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Towards intelligent edge computing through reinforcement learning based offloading in public edge as a service.

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Public Edge as a Service (PEaaS) optimizes Internet of Things (IoT) offloading by using Proximal Policy Optimization (PPO) scheduling. This approach efficiently manages resources, reducing latency and cost for mobile IoT big data processing.

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Artificial Intelligence

Background:

  • Internet of Things (IoT) deployments face challenges in latency, cost, and resource utilization.
  • Traditional offloading strategies often neglect intermediate layers and device mobility, leading to inefficiencies.
  • A need exists for advanced frameworks to optimize resource allocation in dynamic IoT environments.

Purpose of the Study:

  • To propose and evaluate Public Edge as a Service (PEaaS) as an intermediate tier for IoT offloading.
  • To develop a simulation framework, RegionalEdgeSimPy, for modeling and assessing the PEaaS architecture.
  • To optimize task offloading decisions considering latency, cost, congestion, and energy.

Main Methods:

  • Developed RegionalEdgeSimPy, a Python simulator for the PEaaS framework.
  • Implemented a Proximal Policy Optimization (PPO) scheduler incorporating mobility and multiple input parameters.
  • Utilized action masking and a multi-objective reward function for intelligent offloading decisions.
  • Conducted simulations with 10 to 3000 devices in a smart city environment.

Main Results:

  • PPO scheduling prioritizes local Edge processing until over-utilization, then directs tasks to PEaaS.
  • PEaaS effectively handles offloaded workloads, with Cloud resources used minimally.
  • Average utilization: Edge (75.8%), PEaaS (52.9%), Cloud (<1.2%).

Conclusions:

  • The PPO-based PEaaS framework significantly reduces delay, cost, and task failures in IoT deployments.
  • The proposed system demonstrates improved scalability for handling mobility in IoT big data processing.
  • PEaaS offers an efficient intermediate layer for distributed IoT resource management.