# The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms

arXiv: https://arxiv.org/abs/2203.12315 · HTML: https://mlsanity.com/paper/2203.12315/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 73 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks](https://mlsanity.com/paper/1907.03289/) (2019)
- [A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning](https://mlsanity.com/paper/1703.04221/) (2017)
- [Dynamic Proximity-aware Resource Allocation in Vehicle-to-Vehicle (V2V) Communications](https://mlsanity.com/paper/1609.03717/) (2016)
- [Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks](https://mlsanity.com/paper/1802.04491/) (2018)
- [Reinforcement Learning for Resource Provisioning in Vehicular Cloud](https://mlsanity.com/paper/1805.11000/) (2018)
- [Divide and Conquer: Partitioning OSPF networks with SDN](https://mlsanity.com/paper/1410.5626/) (2014)
- [ThermoSim: Deep Learning based Framework for Modeling and Simulation of Thermal-aware Resource Management for Cloud Computing Environments](https://mlsanity.com/paper/2004.08131/) (2020)
- [Game theory and Evolutionary-optimization methods applied to resource allocation problems in emerging computing environments: A survey](https://mlsanity.com/paper/2012.11355/) (2020)

## Abstract (arXiv)

With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods(goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods(energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture,machine-to-machine communication(M2M),Train-to-Train(T2T) communication network, Peer-to-Peer(P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems; Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process.