The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
StreamCloud: An Elastic and Scalable Data Streaming System
Open accessAbstract
Many applications in several domains such as telecommunications, network security, large-scale sensor networks, require online processing of continuous data flows. They produce very high loads that requires aggregating the processing capacity of many nodes. Current Stream Processing Engines do not scale with the input load due to single-node bottlenecks. Additionally, they are based on static configurations that lead to either under or overprovisioning. In this paper, we present StreamCloud, a scalable and elastic stream processing engine for processing large data stream volumes. StreamCloud uses a novel parallelization technique that splits queries into subqueries that are allocated to independent sets of nodes in a way that minimizes the distribution overhead. Its elastic protocols exhibit low intrusiveness, enabling effective adjustment of resources to the incoming load. Elasticity is combined with dynamic load balancing to minimize the computational resources used. The paper presents the system design, implementation, and a thorough evaluation of the scalability and elasticity of the fully implemented system.
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Cites
Principles of Distributed Database Systems
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Self-citationAurora: a new model and architecture for data stream management
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The design of the borealis stream processing engine
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S4: Distributed Stream Computing Platform
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TelegraphCQ: continuous dataflow processing
Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, M. J., Hellerstein, J. M., Hong, W., Krishnamurthy, S., Madden, S. R., Reiss, F., & Shah, M. A. (2003) · Proceedings of the 2003 ACM SIGMOD international conference on Management of data · doi:10.1145/872757.872857 · Cited by 744
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Does big data enhance firm innovation competency? The mediating role of data-driven insights
Ghasemaghaei, M., & Calic, G. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2019.07.006 · Cited by 331
Dias de Assunção, M., da Silva Veith, A., & Buyya, R. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.12.001 · Cited by 304
Samza: Stateful scalable stream processing at LinkedIn
Noghabi, S. A., Paramasivam, K., Pan, Y., Ramesh, N., Bringhurst, J., Gupta, I., & Campbell, R. H. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3137765.3137770 · Cited by 276
A survey on the evolution of stream processing systems
Fragkoulis, M., Carbone, P., Kalavri, V., & Katsifodimos, A. (2024) · The VLDB Journal · doi:10.1007/s00778-023-00819-8 · Cited by 207
Metadata
- Authors
- Gulisano, V., Jimenez-Peris, R., Patino-Martinez, M., Soriente, C., & Valduriez, P.
- Venue
- IEEE Transactions on Parallel and Distributed Systems
- Published
- 2012-12
- Volume
- 23
- Issue
- 12
- Pages
- 2351-2365
- ISSN
- 1045-9219 (ISSN-L)
- OpenAlex
- W2017203676
- Cited by
- 324
- References
- 16
- FWCI
- 39.67
- Type
- journal article
- Language
- en
- Open access
- green
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- Gulisano, V., Jimenez-Peris, R., Patino-Martinez, M., Soriente, C., & Valduriez, P. (2012). StreamCloud: An Elastic and Scalable Data Streaming System. IEEE Transactions on Parallel and Distributed Systems. https://doi.org/10.1109/tpds.2012.24
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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