Abstract:
Complex big data processing applications are growing. They are workflows of decoupled analytical processes now. Stream workflow applications integrate multiple streaming big data applications for decision-making.
Each analytical component of these applications continuously processes data streams whose velocity depends on network bandwidth and parent analytical component processing rate. Thus, cloud applications must be scheduled to meet end users’ data processing and decision-making needs.
We propose two multicloud scheduling and resource allocation methods for stream workflow application execution on multicloud environments that meet workflow application and user performance requirements and reduce execution cost. The genetic algorithm performed well in all experiments.
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