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A common language could help by providing an interoperable way for applications to access compute services across several different clouds; so far, several people have attempted to provide a language for MapReduce-style parallel programming, including some that extend SQL in a wa...

The Case for Cloud Computing

It Professional, no. 2 (2009): 23-27

被引用650|浏览17
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摘要

To understand clouds and cloud computing, we must first understand the two different types of clouds. The author distinguishes between clouds that provide on-demand computing instances and those that provide on-demand computing capacity.

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简介
  • To understand clouds and cloud computing, the authors must first understand the two different types of clouds.
  • Cloud computing doesn’t yet have a standard definition, but a good working description of it is to say that clouds, or clusters of distributed computers, provide on-demand resources and services over a network, usually the Internet, with the scale and reliability of a data center.
重点内容
  • To understand clouds and cloud computing, we must first understand the two different types of clouds
  • Cloud computing is often offered with a pricing model that lets you pay as you go and for just the services that you need
  • Third parties can take advantage of economies of scale to provide a level of security that might not be costeffective for smaller companies
  • Third parties can take advantage of economies of scale to provide a level of security that might not be cost-effective for smaller companies, but a downside is two or more organizations might share the same physical resource and not be aware of it
  • A common language could help by providing an interoperable way for applications to access compute services across several different clouds; so far, several people have attempted to provide a language for MapReduce-style parallel programming, including some that extend SQL in a way that supports this style of programming, but no single language has emerged as the clear winner yet
  • Cloudstone is a toolkit consisting of an open source Web 2.0 social application, a set of tools for generating workloads, a set of tools for performance monitoring, and a recommended methodology for computing a metric that quantifies the dollars per user per month that a given cloud requires
结果
  • The Eucalyptus system is an open source option that provides on-demand computing instances and shares the same APIs as Amazon’s EC2 cloud.
  • Cloud computing is often offered with a pricing model that lets you pay as you go and for just the services that you need.
  • Organizations, have several options for obtaining cloud services, including running their own private clouds or buying cloud services from a third party using the elastic, usage-based pricing model.
  • Cloud computing’s usage-based pricing model offers several advantages, including reduced capital expense, a low barrier to entry, and the ability to scale up as demand requires, as well as to support brief surges in capacity.
  • Third parties can take advantage of economies of scale to provide a level of security that might not be cost-effective for smaller companies, but a downside is two or more organizations might share the same physical resource and not be aware of it.
  • A common language could help by providing an interoperable way for applications to access compute services across several different clouds; so far, several people have attempted to provide a language for MapReduce-style parallel programming, including some that extend SQL in a way that supports this style of programming, but no single language has emerged as the clear winner yet.
  • The authors are in a somewhat analogous position: cloud service providers have pushed back a bit on standardizing, the ability of different clouds to interoperate would enable an interesting new class of applications.
结论
  • For clouds that provide on-demand instances, a recent benchmark called Cloudstone has emerged.[6] Cloudstone is a toolkit consisting of an open source Web 2.0 social application, a set of tools for generating workloads, a set of tools for performance monitoring, and a recommended methodology for computing a metric that quantifies the dollars per user per month that a given cloud requires.
  • Has cloud computing scaled computing to the data center, but it has introduced software, systems, and programming models that significantly reduce the complexity of accessing and using these resources.
引用论文
  • J. Dean and S. Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters,” Comm. ACM, vol. 51, no. 1, 2008, pp. 107–113.
    Google ScholarLocate open access versionFindings
  • S. Ghemawat, H. Gobioff, and S.-T. Leung, “The Google File System,” Proc. 19th ACM Symp. Operating Systems Principles, ACM Press, 2003, pp. 29–43.
    Google ScholarLocate open access versionFindings
  • J. Dean and S. Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters,” Proc. 6th Symp. Operating System Design and Implementation, Usenix Assoc., 2004, pp. 137–150.
    Google ScholarLocate open access versionFindings
  • F. Chang et al., “Bigtable: A Distributed Storage System for Structured Data,” Proc. 7th Symp. Operating System Design and Implementation, Usenix Assoc., 2006, pp. 205–218.
    Google ScholarLocate open access versionFindings
  • R.L. Grossman and Y. Gu, “Data Mining Using High-Performance Clouds: Experimental Studies Using Sector and Sphere,” Proc. 14th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, ACM Press, 2008, pp. 920–927.
    Google ScholarLocate open access versionFindings
  • W. Sobel et al., “Cloudstone: Multi-Platform MultiLanguage Benchmark and Measurement Tools for Web 2.0,” Proc. Cloud Computing and Its Applications, 2008; www.cca08.org/papers.php. Visit www.computer.org/itpro and click on “write for IT Pro.”
    Locate open access versionFindings
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