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Delay Optimal Schemes for Internet of Things Applications in Heterogeneous Edge Cloud Computing Networks.

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Sensors (Basel, Switzerland)
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Summary

This study introduces a Joint Task Offloading and Scheduling (JTOS) framework to minimize hybrid delay for Internet of Things (IoT) applications. JTOS significantly reduces processing and communication delays in dynamic edge computing environments.

Keywords:
CLIPJTOSSDNdynamic environmentframeworktask scheduling

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Internet of Things (IoT) applications in healthcare, intelligent vehicles, and smart homes generate delay-sensitive data requiring rapid execution.
  • Existing offloading and scheduling techniques in dynamic edge computing environments often underperform for delay-sensitive IoT applications.
  • Software-Defined Networks (SDN) and fog computing offer edge computing paradigms to minimize end-to-end delays for these applications.

Purpose of the Study:

  • To develop an optimized framework for joint task offloading and scheduling to address the performance limitations of existing methods in dynamic IoT environments.
  • To minimize the hybrid delay of delay-sensitive IoT applications by formulating the problem as a combinatorial integer linear programming (CILP) model.
  • To introduce a novel Joint Task Offloading and Scheduling (JTOS) framework encompassing task offloading, sequencing, scheduling, searching, and failure management.

Main Methods:

  • Formulation of joint task offloading and scheduling problems as combinatorial integer linear programming (CILP).
  • Development and implementation of the Joint Task Offloading and Scheduling (JTOS) framework.
  • Performance evaluation through comparative analysis against existing baseline methods in dynamic environments.

Main Results:

  • The proposed JTOS framework demonstrates superior performance in minimizing hybrid delay for all applications compared to existing baseline methods.
  • JTOS achieves a 39% reduction in processing delay for IoT applications.
  • JTOS achieves a 35% reduction in communication delay for IoT applications.

Conclusions:

  • The JTOS framework effectively addresses the challenges of offloading and scheduling for delay-sensitive IoT applications in dynamic edge computing settings.
  • JTOS offers significant improvements in reducing both processing and communication delays, enhancing overall application performance.
  • The CILP formulation provides a robust approach to optimizing joint task offloading and scheduling decisions for IoT applications.