학술논문

FAST: Flexible and Low-Latency State Transfer in Mobile Edge Computing
Document Type
Periodical
Source
IEEE Access Access, IEEE. 9:115315-115334 2021
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Containers
Edge computing
Tactile Internet
Bandwidth
Simulation
Search problems
Process control
Application state transfer
multi-access edge computing (MEC)
network function virtualization (NFV)
software-defined networking (SDN)
Language
ISSN
2169-3536
Abstract
Mobile Edge Computing (MEC) brings the benefits of cloud computing, such as computation, networking, and storage resources, close to end users, thus reducing end-to-end latency and enabling various novel use cases, such as vehicle platooning, autonomous driving, and the tactile internet. However, frequent user mobility makes it challenging for the MEC to guarantee the close proximity to the users. To tackle this challenge, the underlying network has to be capable of seamlessly migrating applications between multiple MEC sites. This application migration requires the quick and flexible migration of the application states without service interruption, while minimizing the state transfer cost. In this article, we first study the state transfer optimization problem in the MEC. To solve this problem, we propose a metaheuristic algorithm based on Tabu search. We then propose Flexible and Low-Latency State Transfer in Mobile Edge Computing (FAST), the first programmable state forwarding framework. FAST flexibly and directly forwards states between source instance and destination instance based on Software-Defined Networking (SDN). Both simulation results and practical testbed results demonstrate the favorable performance of the proposed Tabu search algorithm and the FAST framework compared to the state-of-the-art schemes.