{'fraction_non_alphanumeric': 0.02810304449648712, 'fraction_numerical': 0.00234192037470726, 'mean_word_length': 5.104761904761904, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
SAE: Toward Efficient Cloud Data Analysis Service for Large-Scale Social Networks

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 large that a cloud data analysis service, in which the computation is performed on a parallel platform in the could, becomes a good choice for researchers not experienced in parallel programming. In the cloud, a primary challenge to efficient data analysis is the computation and communication skew (i.e., load imbalance) among computers caused by humanity’s group behavior (e.g., bandwagon effect). Traditional load balancing techniques either require significant effort to re-balance loads on the nodes, or cannot well cope with stragglers. In this paper, we propose a general straggler-aware execution approach, SAE, to support the analysis service in the cloud. It offers a novel computational decomposition method that 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.