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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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Cross-clouds services autonomic management approach based on self-organizing multi-agent technology

Cross-clouds services autonomic management approach based on...

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作者： Hou, Fu Mao, Xinjun College of Computer National University of Defense Technology Changsha410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha410073 China

With the rapid development and application of cloud computing, there exist plenty of clouds that are distributed on the open Internet, decentralized in the management, evolving with various services providing diverse functionalities and QoS. Moreover, because of the potential correlativity of cloud services and the dynamic of tenants' requirements, these services in multiple clouds are expected to be effectively managed in an autonomic and transparent way so that tenants can obtain continuous and efficient services. To this end, this paper proposes an approach based on self-organizing multi-agent system to achieving cross-clouds services management, including the service provision at the tenant-end and services aggregation at the cloud-end. In this approach, the clouds services are managed by a series of autonomous agents that are capable of autonomously accessing managed services. They can interact with each other to obtain macro-level services aggregation in terms of self-organization to adapt to the changes of both tenants' requirements and cloud services. The paper details the architecture, mechanisms, and algorithms to implement the aggregation and provision of services in cross-clouds. We also develop relevant cross-clouds services management platform called as CCloudMan, with which several experiments based on the public data sets have been conducted and the experimental results show the efficiency and usability of our proposed approach. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Multi agent systems

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Window memory accesses method in alternate row/column matrix access systems

Window memory accesses method in alternate row/column matrix...

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2010 2nd International Conference on Computer Engineering and technology, ICCET 2010

作者： Jie, Zhou Dou, Yong Lei, Yuanwu Dong, Yazhuo National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Unit 91655 People's Republic of China Beijing China

ISBN: (纸本)9781424463503

Many systems, such as Synthetic Aperture Radar (SAR) processing, two-dimensional image processing, 2d-FFT calculation, need access the row and column data of their matrix alternately. The DRAM memory should be used due to huge data in these systems. To improve the usage of memory bandwidth in such systems, this paper theoretically analyses the optimal window size to minimize the total number of opening/closing pages when performing in such instances by balancing the number of handling physical pages between row and column accesses. This paper presents a window-based optimal memory access method, and we implemented an FPGA-based SDRAM controller with eight simple ports, which is based on window accessing mechanism and supports commercialized SDARM. The experimental results show that the effective I/O bandwidth of external SDRAM using our window layout approach increases from 114.2MB/s of naive implementation to 730.2MB/s with over 6X speedup. In addition, we implemented two SAR processing systems with four FFT processing elements using our window-based SDRAM controller and Corner Turn method separately in FPGA chip. Results show window-based method can achieve a speedup of 2.6 compared to Corner Turn method. © 2010 IEEE.

关键词： Fast Fourier transforms

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Model transformation based verification of Web services composition

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4th International Conference on Grid and Cooperative Computing - GCC 2005

作者： Yang, YanPing Tan, QingPing Xiao, Yong School of Computer Science National University of Defense Technology Changsha 410073 China National Lab. of Parallel Distributed Processing Changsha 410073 China

ISBN: (纸本)3540305106

Current Web services composition proposals, such as BPEL, BPSS, BPMN and WSCI, provide notations for describing the control and message flows in Web service collaborations. However, such proposals remain at the descriptive level, without providing any kind of mechanisms or tool support for verifying the composition specified in the proposed notations. In this paper, we present to analyze and verify Web services composition by using CP-nets. CP-nets combine the strengths of Petri nets with the expressive power of high-level programming and have sound mathematical semantics. These services composition proposals can be transformed by model transformation rules into CP-nets, which can be used to analyze the performance and to investigate behavioral properties by CP-nets specialized tools. © Springer-Verlag Berlin Heidelberg 2005.

关键词： World Wide Web

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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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Accelerating FDTD simulation of microwave pulse coupling into narrow slots on the Intel MIC architecture

Accelerating FDTD simulation of microwave pulse coupling int...

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IEEE Pacific Rim Conference on Communications, Computers and Signal processing, PACRIM 2015

作者： Wang, Qinglin Liu, Jie Cui, Xiantao Fu, Guitao Gong, Chunye Xing, Zuocheng Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer Science National University of Defense Technology Changsha410073 China Beijing Satellite Navigation Center Beijing100094 China

ISBN: (纸本)9781467377881

The coupling of microwaves into apertures plays an important part in many electromagnetic physics and engineering fields. When the width of apertures is very small, Finite Difference Time Domain (FDTD) simulation of the coupling is very time-consuming. As a many-core architecture, the Intel's Many Integrated Core (MIC) architecture owns 512-bit vector units and more than 200 threads. In this paper, we parallelize FDTD simulation of microwave pulse coupling into narrow slots on the Intel MIC architecture. In the implementation, the parallel programming model OpenMP is used to exploit thread parallelism while loop unrolling and SIMD intrinsic functions are utilized to accomplish vectorization. Compared with the serial version on Intel Xeon E5-2670 CPU, the implementation on the MIC coprocessor including 57 cores obtains a speedup of 11.57 times. The experiment results also demonstrate that the parallelization has good scalability in performance. Additionally, how binding relationship between OpenMP threads and hardware threads in MIC influences performance is also reported. © 2015 IEEE.

关键词： Scalability

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Fast flow field prediction of pollutant leakage diffusion based on deep learning

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Environmental science and Pollution Research 2024年第36期31卷 49393-49412页

作者： YunBo, Wan Zhong, Zhao Jie, Liu KuiJun, Zuo Yong, Zhang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang621000 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China School of Aeronautics Northwestern Polytechnical University Xi’an710072 China

Abstract: Predicting pollutant leakage and diffusion processes is crucial for ensuring people’s safety. While the deep learning method offers high simulation efficiency and superior generalization, there is currently a lack of research on predicting pollutant leakage and diffusion flow field using deep learning. Therefore, it is necessary to conduct further studies in this area. This paper introduces a two-level network method to model the flow characteristics of pollutant diffusion. The proposed method in this study demonstrates a significant enhancement in flow field prediction accuracy compared to traditional deep learning methods. Moreover, it improves computational efficiency by over 800 times compared to traditional computational fluid dynamics (CFD) methods. Unlike conventional CFD methods that require grid expansion to calculate all operation conditions, the deep learning method is not confined by grid limitations. While deep learning methods may not entirely replace CFD methods, they can serve as a valuable supplementary tool, expanding the versatility of CFD methods. The findings of this research establish a robust foundation for incorporating deep learning methods in addressing pollutant leakage and diffusion challenges. Graphical abstract: A deep learning method for simulating pollutant leakage diffusion. (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Diffusion

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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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Adaptive Self-Supervised Continual Learning 26

Adaptive Self-Supervised Continual Learning

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Wu, Lilei Wang, Zhen Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Tsinghua University Beijing100084 China

ISBN: (纸本)9781643684369

Continual Learning (CL) studies the problem of developing a robust model that can learn new tasks while retaining previously learned knowledge. However, the current CL methods exclusively focus on data with annotations, disregarding that unlabelled data is the mainstream in real-world applications. To close this research gap, this study concentrates on continual self-supervised learning, which is plagued by challenges of memory over-fitting and class imbalance. Besides, these challenges are exacerbated throughout incremental training. Aimed at addressing these challenges from both loss and data perspectives, we introduce a framework, Adaptive Self-supervised Continual Learning (ASCL). Specifically, we devise an Adaptive Sharpness-Aware Minimization (ASAM) module responsible for identifying flatter local minima in the loss landscape with a smaller memory over-fitting risk. Additionally, we design an Adaptive Memory Enhancement (AME) module responsible for rebalancing self-supervised loss with new and old tasks from a data perspective. Finally, the adaptive mechanisms in AME and ASAM modules dynamically adjust the loss landscape sharpness and memory enhancement strength with the feedback of intermediate training results. The results of our extensive experiments demonstrate the state-of-the-art performance of our methods in continual self-supervised learning scenarios across multiple datasets. © 2023 The Authors.

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Simulation research of communication networks based on prim algorithm

Simulation research of communication networks based on prim ...

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International Forum on Information technology and Applications

作者： Yang, Chenghui Ren, ChuanJun College of Electrical Engineering Northwest University for Nationalities Lanzhou 730030 China National Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9780769541150

In order to improve the efficiency of the communication networks, we used the Kruskal algorithm and the Prim algorithm through algorithm comparison and analysis methods of data structure. A dynamic framework for the communication network installed system is built. Moreover, according to the actual framework of the communication network specific issues, the module chooses the Minimum Cost Spanning Tree Prim algorithm ultimately. The assumptions process and outcomes simulation have proper analysis and certification by C language. Experimental results show that the Minimum Cost Spanning Tree of Prim algorithm on the installed system for communication networks dynamic planning process have the advantage of speed and effectively reduce the waste of resources, which not only can ensure efficiency but also can effectively improve communication networks installed cost. © 2010 IEEE.

关键词： Costs

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