@article{1525,
  author = {Zhenzhang Liu, Zhihui Hu, Lee K. Jonepun},
  title = {Research on Composite SaaS Placement Problem Based on Ant Colony Optimization Algorithm with Performance Matching Degree Strategy},
  journal = {Journal of Digital Information Management},
  year = {2014},
  volume = {12},
  number = {4},
  doi = {},
  url = {http://dline.info/fpaper/jdim/v12i4/1.pdf},
  abstract = {Cloud computing is a term referring to the latest new computing paradigm based on the Internet where a program or application runs on a connected server or servers rather than on a local computing device. Software as a Service (SaaS) is one of the most important computing services in cloud computing. The advantages of SaaS products are well recognized including lower cost of entry, minimal demands on IT and easy upgrades as well as more rigorous. However, this approach has introduced new challenges in SaaS resource management in data centres. One of the challenges is deploying softwares comprised of service and data components over servers in cloud to provide users with required services easily and quickly in SaaS cloud computing. This paper analyzes the SaaS Placement Problem (SPP) and proposes a mathematical model for Composite SaaS placement in cloud. An Ant Colony Optimization Algorithm (ACOA) with performance matching degree Strategy is applied to the problem with the aim of minimizing the total estimated execution time of all the SaaS components and reducing the algorithm computation time. The ACOA is then evaluated against the Genetic Algorithm in experiments and the results show that the ACOA applied to the SPP not only has quicker computation time but also generates better solutions than the Genetic Algorithm (GA).},
}