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The past decade has seen rapid growth of distributed stream data processingsystems. Under these systems, a stream application is realized as a DirectedAcyclic Graph (DAG) of operators, where the level of parallelism of eachoperator has a substantial impact on its overall performance. However, findingoptimal levels of parallelism remains challenging. Most existing methods areheavily coupled with the topological graph of operators, unable to efficientlytune under-provisioned jobs. They either insufficiently use previous tuningexperience by treating successively tuning independently, or explore theconfiguration space aggressively, violating the Service Level Agreements (SLA).  To address the above problems, we propose ContTune, a continuous tuningsystem for stream applications. It is equipped with a novel Big-smallalgorithm, in which the Big phase decouples the tuning from the topologicalgraph by decomposing the job tuning problem into sub-problems that can besolved concurrently. We propose a conservative Bayesian Optimization (CBO)technique in the Small phase to speed up the tuning process by utilizing theprevious observations. It leverages the state-of-the-art (SOTA) tuning methodas conservative exploration to avoid SLA violations. Experimental results showthat ContTune reduces up to 60.75% number of reconfigurations under syntheticworkloads and up to 57.5% number of reconfigurations under real workloads,compared to the SOTA method DS2.

ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems