2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing最新文献_第2页

Intelligent Stock Market Analysis System - A Fundamental and Macro-economical Analysis Approach 智能股票市场分析系统——一种基础和宏观经济分析方法

2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Pub Date : 2014-09-01 DOI: 10.1109/SYNASC.2014.75

M. Tirea, V. Negru

引用次数: 8

Reducing Partial Equivalence to Partial Correctness 将部分等价化为部分正确性

2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Pub Date : 2014-09-01 DOI: 10.1109/SYNASC.2014.30

Stefan Ciobaca

引用次数: 9

Using Models at Runtime to Support Adaptable Monitoring of Multi-clouds Applications 在运行时使用模型支持多云应用程序的自适应监控

2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Pub Date : 2014-09-01 DOI: 10.1109/SYNASC.2014.60

Lorenzo Cianciaruso, Francesco di Forenza, E. D. Nitto, Marco Miglierina, Nicolas Ferry, Arnor Solberg

{"title":"Using Models at Runtime to Support Adaptable Monitoring of Multi-clouds Applications","authors":"Lorenzo Cianciaruso, Francesco di Forenza, E. D. Nitto, Marco Miglierina, Nicolas Ferry, Arnor Solberg","doi":"10.1109/SYNASC.2014.60","DOIUrl":"https://doi.org/10.1109/SYNASC.2014.60","url":null,"abstract":"The ability to run and manage multi-clouds applications (i.e., Applications that run on multiple clouds) allows exploiting the peculiarities of each cloud solution and hence improves non-functional aspects such as availability, cost, and scalability. Monitoring such multi-clouds applications is fundamental to track the health of the applications themselves and of their underlying infrastructures as well as to decide when and how to adapt their behaviour and deployment. It is clear that, not only the application but also the corresponding monitoring infrastructure should dynamically adapt in order to (i) be optimized to the application context (e.g., Adapting the frequency of monitoring to reduce network load), (ii) to enable the co-evolution of the monitoring platform together with the cloud application (e.g., If a service migrates from one provider to another, the monitoring activities have to be adapted accordingly). In this paper, we present a model-based platform for the dynamic provisioning, deployment, and monitoring of multi-clouds applications whose monitoring activities can be automatically and dynamically adapted to best fit with the actual deployment of the application.","PeriodicalId":150575,"journal":{"name":"2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122062186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Expressing BBUFs Lookup Using the π-Calculus 使用π-微积分表达bbuf查找

2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Pub Date : 2014-09-01 DOI: 10.1109/SYNASC.2014.74

Gabriel Ciobanu, Dan Cojocar

引用次数: 0

Optimization Techniques within the Hadoop Eco-system: A Survey Hadoop生态系统中的优化技术:调查

2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Pub Date : 2014-09-01 DOI: 10.1109/SYNASC.2014.65

Giulia Rumi, Claudia Colella, D. Ardagna

{"title":"Optimization Techniques within the Hadoop Eco-system: A Survey","authors":"Giulia Rumi, Claudia Colella, D. Ardagna","doi":"10.1109/SYNASC.2014.65","DOIUrl":"https://doi.org/10.1109/SYNASC.2014.65","url":null,"abstract":"Nowadays, we live in a digital world producing data at an impressive speed: data are large, change quickly, and are often too complex to be processed by existing tools. The problem is to extract knowledge from all these data in an efficient way. MapReduce is a data parallel programming model for clusters of commodity machines that was created to address this problem. In this paper we provide an overview of the Hadoop ecosystem. We introduce the most significative approaches supporting automatic, on-line resource provisioning. Moreover, we analyse optimization approaches proposed in frameworks built on top of MapReduce, such as Pig and Hive, which point out the importance of scheduling techniques in MapReduce when multiple workflows are executed concurrently. Therefore, the default Hadoop schedulers are discussed along with some enhancements proposed by the research community. The analysis is performed to highlight how research contributions try to address common Hadoop points of weakness. As it stands out from our comparison, none of the frameworks surpasses the others and a fair evaluation is also difficult to be performed, the choice of the framework must be related to the specific application goal but there is no single solution that addresses all the issues typical of MapReduce.","PeriodicalId":150575,"journal":{"name":"2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126573382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Using the Distribution of Cells by Dimension in a Cylindrical Algebraic Decomposition 利用柱面代数分解中按维分布的单元

2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Pub Date : 2014-09-01 DOI: 10.1109/SYNASC.2014.15

D. Wilson, M. England, R. Bradford, J. Davenport

引用次数: 16

Topological Image Analysis and (Normalised) Representations for Plant Phenotyping 植物表型的拓扑图像分析和(归一化)表示

2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Pub Date : 2014-09-01 DOI: 10.1109/SYNASC.2014.83

Ines Janusch, W. Kropatsch, Wolfgang Busch

引用次数: 3

Spiking Neural P Systems - A Quick Survey and Some Research Topics 脉冲神经系统-一个快速调查和一些研究课题

2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Pub Date : 2014-09-01 DOI: 10.1109/SYNASC.2014.11

G. Paun

引用次数: 0

A Population-Based Incremental Learning Method for Constrained Portfolio Optimisation 约束投资组合优化的基于群体的增量学习方法

2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Pub Date : 2014-09-01 DOI: 10.1109/SYNASC.2014.36

Yan Jin, R. Qu, J. Atkin

引用次数: 10

Interactive Data Exploration for High-Performance Fluid Flow Computations through Porous Media 通过多孔介质进行高性能流体流动计算的交互式数据探索

2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Pub Date : 2014-09-01 DOI: 10.1109/SYNASC.2014.68

N. Perovic, J. Frisch, R. Mundani, E. Rank

引用次数: 1