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Regular Paper

Online Nonstop Task Management for Storm-Based Distributed Stream Processing Engines

School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei 230026, China
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Abstract

Most distributed stream processing engines (DSPEs) do not support online task management and cannot adapt to time-varying data flows. Recently, some studies have proposed online task deployment algorithms to solve this problem. However, these approaches do not guarantee the Quality of Service (QoS) when the task deployment changes at runtime, because the task migrations caused by the change of task deployments will impose an exorbitant cost. We study one of the most popular DSPEs, Apache Storm, and find out that when a task needs to be migrated, Storm has to stop the resource (implemented as a process of Worker in Storm) where the task is deployed. This will lead to the stop and restart of all tasks in the resource, resulting in the poor performance of task migrations. Aiming to solve this problem, in this paper, we propose N-Storm (Nonstop Storm), which is a task-resource decoupling DSPE. N-Storm allows tasks allocated to resources to be changed at runtime, which is implemented by a thread-level scheme for task migrations. Particularly, we add a local shared key/value store on each node to make resources aware of the changes in the allocation plan. Thus, each resource can manage its tasks at runtime. Based on N-Storm, we further propose Online Task Deployment (OTD). Differing from traditional task deployment algorithms that deploy all tasks at once without considering the cost of task migrations caused by a task re-deployment, OTD can gradually adjust the current task deployment to an optimized one based on the communication cost and the runtime states of resources. We demonstrate that OTD can adapt to different kinds of applications including computation- and communication-intensive applications. The experimental results on a real DSPE cluster show that N-Storm can avoid the system stop and save up to 87% of the performance degradation time, compared with Apache Storm and other state-of-the-art approaches. In addition, OTD can increase the average CPU usage by 51% for computation-intensive applications and reduce network communication costs by 88% for communication-intensive applications.

Keywords

distributed stream processing engine (DSPE)Apache Stormonline task migrationonline task deployment

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Journal of Computer Science and Technology
Volume 39 Issue 1,
January 2024
Pages 116-138
DOI: 10.1007/s11390-021-1629-9
Cite this article:
Zhang Z, Jin P-Q, Xie X-K, et al. Online Nonstop Task Management for Storm-Based Distributed Stream Processing Engines. Journal of Computer Science and Technology, 2024, 39(1): 116-138. https://doi.org/10.1007/s11390-021-1629-9

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Received: 30 May 2021
Accepted: 06 January 2022
Published: 25 January 2024
© Institute of Computing Technology, Chinese Academy of Sciences 2024
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