MyBSP: An Iterative Processing Framework Based on the Cloud Platform for Graph Data

Massive cloud-based data-intensive applications (e.g., iterative MapReduce-based) could involve graph data processing. How to effectively analyze and process large-scale graph data is an unsolved challenging problem. We present a parallel computation framework, named MyBSP, which is inspired by Google's Pregel system. MyBSP supports and implements the Bulk Synchronous Parallel (BSP) programming model, and introduces a module of parallel execution unit to achieve iterative processing, which avoids the restart cost of computation jobs, and therefore reduces the I/O overhead (e.g., network communication and disk access). Furthermore, we implement the MyBSP-based PageRank algorithm. Some experiments are conducted to evaluate and compare the performance and scalability of our MyBSP prototype system with MapReduce model. The experimental results show that the speedup in MyBSP compared to MapReduce is about 3.5X for the small-size graph dataset. Meanwhile, the performance improvement of MyBSP also outperforms MapReduce a factor of 2.1 when processing the large-scale dataset.