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检索条件"任意字段=9th International Conference on Parallel and Distributed Computing, Applications and Technologies"

共 1772 条记录，以下是581-590 订阅

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A User Behavior Anomaly Detection Approach based on Sequence Mining over Data Streams 17

A User Behavior Anomaly Detection Approach based on Sequence...

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17th international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Zhou, Yong Wang, Yijie Ma, Xingkong Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

ISBN: (纸本)9781509050819

How to design a low-latency and accurate approach for user behavior anomaly detection over data streams has become a great challenge. However, existing studies cannot meet low-latency and accurate requirements, due to a large number of subsequences and sequential relationship in behaviors. this paper presents BADSM, a user behavior anomaly detection approach based on sequence milling over data streams that seeks to address such challenge. BADSM uses self-adaptive behavior pruning algorithm to adaptively divide data stream into behaviors and decrease the number of subsequences to improve the efficiency of sequence mining. Meanwhile, the top-k abnormal scoring algorithm is used to reduce the complexity of traversal and obtain quantitative detection result to improve accuracy. We design and implement a streaming anomaly detection system based on BADSM to perform online detection. Extensive experiments confirm that BADSM significantly reduces processing delay by at least 36.8% and false positive rate by 6.4% compared with the classic sequence mining approach PrefixSpan.

关键词： anomaly detection sequence mining data stream user behavior

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Accurate evaluation of bivariate polynomials 17

Accurate evaluation of bivariate polynomials

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17th international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Du, Peibing Jiang, Hao Li, Housen Cheng, Lizhi Yang, Canqun Natl Univ Def Technol Sch Sci Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Max Planck Inst Biophys Chem Am Fassberg 11 D-37077 Gottingen Germany Natl Univ Def Technol State Key Lab High Performance Computat Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781509050819

Polynomials are widely used in scientific computing and engineering. In this paper, we present an accurate and fast compensated algorithm to evaluate bivariate polynomials with floating-point coefficients. this algorithm is applying error free transformations to the bivariate Horner scheme and sum the final decomposition accurately. We also prove the proposed algorithm's accuracy with forward error analysis that the accuracy of the computed result is similar to the result computed by the bivariate Horner scheme in twice the working precision. Numerical experiments illustrate the behavior and it has higher efficiency than the bivariate Horner scheme implemented in double-double library.

关键词： Bivariate polynomial Floating point arithmetic Numerical accuracy Horner scheme Compensation

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Bilateral sampling randomized singular value decomposition 17

Bilateral sampling randomized singular value decomposition

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17th international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Jiang, Hao Du, Peibing Sun, Tao Li, Housen Cheng, Lizhi Yang, Canqun Natl Univ Def Technol Sch Sci Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Max Planck Inst Biophys Chem Am Fassberg 11 D-37077 Gottingen Germany Natl Univ Def Technol State Key Lab High Performance Computat Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc L Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781509050819

Designing fast singular value decomposition (SVD) is significantly interesting in applications. the random direct SVD (RSVD) has provided a fast scheme to compute the well approximate SVD by unilateral randomized sampling. In this paper, we present an efficient random algorithm in a bilateral sampling way. We also prove that the proposed algorithms can be bounded well and have less computational complexity compared to RSVD when the objective matrix is approximately square. Numerical experiments on graph Laplacian and Hilbert matrix demonstrate the efficiency and stability of the proposed methods.

关键词： Low-rank Singular value decomposition Randomized methods Bilateral sampling Stable

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Green-Aware Online Resource Allocation for Geo-distributed Cloud Data Centers On Multi-source Energy 17

Green-Aware Online Resource Allocation for Geo-Distributed C...

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17th international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： He, Huaiwen Shen, Hong Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Univ Adelaide Sch Comp Sci Adelaide SA Australia Univ Elect Sci & Technol Sch Comp China Zhongshan Inst Chengdu Peoples R China

ISBN: (纸本)9781509050819

Huge energy consumption of large-scale cloud data centers damages the environment with excessive carbon emission. More and more data center operators are seeking to reduce carbon footprint via various types of renewable energy sources. However, the intermittent availability of renewable energy source makes it quite challenging to cooperate the dynamic workload arrivals. In this paper, we investigate how to coordinate multi type renewable energy (e.g. wind power and solar power) in order to reduce the long-term energy cost with spatio-temporal diversity of electricity price for geo-distributed cloud data centers under the constraints of service level agreement (SLA) and carbon footprints. To tackle the randomness of workload arrival, dynamic electricity price change and renewable energy generation, we first formulate the minimizing energy cost problem into a constrained stochastic optimization problem. then, based on Lyapunov optimization technique, we design an online control algorithm which can work without long-term future system information for solving the problem. Finally, we evaluate the effectiveness of the algorithm with extensive simulations based on real-world workload traces, electricity price and historic climate data.

关键词： Transportation Supply and demand Programming Linear programming Approximation algorithms Resource management

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Energy-Aware Programming Model for distributed Infrastructures 24

Energy-Aware Programming Model for Distributed Infrastructur...

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24th Euromicro international conference on parallel, distributed, and Network-Based Processing (PDP)

作者： Lordan, Francesc Ejarque, Jorge Sirvent, Raul Badia, Rosa M. Barcelona Supercomp Ctr Barcelona Spain Univ Politecn Cataluna E-08028 Barcelona Spain CSIC Artificial Intelligence Res Inst IIIA Madrid Spain

ISBN: (纸本)9781467387767

Day after day, cloud technologies are more and more adopted by very diverse types of stakeholders, and this success creates a side-effect problem: the energy spent by this kind of infrastructures is growing bigger every day. With the objective of reducing energy consumption when programming applications for cloud infrastructures, we have implemented energy-aware mechanisms in the COMPSs Programming Model, inside the context of the ASCETiC Project. In this paper, we demonstrate that application-level scheduling can have a big impact on the energy consumed by an application when executed in a heterogeneous cloud. We have implemented an energy-aware scheduling mechanism in COMPSs, together with a versioning technique, and we have run experiments with a use case coming from the real estate sector that proves our hypotheses.

关键词： programming models distributed computing energy awareness cloud computing

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A Variable Markovian based Outlier Detection Method for Multi-dimensional Sequence over Data Stream 17

A Variable Markovian based Outlier Detection Method for Mult...

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17th international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Yang, Dongsheng Wang, Yijie Li, Yongmou Ma, Xingkong Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781509050819

Nowadays sequence data tends to be multidimensional sequence over data stream, it has a large state space and arrives at unprecedented speed. It is a big challenge to design a multi-dimensional sequence outlier detection method to meet the accurate and high speed requirements. the traditional methods can't handle multi-dimensional sequence effectively as they have poor abilities for multi-dimensional sequence modeling, and can't detect outlier timely as they have high computational complexity. In this paper we propose a variable Markovian based outlier detection method for multi-dimensional sequence over data stream, VMOD, which consists of two algorithms: mutual information based feature selection algorithm (MIFS), variable Markovian based sequential analysis algorithm (VMSA). It uses MIFS algorithm to reduce the state space and redundant features, and uses VMSA algorithm to accelerate the outlier detection. through VMOD method, we can improve the detection rate and detection speed. the MIFS algorithm uses mutual information as similarity measures and adopt clustering based strategy to select features, it can improve the abilities for sequence modeling through reducing the state space and redundant features, consequently, to improve the detection rate. the VMSA algorithm use random sample and index structure to accelerate the variable Markovian model construction and reduce the model complexity, consequently, to quicken the outlier detection. the experiments show that VMOD can detect outlier effectively, and reduce the detection time by at least 50% compared with the traditional methods.

关键词： multi-dimensional sequence data stream outlier detection feature selection mutual information variable Markovian

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Deadlock Prevention for Resource Allocation in Heterogeneous distributed Platforms 7

Deadlock Prevention for Resource Allocation in Heterogeneous...

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7th international conference on applications of Digital Information and Web technologies (ICADIWT)

作者： Ha Huy Cuong Nguyen Quangnam Univ Dept Informat Technol Tam Ky Vietnam

ISBN: (纸本)9781614996378;9781614996361

Resource allocation and the associated deadlock prevention problem originated in the design and the implementation of the operating systems, comprising distributed computing, parallel computing and grid computing. this paper presents an improving deadlock prevention algorithm used to schedule the policies of resource supply for resource allocation on heterogeneous distributed platform. In the current scenario, deadlock prevention algorithm using two way search method has created the problem of taking higher time complexity of O (root m) arc traversal as the edge (v,w) is added to the graph. this paper proposes the algorithms for allocating multiple resources to competing services running in virtual machines on a heterogeneous distributed platform. We have implemented and performed our algorithm proposed by using CloudSim simulator. the experiments results show that our algorithm can quickly prevent deadlock and then resolve the situation of approximately orders of magnitude in practical cases.

关键词： Cloud computing Resource allocation Heterogeneous distributed Platforms Prevention Deadlock

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An Attack-Resilient Middleware Architecture for Grid Integration of distributed Energy Resources

An Attack-Resilient Middleware Architecture for Grid Integra...

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9th IEEE Int Conf on Internet of things / 12th IEEE Int Conf on Green Comp and Communicat / 9th IEEE Int Conf on Cyber, Phys, and Social Comp / IEEE Int Conf on Smart Data

作者： Wu, Yifu Mendis, Gihan J. He, Youbiao Wei, Jin Hodge, Bri-Mathias Univ Akron Akron OH 44325 USA Natl Renewable Energy Lab Golden CO 80401 USA

ISBN: (纸本)9781509058808

In recent years, the increasing penetration of distributed Energy Resources (DERs) has made an impact on the operation of the electric power systems. In the grid integration of DERs, data acquisition systems and communications infrastructure are crucial technologies to maintain system economic efficiency and reliability. Since most of these generators are relatively small, dedicated communications investments for every generator are capital cost prohibitive. Combining real-time attack-resilient communications middleware with Internet of things (IoTs) technologies allows for the use of existing infrastructure. In our paper, we propose an intelligent communication middleware that utilizes the Quality of Experience (QoE) metrics to complement the conventional Quality of Service (QoS) evaluation. Furthermore, our middleware employs deep learning techniques to detect and defend against congestion attacks. the simulation results illustrate the efficiency of our proposed communications middleware architecture.

关键词： Middleware Quality of service Protocols Relays Real-time systems Computer architecture

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Track Chair's Report: Convergence of distributed Clouds, Grids and their Management CDCGM 2016 25

Track Chair's Report: Convergence of Distributed Clouds, Gri...

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25th IEEE international conference on Enabling technologies - Infrastructure for Collaborative Enterprises (WETICE)

作者： Messina, Fabrizio Mikkilineni, Rao Morana, Giovanni Univ Catania Dept Math & Informat Vle Andrea Doria 6 I-95125 Catania Italy C3DNA Inc Santa Clara CA USA C3DNA Inc R&D & Engn Catania Italy

ISBN: (纸本)9781509016631

this track has made significant contribution to advance the current state of the art in enterprise distributed application management across datacenters, and clouds public or private. the scale of distributed applications and their management have taken a new dimension demanding tolerance to wild fluctuations both in workloads and available computing resource pools. A new computing model proposing extensions to the current von Neumann implementation of the Turing machine was contributed by the efforts of participating in this conference since 2009. the result is a practical demonstration of these concepts, showing infrastructure agnostic auto-failover, auto-scaling and live-migration of even state-sensitive distributed applications. By eliminating virtual image motion which is the current state of the art, this approach improves efficiency and reduces complexity and tool fatigue prevalent in todays enterprises. In addition, a review of the super recursive algorithms and inductive Turing machines in this track provides the theoretical foundation for new computing. In addition, there are five full papers describing advances in current distributed and cloud computing practices dealing with quality of service, adaptive algorithms and software defined network architectures. One short paper discusses a new approach for resource allocation in distributed systems. On the eve of the 25th anniversary of WETICE, this track attests to its success where the work started here has contributed to both theoretical and practical application going from ideation to application within a span of 6 years.

关键词： Cloud computing grid computing distributed Intelligent Managed Element Networks distributed Services Management Services Virtualization parallel computing

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Online credit card fraud detection: A hybrid framework with big data technologies 15

Online credit card fraud detection: A hybrid framework with ...

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Joint 15th IEEE international conference on Trust, Security and Privacy in computing and Communications, 10th IEEE international conference on Big Data Science and Engineering and 14th IEEE international Symposium on parallel and distributed Processing with applications, IEEE TrustCom/BigDataSE/ISPA 2016

作者： Dai, You Yan, Jin Tang, Xiaoxin Zhao, Han Guo, Minyi Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781509032051

In this paper, we focus on designing an online credit card fraud detection framework with big data technologies, by which we want to achieve three major goals: 1) the ability to fuse multiple detection models to improve accuracy, 2) the ability to process large amount of data and 3) the ability to do the detection in real time. To accomplish that, we propose a general workflow, which satisfies most design ideas of current credit card fraud detection systems. We further implement the workflow with a new framework which consists of four layers: distributed storage layer, batch training layer, key-value sharing layer and streaming detection layer. With the four layers, we are able to support massive trading data storage, fast detection model training, quick model data sharing and real-time online fraud detection, respectively. We implement it with latest big data technologies like Hadoop, Spark, Storm, HBase, etc. A prototype is implemented and tested with a synthetic dataset, which shows great potentials of achieving the above goals. © 2016 IEEE.

关键词： Big data

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