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WG-Storm: a resource-aware scheduler for distributed stream processing engines.

Rizwan Ali1, Asif Muhammad1, Muhammad Aleem2

  • 1Department of Software Engineering, National University of Computer and Emerging Sciences, Islamabad, Islamabad, Punjab, Pakistan.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces WG-Storm, a novel scheduler for stream processing engines (SPEs). WG-Storm enhances resource utilization and throughput for big data applications by considering topology and resource awareness.

Keywords:
Distributed computingHeterogeneous clusterOptimized schedulingResource awareStream processing engine

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Big Data Analytics

Background:

  • Stream processing engines (SPEs) face challenges in scheduling big data applications due to resource utilization, dynamic configurations, and heterogeneous environments.
  • Increasing data volumes complicate the prediction of resource and application requirements, impacting overall system throughput.
  • Existing SPEs often overlook network topology, leading to minimized throughput and increased latency.

Purpose of the Study:

  • To propose a topology-aware and resource-aware scheduler, WG-Storm, for enhancing resource usage and throughput in stream processing.
  • To address the inefficiencies in task assignment that limit maximum throughput in big data applications.
  • To improve the performance of Apache Storm in heterogeneous cluster environments.

Main Methods:

  • Developed WG-Storm, a scheduler based on a directed acyclic graph (DAG), integrating topology and resource awareness.
  • Implemented WG-Storm on the Apache Storm platform.
  • Evaluated WG-Storm using two linear topologies and compared its performance against five state-of-the-art schedulers.

Main Results:

  • WG-Storm demonstrated up to a 30% increase in throughput compared to existing schedulers.
  • The proposed scheduler achieved higher throughput while utilizing fewer computing resources.
  • Experimental results confirmed improved resource usage and overall system efficiency in a heterogeneous cluster.

Conclusions:

  • WG-Storm effectively enhances resource utilization and overall throughput in stream processing engines.
  • The topology-aware and resource-aware approach significantly improves task assignment efficiency.
  • WG-Storm offers a promising solution for optimizing big data application scheduling in complex environments.