Efficient Load Balancing Algorithm for the Arrangement-Star Network

The Arrangement-Star is a well-known network in the literature and it is one of the promising interconnection networks in the area of super computing, it is expected to be one of the attractive alternatives in the future for High Speed Parallel Computers. The Arrangement-Star network has many attractive topological properties such as small diameter, low degree, good connectivity, low broadcasting cost and flexibility in choosing the desired network size. Although, some of the research work has been done on Arrangement-Star network, it still needs more investigation and research efforts to explore these attractive topologies and utilize it to solve some real life applications. In this paper we attempt to fill this gap by proposing an efficient algorithm for load balancing among different processors of the Arrangement-Star network. The proposed algorithm is named as Arrangement Star Clustered Dimension Exchange Method ASCDEM presented and implemented on the Arrangement-Star network. The algorithm is based on the Clustered Dimension Exchange Method (CDEM). The ASCDEM algorithm is shown to be efficient in redistributing the load balancing among all different processors of the network as evenly as possible. A complete detail of this algorithm in addition to examples and discussions to explore the benefits of applying this distributed algorithm is presented in this paper. Furthermore an analytical study on the algorithm is presented and discussed to explore the attractive performance of the proposed algorithm.

@article{Awwad2016,
  title     = {Efficient Load Balancing Algorithm for the Arrangement-Star Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {8},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Ahmad M. Awwad and Jehad A. Al-Sadi},
  doi       = {10.14569/IJACSA.2016.070828},
  url       = {https://doi.org/10.14569/IJACSA.2016.070828}
}