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A Novel pagerank-Based Fault Handling Strategy for Workflow Scheduling in Cloud Data Centers

INTERNATIONAL JOURNAL OF WEB SERVICES RESEARCH 2021年第4期18卷 1-26页

作者： Xie, Fei Yan, Jun Shen, Jun Univ Wollongong Fac Engn & Informat Sci Sch Comp & Informat Technol Wollongong NSW Australia Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia

Unexpected faults result in unscheduled cloud outage, which negatively affects the completion of workflow tasks in the cloud. This paper presents a novel pagerank-based fault handling strategy to rescue workflow tasks at the faulty data center. The proposed approach uses a holistic view and considers the task attributes, the timeline scenario, and the overall cloud performance. A priority assignment system is developed based on the modified pagerank algorithm to prioritise workflow tasks. A min-max normalization method is applied to select the target data center and match the timeline at this data center. Additionally, a dynamic pagerank-constrained task scheduling algorithm is proposed to generate the task scheduling solution. The simulation results show that the proposed approach can achieve better fault handling performance, measured by task resilience ratio, workflow resilience ratio, and workflow continuity ratio in both the traditional 3-replica and the image backup cloud environment.

关键词： Cloud Performance Dependent Tasks Fault Handling pagerank algorithm Reactive Fault Tolerance Task Attributes Task Scheduling Timeline Allocation Workflow Scheduling

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GP3D: 3D NAND Based In-Memory Graph Processing Accelerator

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2022年第2期12卷 500-507页

作者： Shim, Wonbo Yu, Shimeng Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Seoul Natl Univ Sci & Technol Dept Elect & Informat Engn Seoul 01811 South Korea

In this work, we proposed GP3D: 3D NAND based processing-in-memory (PIM) accelerator for highly parallel and energy efficient graph processing. Although graph processing algorithms give informative and useful features from the real world graphs, innate randomness of the graph data pattern incurs inefficient processing in conventional computing architectures. Some previous works on nonvolatile memory (NVM) based PIM architecture have been proposed with vector-matrix multiplication (VMM) in memory array, but the limited memory capacity of the architecture causes frequent write operations on memory cells. Our GP3D architecture is based on the standard 3D NAND array structure without any structural changes which can utilize the ultra-high density of 3D NAND to store the large scale graphs in a single die. VMM operations on 3D NAND array enables efficient processing of pagerank algorithm, one of the representative algorithms of graph processing. To process the graphs with compressed format without conversion to the dense format, we exploited ternary content addressable memory (TCAM) function on 3D NAND. GP3D can eliminate the data loading burdens from storage device to memory which gives significant enhancement of the latency and energy efficiency. Evaluated by custom designed graph processing simulator, GP3D showed 30-61 times boosted speed and 18-64 times better energy efficiency over the GPU based system for various graph datasets.

关键词： Three-dimensional displays Memory management Computer architecture Arrays Virtual machine monitors Sparse matrices Computational modeling Graph processing accelerator pagerank algorithm processing-in-memory (PIM) ternary content addressable memory (TCAM) 3D NAND flash

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A Word Sense Disambiguation Method Based on Multiple Sense Graph

A Word Sense Disambiguation Method Based on Multiple Sense G...

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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： Liu, Wenjun Wang, Hailan Wang, Xiping Hou, Mengshu Chengdu611731 China School of Computer and Software Engineering Xihua University Chengdu610039 China School of Big Data and Artificial Intelligence Chengdu Technological University Chengdu611730 China

Word Sense Disambiguation is a process to determine the best meaning of an ambiguous word according to its contextual semantic information. Many methods of Word Sense Disambiguation cannot deal with polysemous words well because they only consider the meaning of the adjacent words before and after ambiguous words, and cannot consider the meaning of all words in the sentence globally. In order to solve the above problems, this paper proposes a word sense disambiguation method based on Multiple Sense Graph. This method applies the BERT model to generate word sense vectors, and globally considers the feature relationship between the ambiguous word and all words in the context. In addition, this method applies the pagerank algorithm to score the importance of each sense vector of the word, and the scoring results are sorted to obtain the best sense of the ambiguous word. The experimental results indicate that the proposed BERT-pagerank method improves the evaluation index compared with the other two semantic disambiguation methods. In summary, the proposed method improves the accuracy of word sense disambiguation to obtain the best word sense. © 2024 W. Liu, H. Wang, X. Wang & M. Hou.

关键词： BERT Model pagerank algorithm Word Sense Disambiguation Word Sense Disambiguation Graph

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An effective fault localization approach based on pagerank and mutation analysis

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JOURNAL OF SYSTEMS AND SOFTWARE 2023年第1期204卷

作者： Yan, Yue Jiang, Shujuan Zhang, Yanmei Zhang, Cheng China Univ Min & Technol Engn Res Ctr Mine Digitalizat Minist Educ Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China

Mutation-based fault localization (MBFL) is a popular method based on mutation testing. MBFL applies a variety of operators to generate mutants and calculates the statement's suspiciousness by counting the execution results of the test cases on the mutants. However, the tie problem of MBFL creates obstacles to accurate fault localization. The tie problem refers to that many statements have the same suspiciousness. To solve the tie problem, we propose a fault localization approach based on the pagerank algorithm and mutation analysis (PRMA). We first apply the pagerank algorithm to calculate the faultiness scores of the statements. Then, we weight the suspicious value of the statements with faultiness scores to solve the tie problem. Finally, the weighted suspicious values are sorted in descending order to generate a list, which is provided to developers for fault localization. To evaluate our approach, we conduct experiments on the real fault benchmark Defects4J and the artificial fault dataset Siemens. We compare PRMA with the traditional MBFL techniques (Metallaxis and MUSE) and recently proposed MBFL methods (MCBFL-hybrid-avg, SMFL and SMBFL). The experimental results show that our approach outperforms above comparison methods and improves the effect of fault localization in both quantity and accuracy.& COPY;2023 Elsevier Inc. All rights reserved.

关键词： Software fault localization Mutation-based fault localization pagerank algorithm Mutation analysis

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AI Driven e - Commerce Product Recommendation 6

AI Driven e - Commerce Product Recommendation

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sinha, Shambhavi Rakhra, Manik Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350304480

E-commerce is growing in today's world, and if you want to sell more to your customers and stay ahead of the competition, you need to keep up with online shopping trends and know what they want. In the past few years, the number of products available on shopping websites has grown by a huge amount. This leads to the problem of systems growing. Websites use a lot of data mining tools and suggestion systems, but users still might not get the best product for their search, which hurts the customer experience. Getting the search results as small as possible and combining goods from different websites can save time and improve the customer experience. Also, no shopping website has a way for customers to track prices and make predictions about them. With the help of web scraping and the Product Rank algorithm, our system tends to give customers rich and expensive goods from all shopping sites at the cheapest price of their choice without having to use human filters. With sales going up and income going up, we must also show how much we lost when we bargained with customers. Our proposed system tends to work like a smart web spider for all e-commerce sites. It scans every page for you and finds the most affordable, ideal-quality goods of your choice in actual time. This also makes sure that all e-commerce websites will be merged into a single, highly screened system. It can also make good suggestions based on what the customer wants and act as an involved player in the system. This tool can also help e-commerce companies because it can find dead pages, prices that the seller put in wrong, glitches, mistakes, and fraudulent activities on their websites in real time. © 2023 IEEE.

关键词： pagerank algorithm Product Rank algorithm Web Crawlers Web Search Engine

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Match-Ignition: Plugging pagerank into Transformer for Long-form Text Matching 21

Match-Ignition: Plugging PageRank into Transformer for Long-...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Pang, Liang Lan, Yanyan Cheng, Xueqi Chinese Acad Sci Inst Comp Technol Data Intelligence Syst Res Ctr Beijing Peoples R China Tsinghua Univ Inst AI Ind Res Beijing Peoples R China Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781450384469

Neural text matching models have been widely used in community question answering, information retrieval, and dialogue. However, these models designed for short texts cannot well address the long-form text matching problem, because there are many contexts in long-form texts can not be directly aligned with each other, and it is difficult for existing models to capture the key matching signals from such noisy data. Besides, these models are computationally expensive for simply use all textual data indiscriminately. To tackle the effectiveness and efficiency problem, we propose a novel hierarchical noise filtering model, namely Match-Ignition. The main idea is to plug the well-known pagerank algorithm into the Transformer, to identify and filter both sentence and word level noisy information in the matching process. Noisy sentences are usually easy to detect because previous work has shown that their similarity can be explicitly evaluated by the word overlapping, so we directly use pagerank to filter such information based on a sentence similarity graph. Unlike sentences, words rely on their contexts to express concrete meanings, so we propose to jointly learn the filtering and matching process, to well capture the critical word-level matching signals. Specifically, a word graph is first built based on the attention scores in each self-attention block of Transformer, and key words are then selected by applying pagerank on this graph. In this way, noisy words will be filtered out layer by layer in the matching process. Experimental results show that Match-Ignition outperforms both SOTA short text matching models and recent long-form text matching models. We also conduct detailed analysis to show that Match-Ignition efficiently captures important sentences and words, to facilitate the long-form text matching process.

关键词： Text Matching Long-form Text pagerank algorithm

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MULTIPLICATION-AVOIDING VARIANT OF POWER ITERATION WITH APPLICATIONS 47

MULTIPLICATION-AVOIDING VARIANT OF POWER ITERATION WITH APPL...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Pan, Hongyi Badawi, Diaa Miao, Runxuan Koyuncu, Erdem Cetin, Ahmet Enis Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

ISBN: (纸本)9781665405409

Power iteration is a fundamental algorithm in data analysis. It extracts the eigenvector corresponding to the largest eigenvalue of a given matrix. Applications include ranking algorithms, principal component analysis (PCA), among many others. Certain use cases may benefit from alternate, non-linear power methods with low complexity. In this paper, we introduce multiplication-avoiding power iteration (MAPI). MAPI replaces the standard l(2) inner products that appear at the regular power iteration (RPI) with multiplication-free vector products, which are Mercer-type kernels that induce the l(1) norm. For an n x n matrix, MAPI requires n multiplications, while RPI needs n(2) multiplications per iteration. Therefore, MAPI provides a significant reduction of the number of multiplication operations, which are known to be costly in terms of energy consumption. We provide applications of MAPI to PCA-based image reconstruction as well as to graph-based ranking algorithms. When compared to RPI, MAPI not only typically converges much faster, but also provides superior performance.

关键词： Power iteration multiplication-free algorithms principal component analysis pagerank algorithm

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Hesitant fuzzy multi-attribute group decision making method based on weighted power operators in social network and their application

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第5期40卷 9383-9401页

作者： Chen, Huiping Nantong Univ Sch Econ & Management Nantong 226019 Jiangsu Peoples R China

This paper aims to put forward a hesitant fuzzy multi-attribute group decision making (MAGDM) method based on the weighted power aggregation operators in social network. From the point of view of social network analysis, decision makers (DMs) are interconnected in the process of MAGDM. Furthermore, the dimension of the obtained hesitant fuzzy element (HFE) by original power operators will be greater with the increasing number of attributes and alternatives and DMs, which will lead to the problem of "intermediate expression swell". This paper combines the order operation laws with the power operators to redefine two novel hesitant fuzzy power aggregation operators to simplify the involved calculation and explore new operators' properties. Meanwhile, when two given elements have different number of values, we use the strength of social ties and social influence to develop an algorithm for extending the HFEs objectively. On the other hand, the pagerank algorithm and the deviation method are used to determine DMs' combined weights. The feasibility of the proposed hesitant fuzzy MAGDM method based on social network is illustrated by the application to the actual issue of decision making and the comparative analysis with the existing method.

关键词： Group decision making hesitant fuzzy set social network power operator pagerank algorithm

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GBSR: Graph-based suspiciousness refinement for improving fault localization

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JOURNAL OF SYSTEMS AND SOFTWARE 2024年 218卷

作者： Li, Zheng Li, Mingyu Wu, Shumei Xu, Shunqing Chen, Xiang Liu, Yong Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong Peoples R China

Fault Localization (FL) is an important and time-consuming phase of software debugging. The essence of FL lies in the process of calculating the suspiciousness of different program entities (e.g., statements) and generating a ranking list to guide developers in their code inspection. Nonetheless, a prevalent challenge within existing FL methodologies is the propensity for program entities with analogous execution information to receive a similar suspiciousness. This phenomenon can lead to confusion among developers, thereby reducing the effectiveness of debugging significantly. To alleviate this issue, we introduce fine-grained contextual information (such as partial code structural, coverage, and features from mutation analysis) to enrich the characteristics of program entities. Graphical structures are proposed to organize such information, where the passed and failed tests are constructed separately with the consideration of their differential impacts. In order to support the analysis of multidimensional features and the representation of large-scale programs, the pagerank algorithm is adopted to compute each program entity's weight. Rather than altering the fundamental FL process, we leverage these computed weights to refine the suspiciousness produced by various FL techniques, thereby providing developers with a more precise and actionable ranking of potential fault locations. The proposed strategy Graph-Based Suspiciousness Refinement (GBSR) is evaluated on 243 real-world faulty programs from the Defects4J. The results demonstrate that GBSR can improve the accuracy of various FL techniques. Specifically, for the refinement with traditional SBFL and MBFL techniques, the number of faults localized by the first position of the ranking list ( Top-1) is increased by 189% and 68%, respectively. Furthermore, GBSR can also boost the state-of-the-art learning-based FL technique Grace by achieving a 2.8% performance improvement in Top-1.

关键词： Fault localization Suspiciousness refinement Graph-based representation Mutation analysis pagerank algorithm

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Convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2021年第3期35卷 268-281页

作者： Barreda, Maria Dolz, Manuel F. Castano, M. Asuncion Univ Jaume I Castello Dept Engn & Ciencia Dels Comp Castellon De La Plana Castello Spain

Modeling the performance and energy consumption of the sparse matrix-vector product (SpMV) is essential to perform off-line analysis and, for example, choose a target computer architecture that delivers the best performance-energy consumption ratio. However, this task is especially complex given the memory-bounded nature and irregular memory accesses of theSpMV, mainly dictated by the input sparse matrix. In this paper, we propose a Machine Learning (ML)-driven approach that leverages Convolutional Neural Networks (CNNs) to provide accurate estimations of the performance and energy consumption of theSpMVkernel. The proposed CNN-based models use a blockwise approach to make the CNN architecture independent of the matrix size. These models are trained to estimate execution time as well as total, package, and DRAM energy consumption at different processor frequencies. The experimental results reveal that the overall relative error ranges between 0.5% and 14%, while at matrix level is not superior to 10%. To demonstrate the applicability and accuracy of theSpMVCNN-based models, this study is complemented with an ad-hoc time-energy model for the pagerank algorithm, a popular algorithm for web information retrieval used by search engines, which internally realizes theSpMVkernel.

关键词： Sparse matrix-vector multiplication (SpMV) performance modeling energy modeling supervised learning convolutional neural networks (CNN) pagerank algorithm

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