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Predictive topology refinements in distributed stream processing system.

Muhammad Hanif1, Choonhwa Lee1, Sumi Helal2

  • 1Division of Computer Science and Engineering, Hanyang University, Seoul, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a workload prediction mechanism for cloud-based streaming processing-as-a-service (SPaaS) systems. The novel approach enhances system performance and ensures quality of service (QoS) by adapting to workload variations.

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Area of Science:

  • Computer Science
  • Data Science
  • Cloud Computing

Background:

  • Cloud computing has led to a surge in data volume, driving interest in big data analytics and streaming applications.
  • Maintaining Quality of Service (QoS) to meet Service Level Agreements (SLAs) cost-effectively is challenging due to fluctuating workloads.
  • Workload prediction is crucial for optimizing cloud-based streaming systems.

Purpose of the Study:

  • To present a novel topology-refining scheme for streaming systems that incorporates workload prediction.
  • To enhance the performance and robustness of streaming systems against dynamic workloads.
  • To ensure cost-effective achievement of QoS goals within SLA constraints.

Main Methods:

  • Developed a workload prediction model combining Support Vector Regression (SVR), autoregressive, and moving average models with a feedback mechanism.
  • Implemented a topology-refining scheme that dynamically adapts to predicted workloads.
  • Utilized Apache Flink as a testbed for evaluating the proposed system.

Main Results:

  • The proposed prediction scheme effectively handles both synthetic and real-world workload traces.
  • The topology-refining scheme demonstrated robustness in adapting to incoming workloads.
  • The system successfully met QoS goals and SLA constraints.

Conclusions:

  • Workload prediction is a viable strategy for improving the performance of cloud-based streaming systems.
  • The novel topology-refining scheme offers a robust solution for dynamic workload management.
  • The approach ensures cost-effective QoS adherence in competitive cloud environments.