Resa: realtime elastic streaming analytics in the cloud

Authors:
Tian Tan;Richard T.B. Ma;Marianne Winslett;Yin Yang;Yong Yu;Zhenjie Zhang
Affiliations:
Shanghai Jiao Tong University, Shanghai, China;Advanced Digital Sciences Center, Singapore, Singapore;Advanced Digital Sciences Center, Singapore, Singapore;Advanced Digital Sciences Center, Singapore, Singapore;Shanghai Jiao Tong University, Shanghai, China;Advanced Digital Sciences Center, Singapore, Singapore
Venue:
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Year:
2013

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Abstract

We propose Resa, a novel framework for robust, elastic and realtime stream processing in the cloud. In addition to traditional functionalities of streaming and cloud systems, Resa provides (i) a novel mechanism that handles dynamic additions and removals nodes in an operator, and (ii) a node re-assignment scheme that minimizes output latency using a queuing model. We have implemented Resa on top of Twitter Storm. Experiments using real data demonstrate the effectiveness and efficiency of Resa.