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PEGASUS: A System for Large-Scale Graph Processing PEGASUS:一个大规模图形处理系统
Pub Date : 1900-01-01 DOI: 10.1201/b17112-9
Charalampos E. Tsourakakis
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引用次数: 16
An Overview of the NoSQL World NoSQL世界概览
Pub Date : 1900-01-01 DOI: 10.1201/b17112-10
Liang Zhao, S. Sakr, Anna Liu
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引用次数: 0
Performance Analysis for Large IaaS Clouds 大型IaaS云的性能分析
Pub Date : 1900-01-01 DOI: 10.1201/b17112-19
R. Ghosh, F. Longo, Kishor S. Trivedi
IaaS clouds are major enablers of data-intensive cloud applications because they provide necessary computing capacity for managing Big Data environments. In a typical IaaS cloud, virtual machine (VM) instances deployed on physical machines (PM) are provided to the users for their computing needs. Recently, IaaS cloud providers are realizing that merely providing the basic functionalities for Big Data processing is not sufficient to survive intense business competitions. Rather, the performance of the cloud provided service is an equally important factor when a CONTENTS
IaaS云是数据密集型云应用程序的主要推动者,因为它们为管理大数据环境提供了必要的计算能力。在典型的IaaS云中,部署在物理机(PM)上的虚拟机(VM)实例提供给用户以满足其计算需求。最近,IaaS云提供商意识到,仅仅提供大数据处理的基本功能是不足以在激烈的商业竞争中生存下来的。相反,云提供的服务的性能是一个同样重要的因素
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引用次数: 0
Virtualizing Resources for the Cloud 虚拟化云资源
Pub Date : 1900-01-01 DOI: 10.1201/b17112-17
Mohammad Hammoud, M. Sakr
Virtualization is at the core of cloud computing. It lies on top of the cloud infrastructure, whereby virtual resources (e.g., virtual CPUs, memories, disks and networks) are constructed from the underlying physical resources and act as proxies to them. As is the case with the idea of cloud computing, which was first introduced in the 1960s [1], virtualization can be traced back to the 1970s [55]. Forty years ago, the mainframe computer systems were extremely large and expensive. To address expanding user needs and costly machine ownerships, the IBM 370 architecture, announced in 1970, offered complete virtual machines (virtual hardware images) to different programs running at the same computer hardware. Over time, computer hardware became less expensive and users started migrating to low-priced desktop machines. This drove the adoption of the virtualization technology to fade for a while. Today, virtualization is enjoying a resurgence in popularity with a number of research projects and commercial systems providing virtualization solutions for commodity PCs, servers, and the cloud. In this chapter, we present various ingredients of the virtualization technology and the crucial role it plays in enabling the cloud computing paradigm. First, we identify major reasons for why virtualization is becoming important, especially for the cloud. Second, we indicate how multiple software images can run side-by-side on physical resources while attaining security, resource and failure isolations. Prior to delving into more details about virtualization, we present a brief background requisite for understanding how physical resources can be virtualized. In particular,
虚拟化是云计算的核心。它位于云基础设施之上,虚拟资源(例如,虚拟cpu、内存、磁盘和网络)由底层物理资源构建,并充当它们的代理。正如云计算的概念在20世纪60年代首次提出[1]一样,虚拟化可以追溯到20世纪70年代[55]。40年前,大型计算机系统极其庞大和昂贵。为了满足不断扩展的用户需求和昂贵的机器所有权,IBM 370体系结构于1970年发布,为在同一计算机硬件上运行的不同程序提供了完整的虚拟机(虚拟硬件映像)。随着时间的推移,计算机硬件变得越来越便宜,用户开始转向价格低廉的台式电脑。这使得虚拟化技术的采用在一段时间内淡出了人们的视野。如今,随着大量研究项目和商业系统为商用pc、服务器和云提供虚拟化解决方案,虚拟化正在重新流行起来。在本章中,我们将介绍虚拟化技术的各种组成部分,以及它在实现云计算范式方面所起的关键作用。首先,我们确定虚拟化变得重要的主要原因,尤其是对云。其次,我们指出了多个软件映像如何在物理资源上并行运行,同时获得安全性、资源和故障隔离。在深入研究虚拟化的更多细节之前,我们先介绍一下理解如何虚拟化物理资源所必需的简单背景知识。特别是,
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引用次数: 0
Advanced Algorithms for Efficient Approximate Duplicate Detection in Data Streams Using Bloom Filters 使用Bloom过滤器在数据流中高效近似重复检测的高级算法
Pub Date : 1900-01-01 DOI: 10.1201/b17112-14
Sourav Dutta, A. Narang
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引用次数: 0
Consistency Management in Cloud Storage Systems 云存储一致性管理
Pub Date : 1900-01-01 DOI: 10.1201/b17112-11
Houssem-Eddine Chihoub, Shadi Ibrahim, Gabriel Antoniu, María S. Pérez
With the emergence of cloud computing, many organizations have moved their data to the cloud in order to provide scalable, reliable and high available services. As these services mainly rely on geographically-distributed data replication to guarantee good performance and high availability, consistency comes into question. The CAP theorem discusses tradeoffs between consistency, availability, and partition tolerance, and concludes that only two of these three properties can be guaranteed simultaneously in replicated storage systems. With data growing in size and systems growing in scale, new tradeoffs have been introduced and new models are emerging for maintaining data consistency. In this chapter, we discuss the consistency issue and describe the CAP theorem as well as its limitations and impacts on big data management in large scale systems. We then briefly introduce several models of consistency in cloud storage systems. Then, we study some state-of-the-art cloud storage systems from both enterprise and academia, and discuss their contribution to maintaining data consistency. To complete our chapter, we introduce the current trend toward adaptive consistency in big data systems and introduce our dynamic adaptive consistency solution (Harmony). We conclude by discussing the open issues and challenges raised regarding consistency in the cloud.
随着云计算的出现,许多组织为了提供可扩展、可靠和高可用性的服务,已经将他们的数据迁移到云中。由于这些服务主要依赖于地理上分布的数据复制来保证良好的性能和高可用性,因此一致性成为问题。CAP定理讨论了一致性、可用性和分区容忍度之间的权衡,并得出结论,在复制存储系统中,这三个属性中只有两个可以同时得到保证。随着数据规模的增长和系统规模的扩大,引入了新的权衡,并且出现了维护数据一致性的新模型。在本章中,我们讨论了一致性问题,并描述了CAP定理,以及它对大规模系统中大数据管理的局限性和影响。然后,我们简要介绍了云存储系统中的几个一致性模型。然后,我们研究了一些来自企业和学术界的最先进的云存储系统,并讨论了它们对保持数据一致性的贡献。为了完成本章,我们介绍了大数据系统中自适应一致性的当前趋势,并介绍了我们的动态自适应一致性解决方案(Harmony)。最后,我们讨论了关于云中的一致性的开放问题和挑战。
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引用次数: 5
MapReduce Family of Large-Scale Data-Processing Systems MapReduce系列大规模数据处理系统
Pub Date : 1900-01-01 DOI: 10.1201/b17112-3
S. Sakr, Anna Liu, A. Fayoumi
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引用次数: 1
Incremental MapReduce Computations 增量MapReduce计算
Pub Date : 1900-01-01 DOI: 10.1201/b17112-5
Pramod Bhatotia, Alexander Wieder, Umut A. Acar, R. Rodrigues
Abstract Distributed processing of large data sets is an area that received much attention from researchers and practitioners over the last few years. In this context, several proposals exist that leverage the observation that data sets evolve over time, and as such there is often a substantial overlap between the input to consecutive runs of a data processing job. This allows the programmers of these systems to devise an e ffi cient logic to update the output upon an input change. However, most of these systems lack compatibility existing models and require the programmer to implement an application-specific dynamic algorithm, which increases algorithm and code complexity. In this chapter, we describe our previous work on building a platform called Incoop, which allows for running MapReduce computations incrementally and transparently. Incoop detects changes between two files that are used as inputs to consecutive MapReduce jobs, and e ffi ciently propagates those changes until the new output is produced. The design of Incoop is based on memoizing the results of previously run tasks, and reusing these results whenever possible. Doing this e ffi ciently introduces several technical challenges that are overcome with novel concepts, such as a large-scale storage system that e ffi ciently computes deltas between two inputs, a Contraction phase to break up the work of the Reduce phase, and an a ffi nity-based scheduling algorithm. This chapter presents the motivation and design of Incoop, as well as a complete evaluation using several application benchmarks. Our results show significant performance improvements without changing a single line of application code.
大型数据集的分布式处理是近年来备受研究者和实践者关注的一个领域。在这种情况下,存在一些利用数据集随时间演变的观察结果的建议,因此,在连续运行的数据处理作业的输入之间通常存在大量重叠。这允许这些系统的程序员设计一个有效的逻辑来更新输入更改后的输出。然而,这些系统中的大多数缺乏与现有模型的兼容性,并且要求程序员实现特定于应用程序的动态算法,这增加了算法和代码的复杂性。在本章中,我们描述了我们之前构建一个名为Incoop的平台的工作,该平台允许以增量和透明的方式运行MapReduce计算。inoop检测作为连续MapReduce作业输入的两个文件之间的更改,并有效地传播这些更改,直到产生新的输出。inoop的设计基于记忆先前运行任务的结果,并尽可能重用这些结果。高效地完成这一任务会带来一些技术挑战,这些挑战可以通过一些新概念来克服,比如高效地计算两个输入之间的增量的大规模存储系统,分解Reduce阶段工作的收缩阶段,以及基于ffi的调度算法。本章介绍了inoop的动机和设计,以及使用几个应用程序基准的完整评估。我们的结果显示了显著的性能改进,而无需更改任何一行应用程序代码。
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引用次数: 2
Large-Scale RDF Processing with MapReduce MapReduce的大规模RDF处理
Pub Date : 1900-01-01 DOI: 10.1201/b17112-6
A. Schätzle, Martin Przyjaciel-Zablocki, Thomas Hornung, G. Lausen
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引用次数: 1
期刊
Large Scale and Big Data
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