# Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

arXiv: https://arxiv.org/abs/2306.06022 · HTML: https://mlsanity.com/paper/2306.06022/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Mobile Edge Computing: A Survey on Architecture and Computation Offloading](https://mlsanity.com/paper/1702.05309/) (2017)
- [Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing](https://mlsanity.com/paper/1510.00888/) (2015)
- [Compute- and Data-Intensive Networks: The Key to the Metaverse](https://mlsanity.com/paper/2204.02001/) (2022)
- [Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications](https://mlsanity.com/paper/2212.14749/) (2022)
- [A Comprehensive Survey of Potential Game Approaches to Wireless Networks](https://mlsanity.com/paper/1506.07942/) (2015)
- [Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2209.13425/) (2022)
- [Joint Communication, Computation, Caching, and Control in Big Data Multi-access Edge Computing](https://mlsanity.com/paper/1803.11512/) (2018)
- [Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing](https://mlsanity.com/paper/2208.07151/) (2022)
- [Joint Wireless and Edge Computing Resource Management with Dynamic Network Slice Selection](https://mlsanity.com/paper/2001.07964/) (2020)

## Built on by (most cited first, 1 of 1)

- [Digital Twinning of a Pressurized Water Reactor Startup Operation and Partial Computational Offloading in In-network Computing-Assisted Multiaccess Edge Computing](https://mlsanity.com/paper/2407.12011/) (2024)

## Abstract (arXiv)

The computing in the network (COIN) paradigm is a promising solution that leverages unused network resources to perform tasks to meet computation-demanding applications, such as the metaverse. In this vein, we consider the partial computation offloading problem in the metaverse for multiple subtasks in a COIN environment to minimize energy consumption and delay while dynamically adjusting the offloading policy based on the changing computational resource status. The problem is NP-hard, and we transform it into two subproblems: the task-splitting problem (TSP) on the user side and the task-offloading problem (TOP) on the COIN side. We model the TSP as an ordinal potential game and propose a decentralized algorithm to obtain its Nash equilibrium (NE). Then, we model the TOP as a Markov decision process and propose the double deep Q-network (DDQN) to solve for the optimal offloading policy. Unlike the conventional DDQN algorithm, where intelligent agents sample offloading decisions randomly within a certain probability, the COIN agent explores the NE of the TSP and the deep neural network. Finally, the simulation results reveal that the proposed model approach allows the COIN agent to update its policies and make more informed decisions, leading to improved performance over time compared to the traditional baseline