Building user-defined runtime adaptation routines for stream processing applications

Stream processing applications are deployed as continuous queries that run from the time of their submission until their cancellation. This deployment mode limits developers who need their applications to perform runtime adaptation, such as algorithmic adjustments, incremental job deployment, and application-specific failure recovery. Currently, developers do runtime adaptation by using external scripts and/or by inserting operators into the stream processing graph that are unrelated to the data processing logic. In this paper, we describe a component called orchestrator that allows users to write routines for automatically adapting the application to runtime conditions. Developers build an orchestrator by registering and handling events as well as specifying actuations. Events can be generated due to changes in the system state (e.g., application component failures), built-in system metrics (e.g., throughput of a connection), or custom application metrics (e.g., quality score). Once the orchestrator receives an event, users can take adaptation actions by using the orchestrator actuation APIs. We demonstrate the use of the orchestrator in IBM's System S in the context of three different applications, illustrating application adaptation to changes on the incoming data distribution, to application failures, and on-demand dynamic composition. © 2012 VLDB Endowment

Towards a flexible data stream analytics platform based on the GCM autonomous software component technology

International audienceBig data stream analytics platforms not only need to support performance-dictated elasticity benefiting for instance from Cloud environments. They should also support analytics that can evolve dynamically from the application viewpoint, given data nature can change so the necessary treatments on them. The benefit is that this can avoid to undeploy the current analytics, modify it off-line, redeploy the new version, and resume the analysis, missing data that arrived in the meantime. We also believe that such evolution should better be driven by autonomic behaviors whenever possible. We argue that a software component based technology, as the one we have developed so far, GCM/ProActive, can be a good fit to these needs. Using it, we present our solution, still under development, named GCM-streaming, which to our knowledge seems to be quite original