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Workshop on Proactive Failure Avoidance, Recovery, and Maintenance

作者： Mi, Haibo Wang, Huaimin Yin, Gang Cai, Hua Zhou, Qi Sun, Tingtao National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Computing Platform Alibaba Cloud Computing Company Hangzhou China

ISBN: (纸本)9781457703751

It is quite a headache for developers to online detect performance problems in large-scale cloud computing systems. The behavior and the hidden connections among the huge amount of runtime request execution paths in cloud computing systems usually contain useful information for performance problem detection. In this paper, we propose an approach to rapidly diagnose the source of performance degradation in large-scale non-stop cloud computing systems. The approach first groups the user requests into categories with a fast clustering algorithm;then applies the principal components analysis to extract the primary methods;finally compares the normal and abnormal behaviors of the primary methods to localize the main cause of performance problems. We conduct extensive experiments over a real-world enterprise system providing services for the public. The results show that our approach can locate the prime causes of performance problems accurately and efficiently.1 © 2011 IEEE.

关键词： Cloud computing

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Identifying faults in large-scale distributed systems by filtering noisy error logs

Identifying faults in large-scale distributed systems by fil...

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Workshop on Proactive Failure Avoidance, Recovery, and Maintenance

作者： Rao, Xiang Wang, Huaimin Shi, Dianxi Chen, Zhenbang Cai, Hua Zhou, Qi Sun, Tingtao National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China Computing Platform Alibaba Cloud Computing Corporation Hangzhou China

ISBN: (纸本)9781457703751

Extracting fault features with the error logs of fault injection tests has been widely studied in the area of large scale distributed systems for decades. However, the process of extracting features is severely affected by a large amount of noisy logs. While the existing work tries to solve the problem by compressing logs in temporal and spatial views or removing the semantic redundancy between logs, they fail to consider the co-existence of other noisy faults that generate error logs instead of injected faults, for example, random hardware faults, unexpected bugs of softwares, system configuration faults or the error rank of a log severity. During a fault feature extraction process, those noisy faults generate error logs that are not related to a target fault, and will strongly mislead the resulted fault features. We call an error log that is not related to a target fault a noisy error log. To filter out noisy error logs, we present a similarity-based error log filtering method SBF, which consists of three integrated steps: (1) model error logs into time series and use haar wavelet transform to get the approximate time series;(2) divide the approximate time series into sub time series by valleys;(3) identify noisy error logs by comparing the similarity between the sub time series of target error logs and the template of noisy error logs. We apply our log filtering method in an enterprise cloud system and show its effectiveness. Compared with the existing work, we successfully filter out noisy error logs and increase the precision and the recall rate of fault feature extraction.1 © 2011 IEEE.

关键词： Errors

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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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Unified Contextualized Knowledge Embedding Method for Static and Temporal Knowledge Graph

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 33卷 82-95页

作者： Gao, Yifu Qiao, Linbo Huang, Zhen Kan, Zhigang He, Yongquan Li, Dongsheng National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology Changsha410000 China Intelligent Game and Decision Lab Beijing China Meituan Beijing100102 China

Recent years, there is a growing interest in knowledge graph embedding (KGE), which maps symbolic entities and relations into low-dimensional vector space to effectively represent structured data from the knowledge graph. In addition, the concept of temporal knowledge graph is proposed to document dynamically changing facts in the real world. Existing works attempt to incorporate temporal information into static KGE methods to accomplish temporal knowledge representations. However, existing static or temporal KGE approaches focus on the single query fact and ignore the query-relevant contextual information in the graph structure. This paper moves beyond the traditional way of scoring facts in distinct vector space and proposes a unified framework with pre-trained language models (PLM) to learn dynamic contextualized static/temporal knowledge graph embeddings, called CoS/TKGE. Given the query-specific subgraph, our model transforms it into an input sequence and uses the PLM to obtain the contextualized knowledge representations, which is flexible adaptive to the input graph contexts. We reformulate the link prediction task as a mask prediction problem to fine-tune the pre-trained language model. And the contrastive learning technique is employed to align dynamic contextual embeddings with static global embeddings. Experimental results on three widely used static and temporal KG datasets show the superiority of our model. © 2014 IEEE.

关键词： Knowledge graph

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Overview of blockchain consensus mechanism 2020

Overview of blockchain consensus mechanism

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2nd International Conference on Big Data Engineering, BDE 2020

作者： Zhang, Changqiang Wu, Cangshuai Wang, Xinyi Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology China College of Computer National University of Defense Technology China

ISBN: (纸本)9781450377225

The advent of the Bitcoin system has brought another boom in the Internet era. In a very short time, many Blockchain systems come into being successively, whose decentration, consensus mechanisms, intelligent contract, and other characteristics make them applicable to various fields such as finance, education, medical, technology, etc. The consensus mechanism is the core of Blockchain technology. And a good consensus mechanism plays a very important role in the stable operation of the Blockchain system. The continuous improvement of consensus mechanisms such as PoW (proof-of-work), PoS (proof-of-stake), DPoS (delegated-proof-of-stake), and PBFT (Practical Byzantine Fault Tolerance) has led to the evolution of Blockchain technology to Blockchain 3.0. Starting from the issue of Byzantine generals, this article analyzes common consensus mechanisms based on existing Blockchain applications and then evaluates their consistency. This paper has a unique perspective, selection and comment on the latest progress and important literature in the field of block chain. At last, it also has a high degree of summary and prospect to the existing problems and the future development trend. © 2020 ACM.

关键词： Blockchain

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Automatic synthesis of processor arrays with local memories on FPGAs

Automatic synthesis of processor arrays with local memories ...

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2010 International Conference on Field-Programmable Technology, FPT'10

作者： Wu, Guiming Dou, Yong Wang, Miao National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China Jiangnan Institute of Computing Technology Wuxi 214083 China

ISBN: (纸本)9781424489817

In this paper, we present an automatic synthesis framework to map loop nests to processor arrays with local memories on FPGAs. An affine transformation approach is firstly proposed to address space-time mapping problem. Then a data-driven architecture model is introduced to enable automatic generation of processor arrays by extracting this data-driven architecture model from transformed loop nests. Some techniques including memory allocation, communication generation and control generation are presented. Synthesizable RTL codes can be easily generated from the architecture model built by these techniques. A preliminary synthesis tool is implemented based on PLUTO, an automatic polyhedral source-to-source transformation and parallelization framework. © 2010 IEEE.

关键词： Field programmable gate arrays (FPGA)

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High performance and memory efficient implementation of matrix multiplication on FPGAs

High performance and memory efficient implementation of matr...

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2010 International Conference on Field-Programmable Technology, FPT'10

ISBN: (纸本)9781424489817

We present a high performance and memory efficient hardware implementation of matrix multiplication for dense matrices of any size on the FPGA devices. By applying a series of transformations and optimizations on the original serial algorithm, we can obtain an I/O and memory optimized block algorithm for matrix multiplication on FPGAs. A linear array of processing elements (PEs) is proposed to implement this block algorithm. We show significant reduction in hardware resources consuming compared to the related work while increasing clock frequency. Moreover, the memory requirement can be reduced to O(S) from O(S 2), where S is the block size. Therefore, more PEs can be integrated into the same FPGA devices. © 2010 IEEE.

关键词： Field programmable gate arrays (FPGA)

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An interface theory based approach to verification of web services

An interface theory based approach to verification of web se...

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30th Annual International Computer Software and Applications Conference, COMPSAC 2006

作者： Zhenbang, Chen Ji, Wang Wei, Dong Zhichang, Qi Yeung, W.L. National Laboratory for Parallel and Distributed Processing Changsha China Department of Computing and Decision Sciences Lingnan University Hong Kong Hong Kong

ISBN: (纸本)0769526551

The verification of web services becomes a challenge in software verification. This paper presents a framework for verification of web service interfaces at various abstraction levels. Its foundation is the interface theory for web services, in which transaction features are incorporated. Within the framework, one may check non mutual invocation, compatibility and refinement of web services at signature, conversation and protocol levels. At protocol level, we present a model checking approach to verifying the protocol properties in Action Set Computation Tree Logic(ASCTL). The paper also discusses the integration of our framework into the web service development. © 2006 IEEE.

关键词： Web services

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A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning

A New Pipeline for Knowledge Graph Reasoning Enhanced by Lar...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chen, Zhongwu Bai, Long Li, Zixuan Huang, Zhen Jin, Xiaolong Dou, Yong National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798891761643

Conventional Knowledge Graph Reasoning (KGR) models learn the embeddings of KG components over the structure of KGs, but their performances are limited when the KGs are severely incomplete. Recent LLM-enhanced KGR models input KG structural information into LLMs. However, they require fine-tuning on open-source LLMs and are not applicable to closed-source LLMs. Therefore, in this paper, to leverage the knowledge in LLMs without fine-tuning to assist and enhance conventional KGR models, we propose a new three-stage pipeline, including knowledge alignment, KG reasoning and entity reranking. Specifically, in the alignment stage, we propose three strategies to align the knowledge in LLMs to the KG schema by explicitly associating unconnected nodes with semantic relations. Based on the enriched KGs, we train structure-aware KGR models to integrate aligned knowledge to original knowledge existing in KGs. In the reranking stage, after obtaining the results of KGR models, we rerank the top-scored entities with LLMs to recall correct answers further. Experiments show our pipeline can enhance the KGR performance in both incomplete and general situations. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Galaxyfly: A novel family of flexible-radix low-diameter topologies for large-scales interconnection networks 16

Galaxyfly: A novel family of flexible-radix low-diameter top...

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30th International Conference on Supercomputing, ICS 2016

作者： Lei, Fei Dong, Dezun Liao, Xiangke Su, Xing Li, Cunlu National Laboratory for Parallel and Distributed Processing Collaborative Innovation Center of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781450343619

Interconnection network plays an essential role in the architecture of large-scale high performance computing (HPC) systems. In the paper, we construct a novel family of lowdiameter topologies, Galaxyfly, using techniques of algebraic graphs over finite fields. Galaxyfly is guaranteed to retain a small constant diameter while achieving a flexible tradeoff between network scale and bisection bandwidth. Galaxyfly lowers the demands for high radix of network routers and is able to utilize routers with merely moderate radix to build exascale interconnection networks. We present effective congestion-aware routing algorithms for Galaxyfly by exploring its algebraic property. We conduct extensive simulations and analysis to evaluate the performance, cost and power consumption of Galaxyfly against state-of-the-art topologies. The results show that our design achieves better performance than most existing topologies under various routing algorithms and traffic patterns, and is cost-effective to deploy for exascale HPC systems. © 2016 ACM.

关键词： Topology

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