Abstract
In the most popular distributed stream processing frameworks (DSPFs), programs are modeled as a directed acyclic graph. Using this model, a DSPF can benefit from the parallelism capabilities of distributed clusters. Choosing a reasonable number of vertices for each operator and mapping the vertices to the appropriate processing resources significantly affect the overall system performance. Due to the simplicity of the current DSPF schedulers, these frameworks perform poorly on large-scale clusters. In this paper, we present a heterogeneity-aware scheduling algorithm that finds the proper number of the vertices of an application graph and maps them to the most suitable cluster node. We begin with a pre-processing step which allocates the vertices to the given cluster nodes using profiling data. Then, we gradually increase the topology input rate in order to scale up the application graph. Finally, using a CPU utilization model which predicts the CPU workload based on the input rate to vertices and the processing node’s CPU characteristics, we identify the bottlenecked vertices and allocate new instances derived from them to the least utilized processing resource. Our experimental results on Storm Micro-Benchmark show that (1) the prediction model estimate CPU utilization with 92% accuracy. (2) Compared to the default scheduler of Storm, our scheduler provides 7 to 44% throughput enhancement. (3) The proposed method can find the solution within 4% (worst case) of the optimal scheduler, which obtains the best scheduling scenario using an exhaustive search over problem design space.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.