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A Case for Abstract Cost Models for Distributed Execution of Analytics Operators.

Rundong Li1, Ningfang Mi2, Mirek Riedewald1

  • 1CCIS, Northeastern University, Boston, USA.

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Summary
This summary is machine-generated.

This study introduces a simplified makespan model for distributed data analytics. The abstract model optimizes job partitioning on clusters, significantly reducing computational complexity and improving performance.

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Data Analytics

Background:

  • Data analytics workloads are increasingly deployed on distributed architectures, particularly clusters of commodity machines.
  • Accurate makespan models are crucial for optimizing job partitioning and minimizing execution time in these environments.

Purpose of the Study:

  • To develop a simplified yet accurate makespan model for data analytics workloads on distributed systems.
  • To explore the use of piecewise linear functions to represent fundamental algorithm properties abstractly.
  • To integrate this model into the makespan optimization process for improved efficiency.

Main Methods:

  • Investigated piecewise linear functions based on input, output, and computational complexity.
  • Developed an abstract cost model that avoids detailed system and implementation specifics.
  • Integrated the simplified model directly into the makespan optimization algorithm.

Main Results:

  • Demonstrated good prediction quality for workload running times.
  • Achieved significant reductions in computational complexity for makespan optimization (orders of magnitude).
  • Successfully optimized makespan across diverse cluster architectures.

Conclusions:

  • The proposed abstract makespan model offers a computationally efficient and accurate approach for distributed data analytics.
  • Simplified functional structures can be effectively leveraged for optimizing complex distributed workloads.
  • The method shows promise for practical application in cluster computing environments.