Abstract: The rapid development of the Internet brings challenges to the data process mode in cloud service. In this regard, the Chinese Academy of Sciences proposes the SEA service and SEA-Cloud collaboration system, in which the decomposition strategy of cloud application is an important link to affect the performance of the system. However, current mainstream methods aim to deal with the simple graph in the cloud environments, which mismatch the directed weighted graph in the SEA-Cloud collaboration scenarios. So, this paper proposes a cloud application decomposition method combining Grey Wolf Optimizer(GWO) and〖JP2〗 Fiduccia-Mattheyses(FM) algorithm to solve the problem. Benefitted from the rapid convergence of GWO, the proposed algorithm takes the outcomes of GWO as the initial inputs of FM algorithm to avoid the sensitivity of FM to the initial partitions. The simulation results illustrate that the combination method outperforms the current method. The obtained partition matches the distribution of SEA resources and the rate of edge cut decreases dramatically, which means that the communication overhead is reduced.

Key words: SEA-cloud collaboration, application decomposition, graph partitioning problem, heuristic algorithm