applied in many fields, such as transportation, health-care and environment monitoring. Despite rapid development, the exponentially increasing data emanating from WSN is not efficiently stored and used. Besides, the data from multiple different types and locations of WSN needs to be well analyzed, fused and supplied to various types of clients, such as PC, workstation and smart phone. The emerging cloud computing technology provides scalable data process and storage power and some types of connectable services, which can helpfully utilize sensor data from WSN. In this paper, we propose an integration framework of cloud computing with WSN, in which sensor data is transmitted from WSN to cloud, and processed and stored in cloud, then mined and analyzed so as to be supplied to various clients. By applying virtualization and cloud storage technology, and Infrastructure as a Service (IaaS) and Software as a Service (SaaS) of cloud service model, the framework can fully process and store mass sensor data from multiple types of WSN. Besides, it efficiently mines and analyzes sensor data, based on which the data applications are well supplied to various types of clients in form of services. © 2013 Springer science+Business Media.

关键词： Virtualization

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Model Provenance Management in MLOps Pipeline 2022

Model Provenance Management in MLOps Pipeline

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8th International Conference on Computing and Data Engineering, ICCDE 2022

作者： Mei, Songzhu Liu, Cong Wang, Qinglin Su, Huayou Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China Information Center Cmc Logistic Support Department China

ISBN: (纸本)9781450395717

Machine learning engineering is an important technology that has attracted the attention of academia and industry in the past two years. For AI to become a productivity of enterprises, it must be engineered to solve the problems of model development, deployment, management, prediction, and other lifecycle management, and MLOps system is the most concerned technology in the current machine learning engineering process. This paper starts from the training evolution process of intelligent computing models in machine learning engineering, focuses on the influence of data, training algorithms, and requirement indicators on the model evolution process, and gives the definition of Model Provenance and its evolutionary correlation elements. Based on the concept of model lineage, this paper designs a method to manage the model evolution process in the MLOps system and implements the related system. © 2022 Association for Computing Machinery. All rights reserved.

关键词： Life cycle

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EdgeAnchor: A Rapid and Balanced File Storage Strategy at the Network Edge 29

EdgeAnchor: A Rapid and Balanced File Storage Strategy at th...

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

作者： Liu, Han Xie, Xingrui Zhang, Zhuopu Cheng, Geyao Luo, Lailong Guo, Deke National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China National University of Defense Technology National Laboratory for Parallel and Distributed Processing China

ISBN: (纸本)9798350330717

Storing files at the network edge has become a new paradigm of storage systems, which is promising to mitigate network congestion and reduce file retrieval latency. However, the traditional file storage scheme cannot effectively meet the requirements of rapid indexing and load balance when applied directly to the edge. Moreover, due to the dynamic nature of the edge environment where edge servers can join or leave at will, it is necessary for the storage scheme to adjust with minimal disruption. In this paper, we propose EdgeAnchor, a novel edge storage strategy that is composed of the two-layer hash mappings. The first layer, file-to-bucket mapping, adopts the pseudo-deletion algorithm to deal with the variations in file size, while the second layer utilizes the multiple bucket-to-server mapping to adapt to the heterogeneity in the servers' storage capacities. Furthermore, EdgeAnchor constructs a list of deleted or added working sets for each bucket and creates a dictionary for the mappings between buckets and edge servers. In the manner, EdgeAnchor ensures a rapid file index and balances server load at the dynamic network edge. We also attach the mathematical analyses to EdgeAnchor, which theoretically proves its logarithmic complexity of hash operations and memory accesses. The experiments conducted on real-world datasets demonstrate that EdgeAnchor achieves the file index throughput twice as high as that of Consistent Hashing, under the constraints of load balance. Additionally, it ensures a low and stable data migration volume, when adding or removing edge servers consecutively. © 2023 IEEE.

关键词： dynamic network edge file storage load balance rapid indexing

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A novel secure inter-domain routing for identifier/locator split network

A novel secure inter-domain routing for identifier/locator s...

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2nd International Conference on Information technology and Management Innovation, ICITMI 2013

作者： Liu, Ya Ping Liu, Zhi Hong Ma, Shi Cong Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China College of Computer National University of Defense Technology China

ISBN: (纸本)9783037858646

With the rapid development of the Internet, the de facto inter-domain routing protocol, Border Gateway Protocol (BGP), has become very vulnerable to many attacks. For this, several secure inter-domain protocols have been proposed, most of which are focus on addressing the issue of faking AS path, but cannot address other issues, such as violating routing policies, reachability attacks and so on. Based on the concept of identifier/locator split and routing isolation, we have proposed a secure identifier-based inter-domain Routing, SIR [1], which can provide control plane security without using the high overhead mechanism. Then, we here optimize SIR and propose another secure inter-domain routing, Optimized SIR (O-SIR), which introduces a checked path table and records the path which has been checked before in each AS. We present the simulations based on the real world datasets CAIDA and compare the performance between SIR and O-SIR by different deployments and different position of attacks. The results show that O-SIR can reduce 18%-45% communication cost of verifying paths with additional below 0.1% routing table size. © (2013) Trans Tech Publications, Switzerland.

关键词： Routing protocols

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Research on Fine-Grained Sentiment Classification 8th

Research on Fine-Grained Sentiment Classification

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8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019

作者： Wang, Zhihui Wang, Xiaodong Chang, Tao Lv, Shaohe Guo, Xiaoting National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology Changsha410073 China

ISBN: (纸本)9783030322359

Aiming at the fine-grained sentiment classification that distinguishes the emotional intensity, the commonly used dataset SST-1 is analyzed in depth. Through the analysis, it is found that the dataset has serious problems such as data imbalance and small overall scale, which seriously restricts the classification effect. In order to solve the related problems, data augmentation method is adopted to realize the optimization of the dataset. The IMDB and other data which are relatively homologous to the original dataset are annotated, and the focus is to expand the categories with fewer numbers. By this way, the problem of data imbalance is effectively alleviated and the original data scale is expanded. Then, based on the Bidirectional Encoder Representations from Transformers (BERT) model, which has good overall performance on natural language processing, the benchmark classification model is built. Through multiple comparison experiments on the original dataset and the enhanced data, the influence of the deficiency of the original dataset on the classification effect is verified. And, it is fully demonstrated that the enhanced data can effectively improve the test results and solve the problem of large differences in performance between different categories well. © 2019, Springer Nature Switzerland AG.

关键词： Classification (of information)

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