Back to Search
Start Over
Joint Resource Allocation and Multi-Part Collaborative Task Offloading in MEC Systems.
- Source :
- IEEE Transactions on Vehicular Technology; Aug2022, Vol. 71 Issue 8, p8877-8890, 14p
- Publication Year :
- 2022
-
Abstract
- This paper investigates the multi-part collaborative task offloading with multiple servers in mobile edge computing (MEC) systems by considering server overload and long-term system performance. Our goal is to achieve the optimal user experience within the range of the network operator’s affordable cost. To this end, we design a two-layer computation offloading framework based on the multi-part offloading mode and the collaborations among small cell base station (SBS) servers. Furthermore, a multi-object constrained optimization problem is formulated, which jointly optimizes user association, channel allocation, and multi-part collaborative task offloading. To solve this problem, we propose a Genetic and Deep Deterministic Policy Gradient (GADDPG)-based computation offloading scheme, where the long-term system performance is considered. Numerical and simulation results demonstrate that the proposed GADDPG-based computation offloading scheme outperforms other methods in effectively solving the server overload and guarantees long-term system performances while ensuring the best user experience. [ABSTRACT FROM AUTHOR]
- Subjects :
- RESOURCE allocation
CONSTRAINED optimization
EDGE computing
MOBILE computing
Subjects
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 71
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 158604180
- Full Text :
- https://doi.org/10.1109/TVT.2022.3174530