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On Foundations of Services Interoperation in Cloud Computing

  • Deyi Li
  • Haisu Zhang
  • Yuchao Liu
  • Guishen Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6213)

Abstract

With a long-run accumulation of IT technologies, cloud computing becomes a new revolution after PC revolution in 1980s, the Internet revolution in 1990s, and the mobile Internet revolution in 2000s. Cloud computing is a paradigm of Internet computing, which takes software as a service oriented to a number of users with changing requirements from time to time, at the same time, takes the response of a requirement as an up-to-date best effort rather than a unique precise one. It is changing the way we share data, information and knowledge.

Services support direct reusing of application programs instead of software deployment, and improve usability of resource sharing. Clouds can be regarded as an enabler for interoperation of large scale service provisioning. Therefore, assembling of software became services aggregation under the mature understanding of interoperability.

Services interoperation may happen between a user (group) and a service, or among services. Users or their groups, services or their aggregations are all called agents here. The description of interoperability of agents focuses on three issues: their role, goal, and combination of services. The role issue characterizes the organization, roles, and actors of an agent, and describes the interaction and cooperation among them. The goal issue depicts the decomposition of goals and determines the constraint relationship among goals. While the two issues compose the problem domain, the third issue is related to the solution domain, in which process distinguishes atomic processes, and composite processes, and defines the input/output together with precondition/effect of processes respectively, the service guides the construction of service chains and aggregation of resources. Usually, the description in problem domain is qualitative, while the specification in solution domain is quantitative.

Keywords

Cloud Computing Gaussian Mixture Model Problem Domain Cloud Model Solution Domain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Deyi Li
    • 1
  • Haisu Zhang
    • 2
  • Yuchao Liu
    • 1
    • 3
  • Guishen Chen
    • 1
  1. 1.Institute of Electronic System EngineeringBeijingChina
  2. 2.College of Command AutomationPLA University of Science and TechnologyNanjingChina
  3. 3.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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