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High-volume online stream processing, also known as fast data processing, is becoming increasingly important in a number of different commercial sectors. Unlike big data processing in which data is processed asynchronously in batches, fast data processing performs synchronous data analysis that generates actionable results within a specified deadline. One of the key challenges in building a fast data processing system is in scaling with increasing volumes of data. In our proposed research, we plan to build a system to efficiently manage the available memory across the entire deployment. The system will determine which data blocks should remain in memory, where a data block should be placed, and what fault tolerance strategy the system should employ. The objective is to build a scalable processing system that can handle both current and future fast data processing demands. TO BE CONT’D
Bernard Wong
Sajjad Rizvi
Smash.bi
Computer science
Information and communications technologies
University of Waterloo
Accelerate
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