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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.

Enterprise-spanning workflows require workflow management systems that can be tailored to specific application needs, as well as enhanced support for interoperability between different workflow management systems. In virtual enterprises, the interoperability problem is not limited to workflow execution, but also entails facilities like worklist management and history management to be interoperable. We present a lightweight system architecture, consisting of a small system kernel on top of which extensions like history management and worklist management are implemented as workflows themselves. The functionality of the kernel such as distributed workflow execution and interoperability interfaces is available for all extensions. We show the feasibility of our approach by presenting the implementation of history management in our workflow specification language, based on state and activity charts, on top of our lightweight kernel, coined Mentor-lite.

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