Earticle

초록

영어

For the large scale services with high-dimensional QoS attributes and distributed environment, traditional service selection approaches are faced with unprecedented challenges in terms of efficiency and performance of QoS. To address these challenges, we propose a three-phase large scale Skyline service selection framework for service composition in clouds. This framework adopts distributed parallel Skyline computation with MapReduce to prune redundant candidate services, and employs parallel multi-objective optimization algorithm based on MapReduce to select Skyline services from the tremendous amount of Skyline services warehouse for composing single service into a set of more powerful Skyline composite services, then applies Top-k query processing technology or multiple attribute decision making support method to select k Skyline composite services from the set of Skyline composite services. Through theoretical analysis, the framework can efficiently solve the service selection problem with large scale services, high-dimensional QoS in cloud computing environment, and quickly generate better composite services with the global optimal QoS.

목차

Abstract
 1. Introduction
 2. A Three-Phase Large Scale Skyline Service Selection Framework
  2.1 Framework Design
  2.2 The Theoretical Analysis of this Framework
 3. Related Work
 4. Conclusions
 Acknowledgements
 References

키워드

저자

• Jinzhong LI [ Department of Computer Science and Technology, Jinggangshan University, Ji’an Jiangxi 343009, China, Key laboratory of watershed ecology and geographical environment monitoring, NASG, Jinggangshan University, Ji’an Jiangxi 343009, China, Department of Computer Science and Technology, Tongji University, Shanghai 201804, China ]
• Jintao ZE [ Department of Computer Science and Technology, Jinggangshan University, Ji’an Jiangxi 343009, China, Key laboratory of watershed ecology and geographical environment monitoring, NASG, Jinggangshan University, Ji’an Jiangxi 343009, China, Department of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China ]
• Lei PENG [ Department of Computer Science and Technology, Jinggangshan University, Ji’an Jiangxi 343009, China, Key laboratory of watershed ecology and geographical environment monitoring, NASG, Jinggangshan University, Ji’an Jiangxi 343009, China ]
• Wenlang Luo [ Department of Computer Science and Technology, Jinggangshan University, Ji’an Jiangxi 343009, China, Key laboratory of watershed ecology and geographical environment monitoring, NASG, Jinggangshan University, Ji’an Jiangxi 343009, China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605