Towards Emulation of Large Scale Complex Network Workloads on Graph Databases with XGDBench

Graph database systems are getting a lot of attention in recent times from the big data management community due to their efficiency in graph data storage andpowerful graph query specification abilities. In this paper we present a methodology for modeling workload spikes in a graph database system using a scalable benchmarking framework called XGDBench. We describe how two main types of workload spikes called data spikes and volume spikes can be implemented in the context of graph databases by considering realworld workload traces and empirical evidence.We implemented these features on XGDBench which we developed using X10. We validated these features by running workloads on Titan which is a popular open source distributed graph database server.We observed the ability of XGDBench in generating realistic workload spikes on Titan. The distributed architecture of XGDBench promotes implementation of such techniques efficiently through utilization of computing power offered by distributed memory compute clusters.