Lecture Notes in Computer Science: 5931 Series Milestone & Key Figures in Cloud Computing

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《计算机科学讲义》(Lecture Notes in Computer Science, LNCSE)是一系列起始于1973年的专业学术出版物，由Gerhard Goos、Juris Hartmanis 和 Jan van Leeuwen三位学者共同创办，旨在发表计算机科学领域的前沿研究成果。这一系列书籍涵盖广泛，包括但不限于数据处理、人工智能、分布式计算等主题，对全球顶尖学术界产生了深远影响。《计算机科学讲义》5931卷的出版标志着该系列在云计算领域的深入探索。2009年，第一国际云计算会议(CloudCom2009)的论文集作为该系列的一部分，编辑团队由Martin Gilje Jaatun Gansen Zhao、Chunming Rong等人组成。会议的焦点集中在云计算这一新兴技术上，这是当时信息技术的一个热点话题，它将传统的计算模型扩展到基于互联网的分布式基础设施，能够提供按需、可扩展的计算和存储服务。在这个卷中，作者们讨论了云技术的关键概念、架构设计、服务模式以及其潜在的应用和挑战。例如，Hadoop作为云计算领域的重要组成部分，可能被特别提及，因为它是一个开源框架，支持大数据分布式处理，常用于海量数据的存储和分析。Hadoop的并行计算模型和MapReduce算法在此讲义中被详细介绍，展示了其在云计算环境中的核心作用。此外，其他部分可能涉及云计算的安全性、经济效益、隐私保护以及如何通过云平台实现企业数字化转型等内容。知名学者如Takeo Kanade（卡内基梅隆大学）、Josef Kittler（萨里大学）和Demetri Terzopoulos（加州大学洛杉矶分校）等人的贡献反映了他们在这个领域的专业知识和研究深度。《计算机科学讲义》5931卷为读者提供了关于云计算及其相关技术的深入洞察，是研究者、教师和学生了解和探索最新发展趋势的重要资源。通过阅读和理解这些内容，人们可以了解到云技术如何改变传统计算环境，以及未来可能的发展趋势。

XVIII Table of Contents

Green Cloud on the Horizon ....................................... 451

Mufajjul Ali

Industrial Cloud: Toward Inter-enterprise Integration ................. 460

Tomasz Wiktor Wlodarczyk, Chunming Rong, and

Kari Anne Haaland Thorsen

Community Cloud Computing ..................................... 472

Alexandros Marinos and Gerard Briscoe

A Semantic Grid Oriented to E-Tourism ............................ 485

Xiao Ming Zhang

Irregular Community Discovery for Social CRM in Cloud Computing ... 497

Jin Liu, Fei Liu, Jing Zhou, and ChengWan He

A Contextual Information Acquisition Approach Based on Semantics

and Mashup Technology .......................................... 510

Yangfan He, Lu Li, Keqing He, and Xiuhong Chen

Evaluating MapReduce on Virtual Machines: The Hadoop Case ........ 519

Shadi Ibrahim, Hai Jin, Lu Lu, Li Qi, Song Wu, and Xuanhua Shi

APFA: Asynchronous Parallel Finite Automaton for Deep Packet

Inspection in Cloud Computing .................................... 529

Yang Li, Zheng Li, Nenghai Yu, and Ke Ma

3. Short Papers

Secure Document Service for Cloud Computing ...................... 541

Jin-Song Xu, Ru-Cheng Huang, Wan-Ming Huang, and Geng Yang

Privacy of Value-Added Context-Aware Service Cloud ................ 547

Xin Huang, Yin He, Yifan Hou, Lisi Li, Lan Sun, Sina Zhang,

Yang Jiang, and Tingting Zhang

A Simple Technique for Securing Data at Rest Stored in a Computing

Cloud .......................................................... 553

Jeﬀ Sedayao, Steven Su, Xiaohao Ma, Minghao Jiang, and Kai Miao

Access Control of Cloud Service Based on UCON .................... 559

Chen Danwei, Huang Xiuli, and Ren Xunyi

Replica Replacement Strategy Evaluation Based on Grid Locality ...... 565

Lihua Ai and Siwei Luo

Performance Evaluation of Cloud Service Considering Fault Recovery ... 571

Bo Yang, Feng Tan, Yuan-Shun Dai, and Suchang Guo

M.G. Jaatun, G. Zhao, and C. Rong (Eds.): CloudCom 2009, LNCS 5931, pp. 2–18, 2009.

Biomedical Case Studies in Data Intensive Computing

Geoffrey Fox

1,2

, Xiaohong Qiu

, Scott Beason

, Jong Choi

1,2

, Jaliya Ekanayake

1,2

Thilina Gunarathne

1,2

, Mina Rho

, Haixu Tang

, Neil Devadasan

, and Gilbert Liu

Pervasive Technology Institute

School of Informatics and Computing

The Polis Center

School of Medicine Indiana University

{gcf,xqiu,smbeason,jychoi,jekanaya,tgunarat,mrho,

hatang}@indiana.edu,

{ndevadas,gcliu}@iupui.edu

Abstract. Many areas of science are seeing a data deluge coming from new

instruments, myriads of sensors and exponential growth in electronic records.

We take two examples – one the analysis of gene sequence data (35339 Alu

sequences) and other a study of medical information (over 100,000 patient

records) in Indianapolis and their relationship to Geographic and Information

System and Census data available for 635 Census Blocks in Indianapolis. We

look at initial processing (such as Smith Waterman dissimilarities), clustering

(using robust deterministic annealing) and Multi Dimensional Scaling to map

high dimension data to 3D for convenient visualization. We show how scaling

pipelines can be produced that can be implemented using either cloud

technologies or MPI which are compared. This study illustrates challenges in

integrating data exploration tools with a variety of different architectural

requirements and natural programming models. We present preliminary results

for end to end study of two complete applications.

Keywords: MapReduce, Clouds, MPI, Clustering, Sequencing, Dryad. Hadoop.

1 Introduction

Data Intensive Computing is very popular at this time. Partly this is due to the well

understood data deluge with all activities including science, government and modern

Internet (Web 2.0) systems all generating exponentially increasing data. One special

driver is that Web Search and related data mining can use an especially simple

programming model MapReduce of which there are now several implementations. It

is attractive to understand how generally applicable MapReduce is to other data

intensive problems as one can expect excellent commercial support for software in

this area. We have looked at the impact of clouds and compared Yahoo (Hadoop) and

Microsoft (Dryad) implementations of the MapReduce step presenting Dryad results

here. We choose two biomedical applications. The first addresses the structure of

Gene families and the processing steps involve sequence alignment, clustering and

visualization after projecting sequences to 3 dimensions using Multidimensional

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