Pec: Proactive Elastic Collaborative Resource Scheduling in Data Stream Processing

In the Distributed Parallel Stream Processing Systems (DPSPS), elastic resource allocation allows applications to dynamically response to workload fluctuations. However, resource provisioning can be particularly challenging, due to the unpredictability of the workload. In addition, unlike CPU resources, bandwidth resources are often ignored in resource allocation. Moreover, resource allocation and resource placement are considered separately. In this paper, we investigate the proactive elastic resource scheduling problem for computation-intensive and communication-intensive applications, which aims at meeting the latency requirement with the minimal energy cost, and propose a dynamic collaborative strategy from the systemic perspective. Specifically, we first model a collaborative workload prediction pattern to accurately predict the upcoming workload, and construct a latency estimation model to estimate the latency of the application. Then, we design an energy-efficient resource pre-allocation method, in which the CPU frequency adjustment and the stability of resource reconfigurations are both considered. Finally, we present a communication-aware resource placement approach. Simulation results show that, compared with the reactive strategies, our strategy achieves an obviously better latency performance, and effectively avoids unnecessary resource adjustments. Meanwhile, the energy consumption is about saved by 50 percent on average, and the communication cost is maintained at a very low level of 4 percent.