TY - GEN
T1 - Domino
T2 - 23rd ACM Symposium on High-Performance Parallel and Distributed Computing, HPDC 2014
AU - Dai, Dong
AU - Chen, Yong
AU - Kimpe, Dries
AU - Ross, Rob
AU - Zhou, Xuehai
PY - 2014
Y1 - 2014
N2 - In recent years, more and more applications in cloud have needed to process large-scale on-line data sets that evolve over time as entries are added or modified. Several programming frameworks, such as Percolator and Oolong, are proposed for such incremental data processing and can achieve efficient updates with an event-driven abstraction. However, these frameworks are inherently asynchronous, leaving the heavy burden of managing synchronization to applications developers. Such a limitation significantly restricts their usability. In this paper, we introduce a trigger-based incremental computing framework, called Domino, with a flexible synchronization mechanism and runtime optimizations to coordinate parallel triggers efficiently. With this new framework, both synchronous and asynchronous applications can be seamlessly developed. Use cases and current evaluation results confirm that the new Domino programming model delivers sufficient performance and is easy to use in large-scale distributed computing.
AB - In recent years, more and more applications in cloud have needed to process large-scale on-line data sets that evolve over time as entries are added or modified. Several programming frameworks, such as Percolator and Oolong, are proposed for such incremental data processing and can achieve efficient updates with an event-driven abstraction. However, these frameworks are inherently asynchronous, leaving the heavy burden of managing synchronization to applications developers. Such a limitation significantly restricts their usability. In this paper, we introduce a trigger-based incremental computing framework, called Domino, with a flexible synchronization mechanism and runtime optimizations to coordinate parallel triggers efficiently. With this new framework, both synchronous and asynchronous applications can be seamlessly developed. Use cases and current evaluation results confirm that the new Domino programming model delivers sufficient performance and is easy to use in large-scale distributed computing.
KW - Cloud computing
KW - Incremental computing
KW - Mapreduce
UR - http://www.scopus.com/inward/record.url?scp=84904410931&partnerID=8YFLogxK
U2 - 10.1145/2600212.2600705
DO - 10.1145/2600212.2600705
M3 - Conference contribution
AN - SCOPUS:84904410931
SN - 9781450327480
T3 - HPDC 2014 - Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing
SP - 291
EP - 294
BT - HPDC 2014 - Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing
PB - Association for Computing Machinery
Y2 - 23 June 2014 through 27 June 2014
ER -