Resource Allocation Optimization Using Artificial Intelligence Methods in Various Computing Paradigms: a Review

EasyChair Preprint 7645, version 1

Abstract

With the advent of smart devices, the demand for various computational paradigms such as the Internet of Things, fog, and cloud computing has increased. However, effective resource allocation remains challenging in these paradigms. This paper presents a comprehensive literature review on the application of artificial intelligence (AI) methods such as deep learning (DL) and machine learning (ML) for resource allocation optimization in computational paradigms. To the best of our knowledge, there are no existing reviews on AI-based resource allocation approaches in different computational paradigms. The reviewed ML-based approaches are categorized as supervised and reinforcement learning (RL). Moreover, DL-based approaches and their combination with RL are surveyed. The review ends with a discussion on open research directions and a conclusion.

Keyphrases: Cloud Computing, Edge Computing, Internet of Things, Reinforcement Learning, deep learning, resource allocation

@booklet{EasyChair:7645,
  author    = {Javad Hassannataj Joloudari and Roohallah Alizadehsani and Issa Nodehi and Sanaz Mojrian and Fatemeh Fazl and Sahar Khanjani Shirkharkolaie and H M Dipu Kabir and Ru-San Tan and U Rajendra Acharya},
  title     = {Resource Allocation Optimization Using Artificial Intelligence Methods in Various Computing Paradigms: a Review},
  howpublished = {EasyChair Preprint 7645},
  year      = {EasyChair, 2022}}