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Collaborative optimization of computational offloading and resource allocation based on Stackelberg game.

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This study introduces a Stackelberg game for optimizing cloud-edge-end collaboration in the Internet of Things. The method enhances system utility by balancing delay, energy, and revenue for efficient task processing.

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Area of Science:

  • Computer Science
  • Distributed Computing
  • Game Theory

Background:

  • The Internet of Things (IoT) and mobile edge computing (MEC) necessitate efficient data processing and low-latency responses.
  • Existing cloud-edge-end architectures face challenges in task scheduling, resource heterogeneity, and dynamic optimization for energy efficiency.

Purpose of the Study:

  • To address the limitations in dynamic collaborative optimization for computational offloading and resource allocation.
  • To maximize system utility by integrating delay, energy consumption, and revenue in a multi-tier computing environment.

Main Methods:

  • Developed a three-tier Stackelberg game model involving cloud, edge, and mobile terminals.
  • Constructed an overall utility model considering delay, energy, and revenue for multi-cloud, multi-edge, and multi-user scenarios.
  • Proposed a backward induction resource pricing, allocation, and computation offload optimization algorithm (BI-PRO).

Main Results:

  • Proved the existence and uniqueness of the Stackelberg equilibrium through game analysis.
  • The BI-PRO algorithm effectively optimizes resource pricing, allocation, and computation offloading.
  • Experimental results demonstrate improved system revenue and stable performance across diverse scenarios.

Conclusions:

  • The proposed Stackelberg game approach offers a robust solution for dynamic collaborative optimization in cloud-edge-end computing.
  • The method significantly enhances overall system utility and performance efficiency.
  • This research contributes to achieving low-latency and energy-efficient task processing in IoT environments.