An operational information system (OIS) supports a real-time view of an organization's information critical to its logistical business operations. A central component of an OIS is an engine that integrates data events captured from distributed, remote sources in order to derive meaningful real-time views of current operations. This event derivation engine (EDE) continuously updates these views and also publishes them to a potentially large number of remote subscribers. The paper first describes a sample OIS and EDE in the context of an airline's operations. It then defines the performance and availability requirements to be met by this system, specifically focusing on the EDE component. One particular requirement for the EDE is that subscribers to its output events should not experience downtime due to EDE failures, crashes or increased processing loads. Toward this end, we develop and evaluate a practical technique for masking failures and for hiding the costs of recovery from EDE subscribers. This technique utilizes redundant EDEs that coordinate view replicas with a relaxed synchronous fault tolerance protocol. A combination of pre- and post-buffering of replicas is used to attain a solution that offers low response times (i.e., 'zero' downtime) while also preventing system failures in the presence of deterministic faults like 'ill-formed' messages. Parallelism realized via a cluster machine and application-specific techniques for reducing synchronization across replicas are used to scale a 'zero' downtime EDE to support the large number of subscribers it must service.

This paper presents a software architecture for continuously mirroring streaming data received by one node of a cluster-based server to other cluster nodes. The intent is to distribute the load on the server generated by the data's processing and distribution to many clients. This is particularly important when the server not only processes streaming data, but also performs additional processing tasks that heavily depend on current application state. One such task is the preparation of suitable initialization state for thin clients, so that such clients can understand future data events being streamed to them. In particular, when large numbers of thin clients must be initialized at the same time, initialization must be performed without jeopardizing the quality of service offered to regular clients continuing to receive data streams. The mirroring framework presented and evaluated has several novel aspects. First, by performing mirroring at the middleware level, application semantics may be used to reduce mirroring traffic, including filtering events based on their content, by coalescing certain events, or by simply varying mirroring rates according to current application needs concerning the consistencies of mirrored vs. original data. Second, we present an adaptive algorithm that varies mirror consistency and thereby, mirroring overheads in response to changes in clients' request behavior. Third, our framework not only mirrors events, but it can also mirror the new states computed from incoming events, thus enabling dynamic tradeoffs in the communication vs. computation loads imposed on the server node receiving events and on its mirror nodes.