Large Scale and Big Data最新文献_第2页

PEGASUS: A System for Large-Scale Graph Processing PEGASUS:一个大规模图形处理系统

Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-9

Charalampos E. Tsourakakis

引用次数: 16

An Overview of the NoSQL World NoSQL世界概览

Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-10

Liang Zhao, S. Sakr, Anna Liu

引用次数: 0

Performance Analysis for Large IaaS Clouds 大型IaaS云的性能分析

Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-19

R. Ghosh, F. Longo, Kishor S. Trivedi

引用次数: 0

Virtualizing Resources for the Cloud 虚拟化云资源

Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-17

Mohammad Hammoud, M. Sakr

{"title":"Virtualizing Resources for the Cloud","authors":"Mohammad Hammoud, M. Sakr","doi":"10.1201/b17112-17","DOIUrl":"https://doi.org/10.1201/b17112-17","url":null,"abstract":"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,","PeriodicalId":448182,"journal":{"name":"Large Scale and Big Data","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121603616","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: 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

Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-14

Sourav Dutta, A. Narang

引用次数: 0

Consistency Management in Cloud Storage Systems 云存储一致性管理

Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-11

Houssem-Eddine Chihoub, Shadi Ibrahim, Gabriel Antoniu, María S. Pérez

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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

Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-3

S. Sakr, Anna Liu, A. Fayoumi

引用次数: 1

Incremental MapReduce Computations 增量MapReduce计算

Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-5

Pramod Bhatotia, Alexander Wieder, Umut A. Acar, R. Rodrigues

{"title":"Incremental MapReduce Computations","authors":"Pramod Bhatotia, Alexander Wieder, Umut A. Acar, R. Rodrigues","doi":"10.1201/b17112-5","DOIUrl":"https://doi.org/10.1201/b17112-5","url":null,"abstract":"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.","PeriodicalId":448182,"journal":{"name":"Large Scale and Big Data","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123112337","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: 2

Large-Scale RDF Processing with MapReduce MapReduce的大规模RDF处理

Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-6

A. Schätzle, Martin Przyjaciel-Zablocki, Thomas Hornung, G. Lausen

引用次数: 1