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Performance and Availability Assessment for the Configuration of Distributed Workflow Management Systems


Weissenfels,  Jeanine
Max Planck Society;

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Gillmann, M., Weissenfels, J., Weikum, G., & Kraiss, A. (2000). Performance and Availability Assessment for the Configuration of Distributed Workflow Management Systems. In C. Zaniolo, P. C. Lockemann, M. H. Scholl, & T. Grust (Eds.), Proceedings of the 7th International Conference on Extending Database Technology (EDBT 2000) (pp. 183-201). Heidelberg, GER: Springer.

Workflow management systems (WFMSs) that are geared for the orchestration of enterprise-wide or even “virtual-enterprise”-style business processes across multiple organizations are complex distributed systems. They consist of multiple workflow engines, application servers, and ORB-style communication servers. Thus, deriving a suitable configuration of an entire distributed WFMS for a given application workload is a difficult task. This paper presents a mathematically based method for configuring a distributed WFMS such that the application's demands regarding performance and availability can be met while aiming to minimize the total system costs. The major degree of freedom that the configuration method considers is the replication of the underlying software components, workflow engines and application servers of different types as well as the communication server, on multiple computers for load partitioning and enhanced availability. The mathematical core of the method consists of Markov-chain models, derived from the application's workflow specifications, that allow assessing the overall system's performance, availability, and also its performability in the degraded mode when some server replicas are offline, for given degrees of replication. By iterating over the space of feasible system configurations and assessing the quality of candidate configurations, the developed method determines a configuration with near-minimum costs.