A topology-based scaling mechanism for Apache Storm

Ce Kuen Shieh, Sheng Wei Huang, Li Da Sun, Ming Fong Tsai, Naveen Chilamkurti

研究成果: Article同行評審

12 引文 斯高帕斯(Scopus)


As more and more well-known companies, such as Twitter, Yahoo, and Alibaba, start to focus on real-time big data applications, how to build a platform for processing real-time data becomes an important issue. Among all the real-time processing systems, Apache Storm is the most well-known and representative open-source, distributed, real-time computation system. In Storm, the computation is implemented by a topology such as a graph where nodes are operators and edges represent the data flows between operators. In big data processing and analysis systems, scalability is an important issue. Storm provides rebalance mechanism for its scalability property, which can adjust the parallelism of a running topology. However, there are some drawbacks in rebalance command, such as resource usage restriction and topology execution suspension. In this paper, we propose a topology-based scaling mechanism for Apache Storm. When a topology is overloaded, it scales by adjusting the number of the cloned topologies or replaced by another new topology with more tasks. When scaling by topology-based mechanism, it eliminates resource usage restriction and execution suspension in the topology, and the procedure is automatically launched. The experimental results show that our topology-based scaling mechanism can improve the scaling performance of Storm.

期刊International Journal of Network Management
出版狀態Published - 2017 5月 1

All Science Journal Classification (ASJC) codes

  • 電腦科學應用
  • 電腦網路與通信


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