Performance comparison of parallel graph coloring algorithms on BSP model using hadoop

Nowadays, Hadoop is massively used to store large data generated by various sources. These data are often represented in large scale graphs to solve real world problems. To compute those data, many Bulk Synchronous Parallel(BSP) model based graph processing systems are available on top of hadoop such as Pregel and Stanford: Graph Processing System (GPS). The problem of graph coloring is to assign color to all the vertices such that no neighbor vertices have same color. The graph coloring problem has many practical application in real world data analytics. In this paper, we have compared the heuristic graph coloring algorithm with BSP model based on hadoop such as Local Maxima First, Local Minima-Maxima First, Local Largest Degree First, Local Smallest-Largest Degree First. We experimented our algorithms on real world graph dataset on our hadoop cluster. The result shows that Local Smallest-Largest Degree First algorithm perform better than other heuristic based algorithms, in term of runtime and number of color used.