Auto-Scaling Techniques for Elastic Data Stream Processing

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Provided by: Technische Universitat Dortmund
Topic: Big Data
Format: PDF
An elastic data stream processing system is able to handle changes in workload by dynamically scaling out and scaling in. This allows for handling of unexpected load spikes without the need for constant over provisioning. One of the major challenges for an elastic system is to find the right point in time to scale in or to scale out. Finding such a point is difficult as it depends on constantly changing workload and system characteristics. In this paper the authors investigate the application of different auto-scaling techniques for solving this problem.
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