Fault-tolerance in the Borealis distributed stream processing system
By M. Balazinska, Hari Balakrishnan, Samuel Madden, M. Stonebraker. ACM SIGMOD Conference, 2005
389 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/7619622c6b3aafa8ac649a3eac0d4370a7bede2e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Ion Stoica
- Scott Shenker
- Matei A. Zaharia
- Haoyuan Li
- Timothy Hunter
- Tathagata Das
- Mosharaf Chowdhury
- Sylvia Ratnasamy
In Information Systems
- Ion Stoica
- Matei A. Zaharia
- Scott Shenker
- Haoyuan Li
- Timothy Hunter
- Tathagata Das
- Mosharaf Chowdhury
- Jimmy J. Lin
Citations in the corpus
- Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters (2012, background): “The seminal academic work on stream processing was in streaming databases such as Aurora, Borealis, Telegraph, and STREAM [4, 2, 3, 1].”
- StreamCloud: An Elastic and Scalable Data Streaming System (2012, neutral): “In some settings, elasticity is closely related to fault-tolerance [20] where the former adds resources as the sys-tem saturates while fault-tolerance requires new computing resources as some of them fail.”
- Discretized streams: fault-tolerant streaming computation at scale (2013, methodology): “Some systems, like Borealis [5], synchronize nodes to avoid this problem, while others, like Storm, ignore it.”
- Delay aware querying with Seaweed (2006, neutral): “Many large-scale systems route tuples through longstanding pre-installed queries [2, 3, 10, 13, 23, 30].”
- Dryad: distributed data-parallel programs from sequential building blocks (2007, neutral): “[25, 10, 34]) and Dryad, such as some operators and the topologies of”