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Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments.

Roxana-Gabriela Stan1, Lidia Băjenaru1,2, Cătălin Negru1

  • 1Computer Science and Engineering Department, University Politehnica of Bucharest (UPB), 060042 Bucharest, Romania.

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Summary
This summary is machine-generated.

Heterogeneous edge-cloud computing faces challenges with task scheduling. Existing algorithms show significant performance degradation and task failures, especially with battery-powered edge devices.

Keywords:
heterogeneous computinghybrid edge–cloud environmentsperformance evaluation frameworktask scheduling

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Cloud Computing

Background:

  • Heterogeneous computing environments combine cloud and edge resources.
  • Edge devices like smartphones and Raspberry Pis introduce unique challenges such as limited battery and memory.
  • Effective task scheduling is crucial for performance in these hybrid ecosystems.

Purpose of the Study:

  • To establish methodologies for evaluating task scheduling policies in heterogeneous edge-cloud computing.
  • To formally define a scheduling model for hybrid edge-cloud ecosystems.
  • To analyze the performance of common scheduling algorithms in these environments.

Main Methods:

  • Development of a scheduling and evaluation framework for hybrid edge-cloud systems.
  • Simulation-based experiments using large workloads and realistic resource capacities.
  • Measurement of key scheduling metrics including mean waiting time, turnaround time, makespan, and throughput.
  • Evaluation of Round-Robin, Shortest Job First, Min-Min, and Max-Min scheduling schemes.

Main Results:

  • State-of-the-art independent task scheduling algorithms exhibit performance degradation in heterogeneous edge-cloud mediums.
  • Significant task failures (over 25%) occur due to resource limitations (battery, memory) on edge devices.
  • Non-optimal resource utilization is observed in comparison to cloud-only environments.

Conclusions:

  • Current task scheduling algorithms are not optimized for the complexities of heterogeneous edge-cloud environments.
  • Resource constraints on edge devices lead to substantial task execution failures.
  • Further research is needed to develop robust scheduling solutions for hybrid computing.