Predicting the Performance of MPI Applications over Different Grid Architectures

Nowadays, the high speed and accurate optimization algorithms are required. In most of the cases, researchers need a method to predict some criteria with acceptable accuracy to use it after in their algorithms. However, in the field of parallel computing the execution time can be considered the most important criteria. Consequently, this paper presents new execution time prediction model for message passing interface applications execute over numerous grid scenarios. The model has ability to predict the execution time of the message passing applications running over any grid configuration in term of different number of nodes and their computing powers. The experiments are evaluated over SimGrid simulator to simulate the grid configuration scenarios. The results of comparing the real and the predicted execution time show a good accuracy. The average error ratio between the real and the predicted execution time for three benchmarks are 4.36%, 5.79% and 6.81%.

Predicting the Brand Popularity from the Brand Metadata

Social networks have become one of the primary sources of big data, where a variety of posts related to brands are liked, shared, and commented, which are collectively called as brand metadata. Due to the increased boom in E/M-commerce, buyers often refer the brand metadata as a valuable source of information to make their purchasing decision. From the literature study, we found that there are not many works on predicting the popularity of the brand based on the combination of brand metadata and comment’s thoughtfulness analysis. This paper proposes a novel framework to classify the comment’s as thoughtful favored or disfavored comment’s, and later combines them with the brand metadata to forecast the popularity of the brand in near future. The performance of the proposed framework is compared with some of the recent works w.r.t. thoughtful comment’s identification accuracy, execution time, prediction accuracy and prediction time, the results obtained are found to be very encouraging.