Effective multi-controller management and adaptive service deployment strategy in multi-access edge computing environment

With the popularity of emerging mobile services such as virtual reality and online gaming, users expect higher quality immersive experiences. In response to the communication reliability and latency performance problems between controllers and switches due to single link failure in the network, a framework for multi-access edge computing systems based on software-defined networking (SDN) has emerged, and this paper proposes a reliable placement method for controllers based on deployment cost and link failure probability. Reliability and worst -case propagation delay model based on link failure probability is constructed. A controller placement strategy with minimum network cost is solved by a controller layout algorithm based on density-peak clustering. A mobile-aware service adaptive deployment method is also proposed in this paper to address the problems of long user-perceived delays and high migration costs due to the limitations of edge server resources and coverage areas and the dynamic nature of user movement. A mobility model based on the probability density function of sojourn time and a service deployment overhead model based on user mobility are constructed. A quantum ant colony algorithm is used to solve the service adaptive deployment scheme. The proposed algorithm is compared with the benchmark algorithm using the vehicle detection benchmark application service. Experimental results show that the proposed controller placement algorithm weighs the deployment cost, worst-case propagation delay and reliability of controllers and gives a reasonable number of controllers and their locations. The proposed deployment algorithm can optimise the quality of the user experience.

Automated summary

This paper proposes a framework for multi-access edge computing systems based on software-defined networking and a reliable placement method. It also introduces a mobile-aware service adaptive deployment method. Experimental results show that the proposed algorithm can optimize the quality of user experience.