Design and Qualitative/Quantitative Analysis of Multi-Agent Spatial Simulation Library

Abstract

Integrating sensor networks in cloud computing gives new opportunities of using as many cloud-computing nodes as necessary to analyze real-time sensor data on the fly. However, most cloud services for parallelization such as OpenMP, MPI, and MapReduce are not always suitable for on-the-fly sensor-data analyses that are implemented as model-based, entity-based, and multi-agent simulations. To address this semantic gap between analyzing algorithms and their actual implementations, we have designed and implemented MASS: a library for multi-agent spatial simulation that composes of a user application of distributed array elements and multi-agents, each representing an individual simulation place or an active entity. All computation is enclosed in each of elements and agents that are automatically distributed over different computing nodes. Their communication is then scheduled as periodic data exchanges among those entities using their logical indices. This thesis presents the design, implementation and evaluation of the MASS library.