Abstract: With the development of computing technology and the advent of the era of big data, the distributed computing has became a research hotspot. Iterative computation of big graph becomes the focus of the research. Reducing the communication data quantity between subgraph after effective partitioning, it is the key to improve the computational performance, because the existing algorithms are difficult to meet the requirements on both minimizing fraction of egdes cut and load balancing at the same time. In this paper, a dynamic-balanced algorithm for graph partitioning named DyBGP is proposed, and it is used to solve the problem of balanced partition. Based on ensuring the partitioning of subgraph boundary vertices optimal, the perturbation strategy to jump out of local optimum to expand the search space is used. Finally, our algorithm is verified the feasibility in the real-world graph, respectively from the balance coefficient and the scale of edges cut compared with the traditional algorithms, such as Hash, Chunk and Metis. In the number of edges cut, it is decreased about 40%, 30%, 5% with our algorithm under specifying perturbation times. In the balance coefficient, our algorithm is more optimized than Metis. The experimental results show that the algorithm is effective.

Key words: balanced graph partitioning, heuristic strategies, load balancing, distributed computing, local optimization