# Joint Computation Offloading, SFC Placement, and Resource Allocation for Multi-Site MEC Systems

arXiv: https://arxiv.org/abs/2003.12671 · HTML: https://mlsanity.com/paper/2003.12671/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2205.09925/) (2022)
- [Koopman based trajectory model and computation offloading for high mobility paradigm in ISAC enabled IoT system](https://mlsanity.com/paper/2406.19871/) (2024)
- [A smart fridge with AI-enabled food computing](https://mlsanity.com/paper/2509.07400/) (2025)

## Abstract (arXiv)

Network function Virtualization (NFV) and Mobile Edge Computing (MEC) are promising 5G technologies to support resource-demanding mobile applications. In NFV, one must process the service function chain (SFC) in which a set of network functions must be executed in a specific order. Moreover, the MEC technology enables computation offloading of service requests from mobile users to remote servers to potentially reduce energy consumption and processing delay for the mobile application. This paper considers the optimization of the computation offloading, resource allocation, and SFC placement in the multi-site MEC system. Our design objective is to minimize the weighted normalized energy consumption and computing cost subject to the maximum tolerable delay constraint. To solve the underlying mixed integer and non-linear optimization problem, we employ the decomposition approach where we iteratively optimize the computation offloading, SFC placement and computing resource allocation to obtain an efficient solution. Numerical results show the impacts of different parameters on the system performance and the superior performance of the proposed algorithm compared to benchmarking algorithms.