Hybrid Rendering with Scheduling under Uncertainty

As scientific data of increasing size is generated by today's simulations and measurements, utilizing dedicated server resources to process the visualization pipeline becomes necessary. In a purely server-based approach, requirements on the client-side are minimal as the client only displays results received from the server. However, the client may have a considerable amount of hardware available, which is left idle. Further, the visualization is put at the whim of possibly unreliable server and network conditions. Server load, bandwidth and latency may substantially affect the response time on the client. In this paper, we describe a hybrid method, where visualization workload is assigned to server and client. A capable client can produce images independently. The goal is to determine a workload schedule that enables a synergy between the two sides to provide rendering results to the user as fast as possible. The schedule is determined based on processing and transfer timings obtained at runtime. Our probabilistic scheduler adapts to changing conditions by shifting workload between server and client, and accounts for the performance variability in the dynamic system.