FAST: near real-time data analytics for the cloud

Authors:
Yu Hua;Hong Jiang;Dan Feng;Lei Tian
Affiliations:
Huazhong Univ. of Sci. and Tech., Wuhan, China;Univ. of Nebraska-Lincoln, Lincoln, NE;HUST, Wuhan, China;UNL, Lincoln, NE
Venue:
Proceedings of the 4th annual Symposium on Cloud Computing
Year:
2013

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Abstract

Existing cloud storage systems have largely failed to offer an adequate capability for real-time data analytics. Since the true value of data heavily depends on how efficiently data analytics can be carried out on the data in (near-) real-time, large fractions of data unfortunately end up with their values being lost or significantly reduced due to the staleness of data. To address this problem, we propose a near real-time and cost-effective data analytics methodology, called FAST, in the cloud. FAST explores and exploits the semantic correlation property within and among datasets via correlation-aware hashing and manageable flat-structured addressing to significantly reduce the processing latency, while incurring acceptably small loss of accuracy. FAST is demonstrated to be a useful tool in supporting near real-time processing of real-world cloud applications.