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Adaptive online scheduling in storm
Abstract
Today we are witnessing a dramatic shift toward a data-driven economy, where the ability to efficiently and timely analyze huge amounts of data marks the difference between industrial success stories and catastrophic failures. In this scenario Storm, an open source distributed realtime computation system, represents a disruptive technology that is quickly gaining the favor of big players like Twitter and Groupon. A Storm application is modeled as a topology, i.e. a graph where nodes are operators and edges represent data flows among such operators. A key aspect in tuning Storm performance lies in the strategy used to deploy a topology, i.e. how Storm schedules the execution of each topology component on the available computing infrastructure.
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“Scalability solutions incorporate distributed processing architectures with efficient data partitioning, query optimization, and fault tolerance mechanisms adapting to changing stream conditions [1048, 35].”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [35]
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Cited by
Sorted by citations
A Survey of Context Engineering for Large Language Models
Mei, L., Yao, J., Ge, Y., Wang, Y., Bi, B., Cai, Y., Liu, J., Li, M., Li, Z.-Z., Zhang, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2507.13334 · Cited by 281
Metadata
- Authors
- Aniello, L., Baldoni, R., & Querzoni, L.
- Affiliations
- Sapienza University of Rome (IT)
- Venue
- Proceedings of the 7th ACM international conference on Distributed event-based systems
- Publisher
- ACM
- Published
- 2013-06-29
- Pages
- 207-218
- OpenAlex
- W1968999661
- Cited by
- 245
- References
- 0
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 9 · 2023: 7 · 2022: 10 · 2021: 21 · 2020: 20 · 2019: 30 · 2018: 31 · 2017: 37 · 2016: 35 · 2015: 27 · 2014: 8 · 2013: 3
- FWCI
- 19.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Distributed and Event-Based Systems (DEBS) (publishedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed and Parallel Computing Systems
- Keywords
- storms, adaptive scheduling, online scheduling, operator placement, big data processing, performance tuning
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