Earticle

초록

영어

Geographical knowledge cloud service is a typical online service that provides big spatial data analysis with the function of knowledge discovery or decision-making. The composition of geographical knowledge cloud service imposes stricter requirements for better overall QoS and execution efficiency of the service chain. In this paper, we present a data volume aware ant colony optimization approach called DVA-MOACO algorithm for geographical knowledge cloud service composition. Our algorithm utilizes a multi-index service quality evaluation model, and improves the transition probability while considering the data transfer cost and other QoS constraints simultaneously when ant finding path. Our algorithm could reach the Pareto near optimal solution rapidly with better QoS performance and lower data transfer cost from numerous candidate solutions.

목차

Abstract
 1. Introduction
 2. Relate Works
 3. QoS Evaluation Model
  3.1. QoS Evaluation Model for Atom Service
  3.2. QoS Evaluation Model for Service Chain
 4. Data Volume Aware MOACO Algorithm
  4.1. The Improved Rule of State Transition
  4.2 The Implementation of DVA-MOACO Algorithm
 5. Algorithm Evaluation
 6. Conclusions
 Acknowledgement
 References

키워드

저자

• Xiaozhu Wu [ College of Computer Science and Mathematics, Fuzhou University, Fuzhou 350012, China, Key Lab of Spatial Data Mining & Information Sharing, MOE, Fuzhou University, Fuzhou 350002, China ]
• Chongcheng Chen [ Key Lab of Spatial Data Mining & Information Sharing, MOE, Fuzhou University, Fuzhou 350002, China, ]
• Hongyu Huang [ Key Lab of Spatial Data Mining & Information Sharing, MOE, Fuzhou University, Fuzhou 350002, China, ]

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