Scalable Workflow System Model Based on Mobile Agents

Abstract

A workflow system defines, creates and manages the execution of business workflows with workflow engines, which interpret workflow definitions, and interact with task performers. As most of non-trivial organizations have massive amount of workflows to process simultaneously, there is ever-increasing demands for better performance and scalability of workflow systems. This paper proposes a workflow system model based on mobile agents, so called Maximal Sequence model, as an alternative to conventional RPC-based and previous mobile agent-based (DartFlow) models. The proposed model segments a workflow definition into blocks, and assigning each of them to a mobile agent. We also construct three stochastic Petri net models of conventional RPC-based, DartFlow, and the Maximal Sequence model-based workflow systems to compare their performance and scalability. The stochastic Petri-net simulation results show that the proposed model outperforms the previous ones as well as comes up with better scalability when the numbers of workflow tasks and concurrent workflows are relatively large.