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Performance problems online detection in cloud computing systems via analyzing request execution paths

Performance problems online detection in cloud computing sys...

引用

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...

引用

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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Network and service management and diagnostics solution of a remote patient monitoring system

Network and service management and diagnostics solution of a...

引用

3rd IEEE International Symposium on Logistics and Industrial Informatics, LINDI 2011

作者： Kozlovszky, Miklos Meixner, Zsolt Windisch, Gergely Márton, Judit Ács, Sándor Bogdanov, Pál Boruzs, Anikó Kotcauer, Péter Ferenczi, János Kozlovszky, Viktor MTA SZTAKI Laboratory of Parallel and Distributed Computing Budapest Hungary Óbuda University John von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9781457718410

We have developed a combined network and service management and diagnostics solution for our in-house developed remote patient monitoring system. The developed system has included into the ALPHA eHealth/remote patient monitoring system and was successfully used in our large Living Lab infrastructure operating in three different Hungarian regions with 40 patients. In this paper we will identify the key elements of the combined Network and Service Management solution of the remote patient monitoring system. © 2011 IEEE.

关键词： Telemedicine

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GPGPU-based data parallel region growing algorithm for cell nuclei detection

GPGPU-based data parallel region growing algorithm for cell ...

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12th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2011

作者： Szénási, Sándor Vámossy, Zoltán Kozlovszky, Miklós Óbuda University John Von Neumann Faculty of Informatics Budapest Hungary MTA SZTAKI Laboratory of Parallel and Distributed Computing Budapest Hungary

ISBN: (纸本)9781457700453

Nowadays microscopic analysis of tissue samples is done more and more by using digital imagery and special immunodiagnostic software. These are typically specific applications developed for one distinct field, but some subroutines are commonly repeated, for example several applications contain steps that can detect cell nuclei in a sample image. The aim of our research is developing a new data parallel algorithm that can be implemented even in a GPGPU environment and that is capable of counting hematoxylin eosin (HE) stained cell nuclei and of identifying their exact locations and sizes (using a variation of the region growing method). Our presentation contains the detailed description of the algorithm, the peculiarity of the CUDA implementation, and the evaluation of the created application (regarding its accuracy and the decrease in the execution time). © 2011 IEEE.

关键词： parallel algorithms

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A wikipedia based semantic graph model for topic tracking in blogosphere

A wikipedia based semantic graph model for topic tracking in...

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22nd International Joint Conference on Artificial Intelligence, IJCAI 2011

作者： Tang, Jintao Wang, Ting Lu, Qin Wang, Ji Li, Wenjie College of Computer National University of Defense Technology Changsha China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong National Laboratory for Parallel and Distributed Processing Changsha China

ISBN: (纸本)9781577355120

There are two key issues for information diffusion in blogosphere: (1) blog posts are usually short, noisy and contain multiple themes, (2) information diffusion through blogosphere is primarily driven by the "word-of- mouth" effect, thus making topics evolve very fast. This paper presents a novel topic tracking approach to deal with these issues by modeling a topic as a semantic graph, in which the semantic relatedness between terms are learned from Wikipedia. For a given topic/post, the name entities, Wikipedia concepts, and the semantic relatedness are extracted to generate the graph model. Noises are filtered out through the graph clustering algorithm. To handle topic evolution, the topic model is enriched by using Wikipedia as background knowledge. Furthermore, graph edit distance is used to measure the similarity between a topic and its posts. The proposed method is tested by using the real-world blog data. Experimental results show the advantage of the proposed method on tracking the topic in short, noisy texts.

关键词： Blogs

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Using complex network features for fast clustering in the web 11

Using complex network features for fast clustering in the we...

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Proceedings of the 20th international conference companion on World wide web

作者： Tang, Jintao Wang, Ting Wang, Ji Lu, Qin Li, Wenjie School of Computer National University of Defense Technology Changsha China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong National Laboratory for Parallel and Distributed Processing Changsha China

ISBN: (纸本)9781450305181

Applying graph clustering algorithms in real world networks needs to overcome two main challenges: the lack of prior knowledge and the scalability issue. This paper proposes a novel method based on the topological features of complex networks to optimize the clustering algorithms in real-world networks. More specifically, the features are used for parameter estimation and performance optimization. The proposed method is evaluated on real-world networks extracted from the web. Experimental results show improvement both in terms of Adjusted Rand index values as well as runtime efficiency. © 2011 Authors.

关键词： Parameter estimation

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parallel biomedical image processing with GPGPUs in cancer research

Parallel biomedical image processing with GPGPUs in cancer r...

引用

3rd IEEE International Symposium on Logistics and Industrial Informatics, LINDI 2011

作者： Reményi, Attila Szénási, Sándor Bándi, István Vámossy, Zoltán Valcz, Gábor Bogdanov, Pál Sergyán, Szabolcs Kozlovszky, Miklos Óbuda University John von Neumann Faculty of Informatics Budapest Hungary Semmelweiss University 2nd Department of Internal Medicine Budapest Hungary MTA SZTAKI/Laboratory of Parallel and Distributed Computing Budapest Hungary

ISBN: (纸本)9781457718410

The main aim of this work is to show, how GPGPUs can facilitate certain type of image processing methods. The software used in this paper is used to detect special tissue part, the nuclei on (HE - hematoxilin eosin) stained colon tissue sample images. Since pathologists are working with large number of high resolution images - thus require significant storage space -, one feasible way to achieve reasonable processing time is the usage of GPGPUs. The CUDA software development kit was used to develop processing algorithms to NVIDIA type GPUs. Our work focuses on how to achieve better performance with coalesced global memory access when working with three-channel RGB tissue images, and how to use the on-die shared memory efficiently. © 2011 IEEE.

关键词： Image processing

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An Outpost Vector placement evaluation of an incremental learning algorithm for Support Vector Machine

An Outpost Vector placement evaluation of an incremental lea...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Piyabute Fuangkhon Thitipong Tanprasert Distributed and Parallel Computing Research Laboratory Faculty of Science and Technology Assumption University Bangkok Thailand

Outpost Vector model synthesizes new vectors at the boundary of two classes of data in order to increase the level of accuracy of classification. This paper presents a performance evaluation of four different placements of outpost vectors in an incremental learning algorithm for Support Vector Machine (SVM) on a non-complex problem. The algorithm generates outpost vectors from selected new samples, selected prior samples, both samples, or generates no outpost vector at all. After that, they are included in the final training set, as well as new samples and prior samples, based on the specified parameters. The experiments are conducted with a 2-dimension partition problem. The distribution of training and test samples is created in a limited location of a 2-dimension donut ring. There are two classes of data which are represented as 0 and 1. The context of the problem is assumed to shift 45 degrees in counterclockwise direction. Every consecutive partition is set to have different class of data. The experimental results show that the placement of outpost vectors generated from only selected new samples yields the highest level of accuracy of classification for both new data and old data. As a result, using samples from different part of the algorithm to generate outpost vectors affects the level of accuracy of classification.

关键词： Training Support vector machines Accuracy Shape Data models Context Vectors

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Biomedical image processing with GPGPU using CUDA

Biomedical image processing with GPGPU using CUDA

引用

作者： Firstauthor, A.R. Secondauthor, I.B. Thirdauthor, G.V. Fourthauthor, P.B. Fifthauthor, M.K. Buda University John von Neumann Faculty of Informatics Budapest Hungary Semmelweiss University 2nd Department of Internal Medicine Budapest Hungary MTA SZTAKI Laboratory of Parallel and Distributed Computing Budapest Hungary

ISBN: (纸本)9789532330670

The main aim of this work is to show, how the GPGPUs can be used to speed up certain image processing methods. The algorithm explained in this paper is used to detect nuclei on (HE hematoxilin eosin) stained colon tissue sample images, and includes a Gauss blurring, an RGB-HSV color space conversion, a fixed binarization, an ultimate erode procedure and a local maximum search. Since the images retrieved from the digital slides require significant storage space (up to few hundred megapixels), the usage of GPGPUs to speed up image processing operations is necessary in the interest of achieving reasonable processing time. The CUDA software development kit was used to develop algorithms to GPUs made by NVIDIA. This work focuses on how to achieve coalesced global memory access when working with three-channel RGB images, and how to use the on-die shared memory efficiently. The exact test algorithm also included a linear connected component labeling, which was running on the CPU, and with iterative optimization of the GPU code, we managed to achieve significant speed up in well defined test environment. © 2011 MIPRO.

关键词： Iterative methods

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Performance problems online detection in cloud computing systems via analyzing request execution paths

Performance problems online detection in cloud computing sys...

引用

International Conference on Dependable Systems and Networks Workshops (DSN-W)

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

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.

关键词： Cloud computing Principal component analysis Instruments Degradation Servers Data mining Accuracy

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