Social network analysis is used to extract features of human communities and proves to be very instrumental in a variety of scientific domains. The dataset of a social network is often so that a large cloud data analysis service, in which the computation is performed on a parallel platform in the cloud, becomes a good choice for researchers not experienced in parallel programming. In the cloud, a primary challenge to efficient computation and data analysis is the communication skew (ie, load imbalance) among computers caused by humanity's group behavior (eg, the bandwagon effect). Traditional load balancing techniques require eithersignificant effort to re-balance loads on the node, or can not cope well with stragglers. A general straggler execution-aware approach, SAE, to support the analysis Service in the cloud. It offers a novel computational method that decomposition factors straggling Feature extraction processes into more fine-grained sub-processes, which are then distributed over clusters of computers for parallel execution. Experimental results show that SAE can speed up the analysis by up to 1.77 times compared with state-of-the-art solutions.