Abstract
The multistage graph problem is a special kind of single-source single-sink shortest path problem. It is difficult even impossible to solve the large-scale multistage graphs using a single machine with sequential algorithms. There are many distributed graph computing systems that can solve this problem, but they are often designed for general large-scale graphs, which do not consider the special characteristics of multistage graphs. This paper proposes DMGA (Distributed Multistage Graph Algorithm) to solve the shortest path problem according to the structural characteristics of multistage graphs. The algorithm first allocates the graph to a set of computing nodes to store the vertices of the same stage to the same computing node. Next, DMGA calculates the shortest paths between any pair of starting and ending vertices within a partition by the classical dynamic programming algorithm. Finally, the global shortest path is calculated by subresults exchanging between computing nodes in an iterative method. Our experiments show that the proposed algorithm can effectively reduce the time to solve the shortest path of multistage graphs.
Highlights
With the continuous development of big data and information technology, graph has been widely applied in many applications, and various graph structures and algorithms have been proposed
Graph models are widely applied in many fields, and the scale of the graph increases significantly. e existing distributed graph computing systems cannot make full use of the special characteristics of the multistage graphs
This paper proposes DMGA which is used to solve the shortest path problem of large-scale multistage graphs on a distributed computing system
Summary
With the continuous development of big data and information technology, graph has been widely applied in many applications, and various graph structures and algorithms have been proposed. The scale of graph data has grown tremendously, so it is difficult even impossible to store and process such large-scale graphs by a single computer or sequential processing method [3] At this point, the distributed computing scheme became a must, and lots of dedicated graph-processing systems have been appearing [4,5,6], such as Pregel [7], PowerGraph [8], GraphX [9, 10], GraphLab [11], and PowerLyra [12]. E current distributed graph processing systems and algorithms are usually designed for general graphs, and they do not consider the special structural properties of multistage graphs, so there are some disadvantages in applying them to multistage graphs, such as high communication cost and long solution time High-quality partition can reduce the communication cost and achieve the load balance [13,14,15], the processing time can be minimized subsequently. e current distributed graph processing systems and algorithms are usually designed for general graphs, and they do not consider the special structural properties of multistage graphs, so there are some disadvantages in applying them to multistage graphs, such as high communication cost and long solution time
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