On Network CoProcessors for Scalable, Predictable Media Services
IEEE Transactions on Parallel and Distributed Systems
I-RMI: performance isolation in information flow applications
Proceedings of the ACM/IFIP/USENIX 2005 International Conference on Middleware
Isolation points: Creating performance-robust enterprise systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
I-RMI: performance isolation in information flow applications
Middleware'05 Proceedings of the ACM/IFIP/USENIX 6th international conference on Middleware
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Abstract: 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 here 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. The intent of dynamically varied mirroring is to improve server scalability, both with respect to its ability to stream data events to a large number of clients and to deal with large and highly variable request volumes from clients that require other services, such as new initial states computed from incoming data events. 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 trade-offs in the communication vs. computation loads imposed on the server node receiving events and on its mirror nodes. This capability is used for adaptive event coalescing in response to increases or decreases in client request loads.