Adaptable mirroring in cluster servers

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.