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Integer particle swarm optimization based task scheduling for device-edge-cloud cooperative computing to improve SLA

Bo Wang¹, Junqiang Cheng², Jie Cao¹

¹Zhengzhou University of Light Industry, Zhengzhou, China.

Peerj. Computer Science

|May 2, 2022

Summary

This summary is machine-generated.

This study introduces an optimized task scheduling method for Device-Edge-Cloud Cooperative Computing (DE3C) environments. The new approach significantly enhances Service-Level Agreement (SLA) satisfaction and resource efficiency.

Keywords:

Edge cloud Particle swarm optimization Task offloading Task scheduling

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Area of Science:

Computer Science
Distributed Computing
Artificial Intelligence

Background:

Device-Edge-Cloud Cooperative Computing (DE3C) environments require efficient task scheduling for optimal resource utilization and user satisfaction.
Existing task scheduling methods in DE3C often address only subsets of sub-problems, leading to suboptimal performance.
Key sub-problems include offloading decisions, task assignment, and task ordering.

Purpose of the Study:

To optimize Service-Level Agreement (SLA) satisfaction and resource efficiency in DE3C environments.
To address the limitations of existing methods by jointly optimizing multiple task scheduling sub-problems.
To develop a novel task scheduling approach for complex DE3C systems.

Main Methods:

Formulated the task scheduling problem as a binary nonlinear programming problem.
Proposed an Integer Particle Swarm Optimization (IPSO) method for efficient problem-solving.
Integrated an earliest deadline first (EDF) scheme within IPSO to manage task ordering.

Main Results:

The proposed IPSO method jointly optimizes offloading decisions and task assignment.
The integrated EDF scheme effectively addresses the task ordering problem for each computing core.
Experimental results demonstrate significant performance improvements compared to existing methods.

Conclusions:

The proposed integrated IPSO and EDF approach provides a superior solution for task scheduling in DE3C.
This method achieves substantial gains in both SLA satisfaction and resource efficiency.
The findings offer a promising direction for enhancing DE3C system performance.