Open Cloud Technologies

Open Cloud Technologies

Andres-Leonardo Martinez-Ortiz (Telefonica O2, UK)
Copyright: © 2012 |Pages: 17
DOI: 10.4018/978-1-4666-0098-0.ch001
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Abstract

The open source perspective offers an interesting insight about cloud computing technologies: in one hand, cloud systems belong to the category of the Ultra-Large-Scale (ULS) systems, i.e. very complex systems where conventional approach for the technological development does not work. For such as systems, Free Libre Open Source Software (FLOSS) licensing attracts innovation from the developers’ communities, reduces the risks of technology adoption and fosters the interoperability between systems and the creation of open standards. In the other hand, the current systems are far from achieving interoperability; even the FLOSS´s principles remain pending for many components in the architecture of the main cloud solutions, and for these reasons many FLOSS evangelists do not recommend using them. As a balance between the obvious drawbacks and benefits, recently a new strategy has appeared: Free/Open Services. However, it seems difficult to find short term solutions. This chapter illustrates both ideas, highlighting the pros and cons of these technologies, including a reference of main “open cloud” groups and open source technologies for the cloud. The rest of the book will include additional and deeper descriptions of some of the most interesting open cloud technologies.
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Ultra-Large Scale Cloud Systems

Cloud computing systems belong to the category of Ultra-Large Scale Systems. To justify this affirmation, they can be described following the classical report about ULS Systems (Carnegie Mellon, 2006). This allows us to argument why open source strategy is convenient for cloud computing systems. ULS systems are huge in term of LOC, people involved, data stored or managed or number of interconnection between subsystems. But ULS systems are not only a matter of size but scale. With the scale arise the main features of ULS of system:

  • Decentralization, in terms of data, development, evolution and operational control

  • Inherently conflicting, with unknowable and diverse requirements, due to the huge number of stakeholders.

  • Continuous evolution and deployment, so the integration of new features will continue all along the system life, evolving continuously.

  • Formed by heterogeneous, inconsistent and changing parts.

  • Boundaries between people and system shrinking i.e. the human will be not just user of the system but part of it as well.

  • Normal failures, i.e. the system has to be designed taking into account that the failures will part of its “regular” activity.

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