Enhancing Parallelization of Agent-based Graph Computing

Abstract

The demand for distributed data processing grows as modern applications involve increasingly large and complex datasets. Traditional distributed computing frameworks, such as Apache Spark and Hadoop MapReduce, are effective for large-scale data processing but are not always well suited for graph computation. The MASS (Multi-Agent Spatial Simulation) Java library instead provides an agent-based approach to distributed graph computation and has been proven effective for graph computing applications and graph database. However, the performance of MASS Java remains limited in some cases because graph applications often require many agent operations, which introduces significant overhead. To address these limitations, this thesis introduces several enhancements for improving agent execution performance in MASS Java and evaluates them using graph computing applications and graph database queries. The evaluation shows that the enhancements can improve MASS Java performance in both graph computing and graph database query execution. In addition, this thesis identifies a major overhead in the current MASS graph database and proposes a solution to reduce it. Overall, this thesis contributes to the optimization and evaluation of MASS Java for graph applications and provides useful guidance for future development.