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Computation Offloading in Multi-Access Edge Computing: A Multi-Task Learning Approach
- Source :
- IEEE Transactions on Mobile Computing. 20:2745-2762
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Multi-access edge computing (MEC) has already shown great potential in enabling mobile devices to bear the computation-intensive applications by offloading some computing jobs to a nearby access point (AP) integrated with a MEC server (MES). However, due to the varying network conditions and limited computational resources of the MES, the offloading decisions taken by a mobile device and the computational resources allocated by the MES can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which may not be optimized with the lowest cost. In this paper, we propose a novel offloading framework for the multi-server MEC network where each AP is equipped with an MES assisting mobile users (MUs) in executing computation-intensive jobs via offloading. Specifically, we formulate the offloading decision problem as a multiclass classification problem and formulate the MES computational resource allocation problem as a regression problem. Then a multi-task learning based feedforward neural network (MTFNN) model is designed and trained to jointly optimize the offloading decision and computational resource allocation. Numerical results show that the proposed MTFNN outperforms the conventional optimization method in terms of inference accuracy and computational complexity.
- Subjects :
- Computational complexity theory
Artificial neural network
Computer Networks and Communications
Computer science
Distributed computing
Mobile computing
020206 networking & telecommunications
02 engineering and technology
Decision problem
Computational resource
0202 electrical engineering, electronic engineering, information engineering
Computation offloading
Feedforward neural network
Electrical and Electronic Engineering
Software
Edge computing
Subjects
Details
- ISSN :
- 21619875 and 15361233
- Volume :
- 20
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Mobile Computing
- Accession number :
- edsair.doi...........7fefdc62c923a4c1cc514efb77f1be8c