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Deep Reinforcement Learning Based Resource Allocation Strategy in Cloud-Edge Computing System.

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  • 1Department of Information Security, Naval University of Engineering, Wuhan, China.

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Summary
This summary is machine-generated.

Collaborative cloud-edge computing enhances mobile app performance by optimizing resource allocation. New algorithms (CERAI, CERAU) significantly reduce operating costs in private and public cloud environments.

Keywords:
Markov decision processcollaborative cloud-edge computingedge computingreinforcement learningresource allocation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Mobile applications strain edge nodes with limited computing power, degrading user experience.
  • Collaborative cloud-edge computing allows edge nodes to leverage cloud resources.
  • Cloud services are categorized into private and public clouds, each with unique resource management challenges.

Purpose of the Study:

  • To develop efficient resource allocation algorithms for collaborative cloud-edge computing environments.
  • To address the sequential decision-making problem of resource allocation in private and public clouds.
  • To reduce the long-term operating costs of cloud-edge systems.

Main Methods:

  • Modeling resource allocation as a Markov decision process and parameterized action Markov decision process.
  • Proposing cost-efficient resource allocation algorithms CERAI (private cloud) and CERAU (public cloud).
  • Utilizing deep reinforcement learning algorithms: Deep Deterministic Policy Gradient (DDPG) and P-DQN.

Main Results:

  • CERAI and CERAU demonstrated effectiveness in reducing long-term operating costs.
  • Algorithms performed well across various demanding settings using synthetic and real Google datasets.
  • Comparative analysis showed superior performance against three typical resource allocation algorithms.

Conclusions:

  • CERAI and CERAU provide effective solutions for resource allocation in collaborative cloud-edge computing.
  • The proposed algorithms offer valuable insights for enterprises designing cloud-edge resource strategies.
  • Optimized resource allocation is crucial for enhancing user experience and operational efficiency.