Auto-scaling techniques for elastic data stream processing
作者： Thomas Heinze , Valerio Pappalardo , Zbigniew Jerzak , Christof Fetzer
DOI: 10.1109/ICDEW.2014.6818344
关键词: Computer science 、 Scaling 、 Big data 、 Point (geometry) 、 Work (physics) 、 Scalability 、 Workload 、 Constant (mathematics) 、 Real-time computing 、 Scale (ratio)
摘要: An elastic data stream processing system is able to handle changes in workload by dynamically scaling out and in. This allows for handling of unexpected load spikes without the need constant overprovisioning. One major challenges an find right point time scale or out. Finding such a difficult as it depends on constantly changing characteristics. In this paper we investigate application different auto-scaling techniques solving problem. Specifically: (1) formulate basic requirements technique used (2) use formulated select best auto (3) perform evaluation selected using real world data. Our experiments show that existing systems are performing worse than strategies our work.
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